Next-Generation-Manufacturing-Technology-Initiative.pdf

Next-GenerationManufacturingTechnologyInitiative

Strategic InvestmentPlan for the

Model-BasedEnterprise

v2.127 May 2005

NEXT-GENERATION MANUFACTURING TECHNOLOGY INITIATIVE

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FOREWORD

This document presents the NGMTI Strategic Investment Plan for the Model-Based Enterprise. Com-ments, questions, and additional input are welcome and highly encouraged. To participate in develop-ment of the NGMTI Strategic Investment Plans, please log into the NGMTI Communities of Practice athttp://www.ngmti.us.

Copyright ©2005, NGMTI


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CONTENTS

1.0 EXECUTIVE SUMMARY .................................................................................................................... 1-1

1.1 THE NEXT-GENERATION MANUFACTURING TECHNOLOGIES INITIATIVE ....................................... 1-11.1.1 The NGMTI Program .................................................................................................................... 1-11.1.2 The NGMTI Assessment of Manufacturing ................................................................................. 1-11.1.3 The NGMTI Strategy ..................................................................................................................... 1-2

1.2 THE NGMTI ROADMAP FOR THE MODEL-BASED ENTERPRISE........................................................ 1-21.2.1 The Model-Based Enterprise – Definitions and Framework ........................................................ 1-41.2.2 The Model-Based Enterprise – A Total Business Approach.......................................................... 1-51.2.3 The Functional Model for MBE .................................................................................................... 1-6

1.3 THE MBE VISION .................................................................................................................................... 1-71.3.1 Vision for Product Realization & Support..................................................................................... 1-81.3.2 Vision for Resource Management................................................................................................ 1-101.3.3 Vision for Strategic Management................................................................................................. 1-12

1.4 ACHIEVING THE GOALS: THE MBE PROJECT PLANS....................................................................... 1-13

2.0 PRODUCT REALIZATION & SUPPORT ........................................................................................ 2-1

2.1 FUNCTIONAL MODEL FOR PRODUCT REALIZATION & SUPPORT............................................... 2-1

2.2 CURRENT STATE OF PRODUCT REALIZATION & SUPPORT......................................................... 2-2

2.2.1 Innovation & Conceptualization ............................................................................................. 2-82.2.2 Product & Process Development ............................................................................................ 2-92.2.3 Manufacturing Execution...................................................................................................... 2-122.2.4 Life-Cycle Support ................................................................................................................ 2-14

2.3 FUTURE STATE VISION & GOALS FOR PRODUCT REALIZATION & SUPPORT ........................ 2-16

2.3.1 Innovation & Conceptualization ........................................................................................... 2-232.3.2 Product & Process Development .......................................................................................... 2-252.3.3 Manufacturing Execution...................................................................................................... 2-302.3.4 Life-Cycle Support ................................................................................................................ 2-36

2.4 ROADMAP FOR PRODUCT REALIZATION & SUPPORT................................................................ 2-41

3.0 ENTERPRISE RESOURCE MANAGEMENT.................................................................................. 3-1

3.1 FUNCTIONAL MODEL FOR ENTERPRISE RESOURCE MANAGEMENT ......................................... 3-1

3.2 CURRENT STATE OF RESOURCE MANAGEMENT .......................................................................... 3-2

3.2.1 Financial Management ............................................................................................................ 3-73.2.2 Operations Management ......................................................................................................... 3-93.2.3 Supply Chain Management ................................................................................................... 3-113.2.4 Marketing, Sales, & Distribution .......................................................................................... 3-123.2.5 Workforce Management........................................................................................................ 3-133.2.6 Capital Asset & Inventory Management .............................................................................. 3-143.2.7 Knowledge/Information Management.................................................................................. 3-163.2.8 Technology Management...................................................................................................... 3-17


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3.3 FUTURE STATE VISION AND GOALS FOR ENTERPRISE RESOURCE MANAGEMENT ............... 3-19

3.3.1 Financial Management .......................................................................................................... 3-213.3.2 Operations Management ....................................................................................................... 3-233.3.3 Supply Chain Management ................................................................................................... 3-263.3.4 Marketing, Sales, & Distribution .......................................................................................... 3-283.3.5 Workforce Management........................................................................................................ 3-323.3.6 Capital Asset & Inventory Management .............................................................................. 3-343.3.7 Knowledge/Information Management.................................................................................. 3-363.3.8 Technology Management...................................................................................................... 3-40

3.4 ROADMAP FOR ENTERPRISE RESOURCE MANAGEMENT........................................................... 3-42

4.0 STRATEGIC MANAGEMENT............................................................................................................ 4-1

4.1 FUNCTIONAL MODEL FOR STRATEGIC MANAGEMENT ............................................................... 4-2

4.2 CURRENT STATE ASSESSMENT FOR STRATEGIC MANAGEMENT ............................................... 4-3

4.2.1 Technology Portfolio Management ........................................................................................ 4-74.2.2 Financial & Capital Assets Management ............................................................................. 4-114.2.3 Knowledge Management & Applications ............................................................................ 4-134.2.4 Strategic Planning & Execution............................................................................................ 4-144.2.5 Strategic Operations Management........................................................................................ 4-16

4.3 FUTURE STATE VISION & GOALS FOR STRATEGIC MANAGEMENT ......................................... 4-21

4.3.1 Technology Portfolio Management ...................................................................................... 4-234.3.2 Financial & Capital Assets Management ............................................................................. 4-264.3.3 Knowledge Management & Applications ............................................................................ 4-284.3.4 Strategic Planning & Execution............................................................................................ 4-304.3.5 Strategic Operations Management........................................................................................ 4-31

5.0 MBE PROJECT PLANS........................................................................................................................ 5-1

MBE 13 – Information Delivery to Point of Use ..................................................................................... 5-2MBE 7 – Product-Driven Product & Process Design ............................................................................ 5-10MBE 1 – Flexible Representation of Complex Models ......................................................................... 5-19MBE 5 – Intelligent Models.................................................................................................................... 5-28MBE 6 – Configuration Management for the Model-Based Enterprise................................................ 5-35MBE 3 – System-of-Systems Modeling for the Model-Based Enterprise............................................. 5-43MBE 4 – Enterprise-Wide Cost Modeling ............................................................................................. 5-50MBE 10 – Model-Based Distribution ..................................................................................................... 5-58MBE 11 – Multi-Enterprise Collaboration ............................................................................................. 5-66MBE 8 – Model-Based Product Life Cycle Management ..................................................................... 5-71MBE 9 – Model-Based, Real-Time Factory Operations........................................................................ 5-79MBE 2 – Shared Model Libraries ........................................................................................................... 5-89MBE 12 – Model-Based Resource Management ................................................................................... 5-94

APPENDIX: MBE PROJECT PLAN ............................................................................................................. A-1


Executive Summary 1-1

1.0 EXECUTIVE SUMMARY

1.1 THE NEXT-GENERATION MANUFACTURING TECHNOLOGIES INITIATIVE

1.1.1 THE NGMTI PROGRAM

The Next Generation Manufacturing Technologies Initiative (NGMTI) is a government/industry partner-ship to accelerate the development of breakthrough manufacturing technologies that strengthen the de-fense industrial base and improve the global economic competitiveness of U.S. manufacturers. NGMTIwill achieve this mission by creating strategic investment plans for innovative manufacturing technolo-gies, and by driving the implementation of those tech-nologies through focused pilots and partnerships.

NGMTI is unlike any program before it. It is deliveringdefinitive plans for manufacturing success, and building acompelling national consensus for implementation.NGMTI takes a new approach to seeking increasedfunding for manufacturing technology. It uses a systematic methodology for selecting the right technolo-gies, and defines a clear pathway to dramatic return on investment for focused technology deployment.The rich information and business case for investment provide the foundation for securing needed fund-ing. NGMTI seeks to create new synergies and leverage opportunities to make better use of the fundingpresently available, and to seek additional funding for opportunities with high potential value to the na-tion. The requests for new funding will be supported by a business case and by rich plans which, whenimplemented, will deliver the best solutions to the problems that challenge our nation’s manufacturinginfrastructure and economic strength.

1.1.2 The NGMTI Assessment of Manufacturing

NGMTI is vital to the nation’s defense and economic health. U.S. manufacturers face deepening chal-lenges: a widening trade imbalance exceeding $600 billion in 2004, growing competition from low-wagecountries, a decline in long-term technology investment, and a sharp increase in the cost of doing businessin the U.S. According to a study by the Manufacturers Alliance/MAPI of the nine largest U.S. tradingpartners, the overhead cost of manufacturing in the U.S is 22% higher than the average of these partners.1

Such factors have caused the loss of some 3 million U.S. manufacturing jobs and a continued decline inmanufacturing as a percentage of gross domestic product (GDP) – from about 30% of the GDP in the1950s to about 15% today. While job count may decline in the short term, the wealth generation frommanufacturing and the power of technology-driven innovation must not be lost. Indeed, it should be ac-celerated as a core element of our national economic strategy. There is a great opportunity to redefine thecompetitive base through application of new and emerging technologies and enabling tools. That is theformula for success supported by NGMTI. The next few paragraphs support that formula.

Technology Drives Innovation. Innovation in product development and process technologies will en-able faster generation of new product ideas, rapid and efficient maturation of those ideas to designs, andacceleration of innovative products to the marketplace. In this way, the U.S. can be preeminent throughmore effective product development and early market penetration. Advanced manufacturing processescomplete the innovation equation. Process excellence drives productivity improvement, which reducesproduction costs and neutralizes the competitive impact of labor rates. Through process excellence, wecan achieve an environment that drives the labor content down to a point of insignificance – making high-value manufacturing geographically immune from competition from low-cost labor sources. In the next- 1 Jeremy A. Leonard, “How Structural Costs Imposed on U.S. Manufacturers Harm Workers and Threaten Competitiveness,”

Manufacturers Alliance/MAPI, 2003.



generation manufacturing world, siting of production centers will be based on access to markets and mar-ket positioning, and will not be a necessity for low-cost production.

Innovation Drives Wealth Creation. Increasing demand for innovative products creates market pullthat will be satisfied by successful manufacturing firms. Wealth generation does not come from the smallmargins of commodity production, and the U.S. probably cannot (and should not) compete for low-valueproducts with high labor content. However, innovative processes and efficient operations can greatly re-duce the labor component and slash the cost of producing low-value products2. New high-value productscreate a tremendous market pull that only innovative, fast-to-market, technology-rich companies can sat-isfy. These two strategies – low-cost manufacturing through process innovation and fast to market withinnovative products – should dominate our national thinking and define the next generation of U.S. manu-facturing.

Wealth Creates Jobs. Next-generation manufacturing technologies create wealth, and wealth createsjobs. The job content will change, with less emphasis on manual skills and touch labor and far greaterneed for computer programmers, planners and coordinators, and designers. There will be more jobs indistribution, sales, marketing and product support functions because of the wealth created through inno-vation-driven manufacturing.

Technology is the Engine of Economic Growth. The manufacturing infrastructure and technology tool-set that we use today will not support us in the future global economic arena. No single company or or-ganization can supply that next-generation toolset. It requires a commitment to and support from bothindustry and government to 1) define what needs to be done; 2) establish the priorities, and 3) createstrong synergy to deliver the needed solutions. That is what NGMTI is doing. NGMTI is a critical com-ponent of a national manufacturing success strategy.

1.1.3 THE NGMTI STRATEGY

NGMTI is a dual-purpose program. It is sponsored by the Department of Defense (DoD), and one of themajor tenets of NGMTI is the delivery of breakthrough emerging technologies to support DoD’s manu-facturing needs. Specifically, we seek opportunities to move ideas that have direct application in meetingthe needs of the warfighter – from development to deployment and operational support. NGMTI alsoseeks crosscutting technologies that are good for all manufacturing enterprises. This unique partnership isworking with 175 manufacturing community leaders representing more than 75 different organizations,and the number is growing rapidly.

The NGMTI program is managed by the Advanced Technology Institute (ATI) in partnership with theIntegrated Manufacturing Technology Initiative (IMTI) and the National Council for Advanced Manu-facturing (NACFAM). National manufacturing leaders provide steerage and advocacy for NGMTIthrough an Industry/Government Forum.

NGMTI addresses the common requirements of DoD and U.S. industry through a three-part strategy:

1. Developing a Strategic Investment Plan for Manufacturing. NGMTI is working with hundreds ofrepresentatives of the manufacturing community to define compelling needs, map current R&D in-vestments against those needs, identify critical voids, and develop a comprehensive national planfor focused manufacturing technology investment.

2. National Implementation of the Strategic Investment Plan. As an integral part of the Strategic In-vestment Plan development, NGMTI is building the Industry/Government Forum as a leadership-level coalition of the industry, government, and research communities to work together to executethe plan. The Forum convenes semi-annually to review requirements and strategies.

2 As an example, American Safety Razor Company – which makes 1 billion razors per year – recently brought most of its off-

shore operations back to the U.S. because technology enabled productivity gains and cost reductions that offset the advantageof offshore labor rates.



3. Transitioning Manufacturing Technology. NGMTI is implementing strategies and processes toaccelerate the maturation and implementation of new technologies in alignment with the StrategicInvestment Plan. These strategies include proof-of-concept experiments and a National Manufac-turing Technology Testbed Network for facilitating and widespread deployment. The TestbedNetwork, integrating the resources of leading government and industry manufacturing laboratories,will provide a national capability to advance manufacturing technology.

As shown in Figure 1.1-1, NGMTI uses a combination of research and input from subject matter experts(SMEs), plus input from industry participants via internet-based Communities of Practice, to create Tech-nology Roadmaps for each NGMTI Thrust Area. This serves as input to a structured evaluation processwherein high-priority topics are selected for more in-depth treatment and as candidates for focused R&Dprojects through the NGMTI Testbed Network or other mechanisms.

Figure 1.1-1. NGMTI follows a structured process to define and prioritize needs, develop plansto meet those needs, and implement the required research and development.

The program is built around a series of six Thrust Areas that provide a focused structure for managingtechnology requirements that cut across the nation’s defense and commercial manufacturing base. TheseThrust Areas are:

1. Model-Based Enterprise2. Emerging Process Technologies3. Intelligent Systems4. Enterprise Integration5. Knowledge Applications6. Safe, Secure, Reliable & Sustainable Manufacturing Operations.

These topics were selected based on input from industry and government focus groups to define the right“umbrellas” under which to capture the high-priority technology needs of the nation’s manufacturingcommunity.

1.2 THE NGMTI ROADMAP FOR THE MODEL-BASED ENTERPRISE

This document provides an overview of the NGMTI Roadmap for the Model-Based Enterprise, the firstof the six Thrust Areas addressed by the NGMTI program. The roadmap is one element of a broaderstrategic investment plan that includes a series of white papers outlining recommended research, devel-opment, and demonstration projects for near-term implementation by government and industry. A brief



synopsis of the white papers is provided later in this section; full copies of these and other NGMTI docu-ments are available in the NGMTI Communities of Practice at http://www/ngmti.us.

1.2.1 THE MODEL-BASED ENTERPRISE – DEFINITIONS AND FRAMEWORK

“Model-based enterprise” is a relatively new term for a collection of concepts that have matured over thelast decade and coalesced under a new name. The vision of a totally “digitally-driven” design, produc-tion, and product support environment became an important driver of manufacturing enterprise strategiesin the 1990s as extensions of concurrent engineering, integrated product and process development, andother emerging disciplines. “Integrated product realization” emerged as an all-encompassing concept thatwent beyond basic integration of product and process activities to call for a new toolset supporting a to-tally digital product life-cycle management system. The term “model-based enterprise” has become theembodiment of this progressive approach.

What is a “model-based” manufacturing enterprise? Simply stated, it is a manufacturing entity that ap-plies modeling and simulation (M&S) technologies to radically improve, seamlessly integrate, and strate-gically manage all of its technical and business processes related to design, manufacturing, and productsupport. By using product and process models to define, execute, control, and manage all enterpriseprocesses, and by applying science-based simulation and analysis tools to make the best decisions atevery step of the product life-cycle, it is possible to radically reduce the time and cost of product innova-tion, development, manufacture, and support.

Before examining the model-based enterprise in depth, it is important to acknowledge that models are atthe core of the concept. The term “model” can be defined in many ways; however, what is important isthe functions that a model performs in the manufacturing enterprise environment. Within that context,the following definitions are appropriate.3

• A model is a representation of a product: The most common reaction to the question of “what is amodel?” related to design and manufacturing is that it is a digital description of a product. Statedmore completely, a product model is an electronic representation of all attributes of a product thatenable its manufacture, use, and support. An effective product model contains all elements needed todefine a product and can provide detailed information about that product. Further, it provides infor-mation that is useful in applying the product as a piece of a whole, as in components, subassemblies,and assemblies.

• A model is a representation of interactions and results – In manufacturing processes, a modelmimics or “mocks up” a process including the interrelationships of entities and parameters. Hence,the model is able to determine the results of interactions based on changes in parameters of an entityor a process variable. In more scientific language, a process model is a mathematical description of acomplex phenomenon or object useful in defining how products, processes, or systems respond tovarious inputs.

• A model is an enabler: A model can enable many things that are not otherwise possible. A productmodel can provide the information that enables downstream processes such as design of tooling, fab-rication of fixtures and molds, manufacturing of products and assemblies, and inspection operations.It enables the exploration of options and quantification of expected results for each option. This ca-pability is often referred to as virtual prototyping. Models enable evaluation of all parameters andtheir impacts on performance, costs, and other important attributes of a product or a process.

• A model is an integrator – Modeling a single process may not be difficult, but may not deliver greatvalue of itself. However, the ability to assemble collections of related models into metamodels thatcan define the results of complex interactions across products and processes – without losing any of

3 A recent survey conducted by the Model-Based working group of the National Nuclear Security Administration provides the

source material from which this discussion of model functions is derived.



the constituent values – can add tremendous value. The ability to integrate complex models offersthe possibility of implementing radically new business processes and reengineering corporate cul-tures, based on an unprecedented ability to accurately predict the results of options for change.Process models, integrated across an enterprise, enable enterprise-wide process management. Costmodels, when fully populated across a full range of product and process functions, can enable costestimating, tracking, and management to a level not achievable otherwise. Process and factory mod-els that document the full range of capabilities, can be configured in enterprise resource models toenable optimization of capacity and utilization. The list could continue, but the point is made thatmodels, taken alone, have interesting value, but, when applied in integrated systems, they deliverbreakthrough success.

1.2.2 THE MODEL-BASED ENTERPRISE – A TOTAL BUSINESS APPROACH

The model-based enterprise, as the name implies, utilizes models to drive and enable every function of theenterprise. While many leading manufacturers make extensive using of modeling and simulation tools intheir engineering and business processes, the model-based enterprise concept represents a sweepingchange in the technological foundation, the business processes, and the culture of the enterprise as dis-cussed below.

The Technological Foundation for the Model-Based Enterprise (MBE): While there is a tendency tointerpret the MBE concept as simply “all-digital processes,” it should be understood that enterprise func-tions are modeled only to the level that it makes business sense to do so. The technical environment isdata-, information-, and knowledge-rich, and provides analytical tools that understand the interactions anddependencies of the enterprise’s systems and tools. This empowers a new level of technical understand-ing of products, processes, and resources – supporting radically improved decision making across the en-terprise.

Model-Based Business Practices: It does little good to have model-based tools if the company’s busi-ness processes and systems do not support their utilization. In a model-based enterprise, business func-tions are engineered to pull needed information from product and process models and linked knowledgesources, and apply that information together with business models. As an example, a model-based sched-uling system would understand what needs to be accomplished to start a production process and wouldmodel the production activity. Product engineering, cost management, resource allocation, and other en-terprise systems would interact with that model based on their own models and data in order to optimizeplan for the best balance of results. In this manner, all business processes are integrated across the enter-prise, using models to share and act on requirements, knowledge, and resource information.

The ability to model all processes of the enterprise also provides unprecedented flexibility to accommo-date change. Managers at all levels can use MBE tools to quickly explore different scenarios fed by acontinuous stream of external and internal information. This enables companies to quickly adopt im-proved methods and tools, supporting continuously efficient operation for delivery of total value to enter-prise stakeholders.

The Culture of the Model-Based Enterprise: A model-based enterprise dictates a mindset of virtualexperience in concert with physical experience. Productivity and quality metrics are key to MBE success,and embracing a culture of continuous optimization is a prerequisite. In a model-based culture, simula-tion and modeling systems replace much physical prototyping with the capability to deliver the first prod-uct correct every time. Validated product models drive validated processes to produce parts that fit andoperate exactly as designed. The certification of product lies in the certification of the processes used tomake it, with testing only done to satisfy customer-imposed (e.g., regulatory compliance) requirements.The culture of the model-based enterprise adapts quickly to incorporate new technologies, by providingthe ability to thoroughly simulate the effects of a contemplated upgrade, replacement, or “refresh” of aproduct, process, or system.



1.2.3 THE FUNCTIONAL MODEL FOR MBE

The MBE initiative addresses manufacturing enterprise requirements across three broadly defined func-tional areas:

• Product Realization & Support –all activities required to conceive, develop, produce, and supportthe enterprise’s products, including appropriate disposition of the product at the end of its useful life.Product realization is the core of the manufacturing enterprise mission – the design, fabrication, andsupport of products to generate revenue and fulfill the needs of the enterprise’s customers and otherstakeholders.

• Resource Management –all activities associated with the business of the enterprise, including con-trol and oversight of production and support operations, supply chains, and sales and distributionmechanisms; and human and financial resources, knowledge and technology resources, and other as-sets.

• Enterprise Management – all activities required to enable a company’s leadership team to guidethe enterprise based on current, complete, accurate information. Strategic management is discrimi-nated from resource management in that strategic management is not specifically concerned with theenterprise at an operational level, although the two functions are closely interrelated.

As shown in Figure 1.2-1, each of these topics is broken down into a set of logical functional elementsand addressed in detail in the MBE Roadmap. The Roadmap provides 1) an assessment of the currentstate of practice; 2) a vision of the future state of capability enabled by model-based tools and processes;3) goals and requirements that must be met to achieve the vision; and 4) a notional timeline for conduct-ing the required research, development, and implementation.

Figure 1.2-1. Functional Model for the MBE Roadmap.



1.3 THE MBE VISION

An integrated, all-digital system will support all functions of the enterprise. From the innovation proc-ess to delivery and support of product, a rich set of interconnected models that communicate in realtime will augment human creativity and automate the operational component. The models that defineproducts and processes will be so accurate that they will serve as the controller for process execution.All business and technical functions will be integrated using model-based systems for optimized per-formance and real-time, responsive control of the enterprise.

In the future manufacturing enterprise, knowledge will be captured and applied through model-basedsystems and processes. Material properties, costs, and other factors will be readily available to aid de-signers in engineering products and processes for total performance. Processes will be characterized us-ing robust and knowledge-rich models to provide absolute assurance of performance. Product featuresand characteristics will be modeled to assure product performance throughout the life cycle. The enter-prise will be supported by analytical tools that include the capability to address functions and issuesthroughout the supply chain. Knowledge available to the modeling environment will not be limited totechnical data, but also support legal, financial, regulatory, and other critical business functions.

Analytical tools will evaluate options in real time and to the needed level of accuracy for the modeledfunction. Total value will always be the objective. In the conceptualization phase of product develop-ment, rapid concurrent evaluation of many alternatives may be more important than high-fidelity evalua-tion. In detailed design, exact performance prediction from high-fidelity simulations is essential. Everyparameter and every operation will be evaluated in light of the impact of individual decisions on thewhole of the product or the function of the enterprise.

Sequential processes such as manufacturing will take on a new content as the “product script” is devel-oped. From the first gleam of an idea, to conceptual design, to detailed design and on through the proc-ess, knowledge will be continuously added to a living, increasingly robust life-cycle model. The processmodels will be accurate and capable of responding to off-normal conditions to the point that model-basedcontrol will be standard practice in even the most demanding industries. Actual performance will becompared to the processing model of the script, and any deviation from requirements will result in animmediate corrective response.

Business and technical functions will be integrated in a model-rich environment. Enterprise-wide re-sources will be managed in real time to assure that the best decisions are made. Enterprise resource man-agement systems will consist of an open framework supported by an accurate and dynamic set of modelsenabling real-time awareness of all enterprise functions and support for all business and technical deci-sions.

The model-based enterprise is not only about making product and managing resources, but extends tostrategic decision processes as well. Managers, strategists, and all stakeholders will have the capabilityto evaluate strategic options based on knowledge gained from past experience; data from present opera-tions; and trends, forecasts, and predictions for the future. The executives of the future will be supportedby a rich set of knowledge-based advisors, visualization tools, and other capabilities that will enable themto evaluate options and make the best possible decisions.

The Perspectives of MBE – A key precept of the model-based enterprise is to facilitate unity of all en-terprise functions, ensuring that different functions and organizational entities work together as a seamlessunit and that every function or person has immediate access to any information that they need. This is notan issue of “integration,” but rather requires that each of the unique perspectives of the major functions ofthe enterprise be directly and clearly supported. To use a simple analogy, the window to the model-basedenterprise systems is like a kaleidoscope. No matter who looks into the kaleidoscope, they see exactly theview they want – often radically different views, but all drawn from the exact same repository of infor-mation and knowledge.



The perspectives addressed by the MBE Roadmap are introduced using the three “elements” of the MBEfunctional model shown previously: Product Realization and Support, Enterprise Resource Management,and Strategic Enterprise Management.

The unifying force for all perspectives is the integration of the enterprise. All systems collect and sharedata with all other systems – without any need to manually translate or reenter data. All individual mod-els and model-based systems interoperate with all others with which they interact. It is also important tonote that integration removes the walls between design, analysis, and execution. The same data that isused in analytical tools to evaluate options also supports the design function, and the design data feedsseamlessly into the execution systems. The data for making products also supports the management ofresources. In this way, a model-based enterprise is a machine that, simply put, turns ideas into money.

The following sections provide an overview of each of the three elements, or perspectives, of the MBEconcept.

1.3.1 Vision for Product Realization & Support

Seamlessly integrated M&S tools will enable distributed teams to quickly create product and processdesigns that achieve the best balance of performance, cost, robustness, and other factors. The productmodel and underlying knowledge base will control all processes across the product life cycle, capturingand sharing data to drive continuous improvement throughout the enterprise. Manufacturing proc-esses will be designed and qualified entirely in the virtual realm, drawing on scientifically accuratemodels of materials, unit processes, and equipment. The resulting model-based knowledge base willsupport all aspects of maintenance, training, and life-cycle support.

The ultimate vision for product realization in the model-based enterprise is the ability to seamlessly moveback and forth between the virtual representation of the product and its processes, and the physical realityof the processes as they occur in real time. The product model will monitor and guide the productionprocess and support analysis and decision processes to address changing requirements and deal with off-normal conditions and other problems.

This tightly coupled virtual/real representation of the product and processes will be visible to all valuechain members as they perform their functions, showing the status of the manufacturing process and theirposition in that process. This vision of a seamless value chain cannot be realized without the unifyingbase of the comprehensiveproduct model.

The model-based productrealization environmentwill consistently deliverbest designs to satisfy abalanced set of objectivesfor the enterprise and itsstakeholders. Rich andmathematically accuratevisualization environ-ments, augmented bypowerful analytical tools,will allow users to inter-actively refine objectivesand preferences in per-forming trade-offs for op-timization (Figure 1.3.1-1). As each preference is

Figure 1.3.1-1. Analytical applications will be integrated on the desktopinterface that enables users to take full advantage of capabilities resident

anywhere in the supply chain.



specified, the user will see its impact on performance, cost, delivery time, aesthetics, and other attributesof interest.

The resulting product model will not be merely a product representation coupled to a database of physicalattributes. Rather, it will be a total product definition that is continuously linked to all sources of techni-cal and business information that define and affect it throughout the life cycle. This knowledge base willnot only enable the enterprise to radically improve its ability to design, produce, and support its products,but will provide a total audit trail of actions taken and supporting rationale. This will significantly im-prove the ability of the enterprise to address and favorably resolve potential liability issues throughout aproduct’s life.

Future manufacturing process designers will draw on a comprehensive library of validated, thoroughlycharacterized models and simulations of common materials, unit processes, and manufacturing equipmentto integrate optimal process designs for individual products. Resources (internal resources or those avail-able from supply chain partners) will be modeled in terms of their characteristics and availability for anyenterprise project. Interoperable, scaleable models that understand – and actively search for – the infor-mation they need for completion will be standard tools for product and process engineering and manu-facturing execution. They will autonomously determine and search for the information they need to sat-isfy the requirements of the specification and production plan, using intelligent digital advisors to guidehuman users in making the best decisions at every step.

Equipment and tooling manufacturers and material commodity vendors will provide validated 3D models,performance simulations, and supporting data as a standard part of their equipment and products, withstandards ensuring the ability of different models to integrate in plug-and-play fashion. This will enableprocess designers across a supply chain to quickly create accurate virtual production lines, filling in gapsonly as needed for product-specific tooling and proprietary processes. Virtual test environments will en-able product and process designers to subject their designs to “test to destruction” rigor without makingphysical prototypes.

The manufacturing execution team will use process simulations coupled with certified material, equip-ment, and process models to optimize the manufacturing strategy, testing and “producing” product in thevirtual realm to verify readi-ness for production (Figure1.3.1-2). These same modelswill control the productmanufacturing process, withlow-cost sensors and intelli-gent monitoring systems con-tinuously comparing perform-ance against the process mod-els to keep the systems run-ning in continuous confor-mance with requirements andspecifications.

Figure 1.3.1-2. Manufacturing process parameters and control informationwill be downloaded directly from the product/process model to drive and

control all manufacturing processes.



1.3.2 VISION FOR RESOURCE MANAGEMENT

Future manufacturing enterprises will continuously optimize internal and external resources to maxi-mize value to all stakeholders. Fully integrating model-based design, manufacturing, and productsupport with all business functions will make appropriate knowledge available to decision-makers andenable them to tune enterprise performance for total customer and stakeholder satisfaction.

The processes and operations of future enterprises will be powered by model-based business systems thatprovide continuous, precise visibility of capital asset and inventory requirements for manufacturers of allsizes. Intelligent resource management models linked to enterprise resource management (ERM) systemswill monitor the enterprise’s marketing, sales, and distribution systems and external information sourcesto accurately predict near-term and long-term variations in product demand by region and locality, auto-matically recalculating requirements and redirecting resources at the enterprise level and at operatingsites. The system will enable product managers and operations managers to understand, with a high de-gree of confidence, what requirements are coming the next day, week, month, and year. It will enablethem to quickly evaluate the pros, cons, and deeper implications of all options for responding to thoserequirements. More importantly, the system will enable them to re-plan quickly as requirements changeand as new opportunities and challenges arise.

The core elements of the enterprise (including its business rules and strategies as well as its processes andsystems) will be modeled so accurately and thoroughly that routine allocation of resources will be han-dled autonomously by ERM systems. These systems will have total connectivity to all enterprise proc-esses and assets – including product/process capabilities, manpower and skills, facilities and equipment,raw material and product inventories, supply chain capabilities, and working capital and budgets (Figure1.3.2-1).

This seamless connectivity will extend to every tier of the enterprise’s supply chains. An open businesssystems architecture based on well-defined standards for modeling and managing different types of re-sources will enable different companies to quickly “plug together” to exploit new opportunities. Whileallocation of resources will always be at the discretion of the enterprise’s managers, the ability to access

Figure 1.3.2-1. Future ERM systems will provide total connectivity of all enterprise processes to all enterpriseresources, with powerful modeling and simulation capabilities that enable fast, accurate decisions.



current resource information anywhere in the supply chain – with appropriate security – will eliminatemuch of the inefficiency inherent to managing complex supply chain relationships.

Science-based models of the inputs, outputs, demand factors, and dependencies of every enterprise proc-ess, coupled with continuous access to all sources of information that affect these processes, will provideclear definition of what resources need to be where and when, and when they will be available again forreallocation. These models will control the systems that execute the enterprise’s technical and businessprocesses. Managers at all levels will interact with the system to develop plans, monitor performance,analyze issues, evaluate opportunities, and efficiently direct resources to the point of need.

Model-based operations management systems will enable supervisors and managers to quickly detect per-formance issues and direct the right resources to correct problems and optimize performance (Figure1.3.2-2). This will rapid evaluation of different options for improving performance, such as rearrangingshifts and workflows, adding or changing out equipment, and adjusting work-in-process levels. They willbe able to “plug in” different resource options into the operations simulation and run multiple simultane-ous scenarios to determine the best cost/performance solution. The system will automatically generateimplementation plans that schedule the tasks to be done, including procurement, installation, checkout,worker training, and revision of workflows and maintenance plans.

The greatest benefits of model-based resource management will come from a radically improved ability toprepare for new requirements and respond to problems throughout the supply chain. Future product andprocess models will provide precise definitions of the resources they require for their execution – includ-ing raw materials, parts, and components; manufacturing labor and skills; facility space, equipment, tool-ing, and fixtures; handling and transport; and product support, including training and documentation.These requirements will be “uptaken” by the ERM system and fed to functional planning systems for im-plementation. Managers will use desktop modeling and simulation tools, connected to the enterprise’sknowledge bases, to evaluate options for meeting the requirements with those resources in ways that offerthe best balance of performance, speed, cost, risk, and profitability. These tools will also enable manag-ers to plan for new requirements and priorities of the business environment in areas such as safety andenvironmental compliance.

The same tools will enable managers to rapidly determine the best response when requirements change asa result of design changes or due to performance or schedule problems anywhere in the supply chain.Intelligent advisors will rapidly recalculate the impacts of an actual or planned change in resources on allother dependent resources, and provide recommendations for corrective action to get the product, process,project, program, or operation back on track.

Figure 1.3.2-2. Model-based operations management will enable precise control of all factory systems,interfacing with equipment-level automation to continuously tune performance.



1.3.3 VISION FOR STRATEGIC MANAGEMENT

A master enterprise model that is unique to the enterprise will guide the strategic management team incontinuously positioning the company for success in the rapidly changing global marketplace. Mod-eling systems accessed through a “strategic management cockpit” will support scenario-based evalua-tion of options for all strategic management functions. Model-based intelligent advisors will guide theorganization to the best choices and best decisions for achieving corporate objectives and responding tochallenges and opportunities.

In the NGMTI vision for the model-based enterprise, top-down and bottom-up management processesacross all functional elements of the enterprise are integrated in a “unity model” as illustrated in Figure1.3.3-1. The unity model provides a framework whereby strategic planning and direction processes areintegrated into work processes at every level of the organization. Information flows easily and accuratelyin all directions, to exactly the right levelneeded to support processes and operationsfor every function.

The unity model aligns the enterprise mis-sion and goals down to the lowest level ofeach area of the company. It places focusand value where it belongs, on the enterpriseas a whole, not on any single entity orgroup. With all corporate elements in unity,the inherent disconnects between levels andunits of the organization are eliminated.Each corporate officer occupies an equidis-tant management position. This facilitates aunity of purpose, roles, and responsibilitiesthroughout every organizational element,regardless of their specific role.

This unified process dissolves the notionthat strategic responsibility is only signifi-cant to senior executives. Instead, it be-comes the mission of every managementlevel, and flows all the way to the plantfloor. More importantly, it provides aframework for developing and implementing model-based tools that every manufacturing company, re-gardless of size, sector, or organizational design, can apply to unify and coordinate its strategic manage-ment processes.

In the manufacturing companies of the future, enterprise processes will be integrated and guided by amaster enterprise model. This is not an organizational architecture, but rather a high-level process model– unique to every company – that contains or links to all the constituent models that define and guide thecompany’s business and technical processes. The master enterprise model contains (or provides real-timeaccess to) comprehensive, accurate, and timely information on the internal workings of the enterprise andits supply chain, plus the external information and events that may affect the enterprise.

As the strategic management team executes its analysis and planning processes, the master model deliversthe information needed to make the best decisions for the enterprise. It ensures that the implications ofeach decision are reflected in all affected business units, organizational elements, and processes, and pro-vides feedback from these elements in order to optimize strategies for best results. The master model thus

Figure 1.3.3-1. The unity model provides a frameworkfor a model-based environment to realize the future vision

for strategic enterprise management.



enables accurate analysis of the current situation and scenarios for the future, helping the managementteam position the enterprise to maneuver and succeed in the rapidly changing global marketplace.

An open systems framework, flexible information representation schemes, and highly automated moni-toring and surveillance functions will tightly couple strategic management processes to operational reali-ties. Digesting daily a huge and complex quantity of data about internal operations and external condi-tions and events, the master model will empower the operation of the desktop management cockpit thatprovides each strategic management function with a continuously updated overview of pertinent events,trends, and opportunities for improvement. A continuous scan of external information sources (news me-dia, patent applications, changes in regulations, R&D monitoring services, etc.) will feed into a continu-ously updated “threats and opportunities” analysis for the enterprise. Intelligent advisory systems willintegrate this information with internal information to identify opportunity and challenge scenarios forconsideration by the strategic management team. The model-based strategic management environmentwill be a dynamic one that learns from both inside and outside the organization on a 24/7 basis, integrat-ing the top floor with the shop floor (and everywhere in between) and supporting the best decisions atevery level.

In the model-based enterprise, the information required for all business functions is available when it isneeded, where it is needed, and in the form in which it is most useful. The strategic management cockpitwill place anyone who needs the services in contact with the information they need and the tools to con-vert it to actionable knowledge. The cockpit is the point of interface between the user and the master en-terprise model, and it is supported by analytical tools, intelligent advisors, and connectivity to all internaland external knowledge bases available to the enterprise. The user can present a scenario to the cockpit,and quickly receive an analysis based on the best information available. The basis for recommendationswill be visible, enabling analysis of every decision and supporting continuous learning for the cockpit’sintelligent systems.

The strategic management cockpit will give every member of the team the information they need to setobjectives, monitor performance, and respond to opportunities and challenges. Occurrences will be ana-lyzed for their strategic value, and proactive changes in corporate direction will be recommended whenappropriate. The system architecture will be open, interoperable, and modular, enabling companies toquickly tailor generic modules to support the specific needs of the enterprise.

1.4 ACHIEVING THE GOALS: THE MBE PROJECT PLANS

The MBE Roadmap outlines more than 60 goals and 200 supporting requirements for development andimplementation of model-based capabilities. The scope of work required to achieve these capabilitiesrepresents a huge undertaking with a high level of technical risk. In order to decompose this scope intomanageable segments, the MBE team conducted a structured prioritization of the goals to define compel-ling needs that can be addressed in the near term to begin delivering vital capabilities to U.S. manufactur-ers. This approach also provides a means to demonstrate the value and power of the MBE vision – help-ing build support and momentum to address the longer-term, higher-risk goals.

The prioritization process identified 13 topics for expansion into project plans. In most cases these topicsrepresent a rollup of several key goals that are closely related; in other cases, a single compelling goalwas expanded into greater detail.

Table 1.4-1 identifies the selected project topics and provides a brief synopsis of project objectives andestimated resource requirements, which total approximately $132 million over 7 years. These estimatesare rough-order-of-magnitude only, intended to provide a starting point for detailed planning by imple-mentation teams.



Table 1.4-1. The MBE Project Slate.

Project Scope and Key Objectives Scope

1. FlexibleRepresentationof ComplexModels

Develop the capability to create a product model that is rich enough to support all devel-opment, production, support, and end-of-life disposition activities throughout the productlife-cycle. The resulting product model will have the flexibility and power to quickly providethe exact “view” to support desired functions. The model of the product (and its associatedmanufacturing and support processes) will integrate all needed information, either withinthe model or by linking to data within the enterprise or from external sources.

84 mo,$33M

2. Shared ModelLibraries

Establish a common, robust framework for managing repositories of collaborative modelsthat, when assembled, can accurately simulate materials, products, and enterprise func-tions across different industry sectors. Establish an initial library of such models to validatethe technical feasibility and business value of the shared model library concept.

39 mo.,$9.1M

3. System-of-SystemsModeling

Develop and demonstrate capabilities, approaches, and tools for multi-level, multi-systemmodeling of products, processes, and life-cycle functions for a representative set of prod-ucts in a selected manufacturing industry sector. Demonstrate the ability of system-of-systems modeling techniques to reduce product development time and cost, eliminatecurrent needs for manual integration among and across enterprise processes, and deliverproduct/process designs that are optimized for performance across the product life cycle.

32 mo.,$4.4M

4. Enterprise-WideCost Modeling

Develop the capability to establish and manage comprehensive, highly precise total prod-uct cost models that reflect not only traditional materials and direct production costs, butalso design and other investment and indirect factors. These cost elements will not bestatic inputs; rather, the models will link to the “live” sources of cost data, down to the low-est level of the supply chain.

30 mo.,$8.4M

5. IntelligentModels

Develop enabling technologies and demonstrate the use of intelligent models that under-stand, seek out, acquire, and act on the information they need to execute their functions.Establish linkages between the physical modeling realm and the logical models that pro-vide intelligence to product, process, and enterprise models.

36 mo.,$5.6M

6. ConfigurationManagement forthe Model-BasedEnterprise

Develop an integrated system that ensures association and traceability of the right infor-mation with any product or process throughout its life cycle. Develop requirements and anintegration strategy for managing complex interdependent configuration entities within themanufacturing enterprise, to the lowest level of its supply chains, and across the full life-cycle of the products it manufactures.

60 mo,$23M

7. Product-DrivenProduct &Process Design

Develop and pilot M&S capabilities that enable a product model to automatically drivedownstream manufacturing and support applications. Demonstrate collaborative interac-tion between product and process models to evaluate the current state of capability andprovide business-case data regarding the impacts of decisions made at each step of prod-uct design and manufacturing.

38 mo.,$7M

8. Model-BasedLife-CycleManagement

Provide the capability to create and apply scaleable, high-fidelity product life-cycle modelsthat support every phase of the product lifespan and through all tiers of the supply chain.

66 mo.,$13.5M

9. Model-Based,Real-TimeFactoryOperations

Develop enabling technologies and demonstrate real-time, model-based control of factoryoperations, including production and maintenance operations as well as active interfaceswith asset, inventory, and facility management systems. Provide the models that establishthe necessary operations control functions, and integrate these models with material,product, process, and control models to deliver a prototype system.

36 mo.,$4.0M

10. Model-BasedDistribution

Develop enabling technologies and conduct proof-of-principle demonstrations of model-based distribution capabilities able to support highly complex requirements such as thosefor military systems. Provide a generic system framework that supports design for distri-bution, distribution planning, management/execution, and re-planning in response tochanges in demand.

39 mo.,$6.8M

11. Multi-EnterpriseCollaboration

Provide the initial set of methods and standards required for seamless interaction ofmodel-based processes among supply chain members. Demonstrate these capabilitieswith a team of industry partners in a selected manufacturing sector.

34 mo.,$2.7M

12. Model-BasedResourceManagement

Develop enabling technologies to create a foundational, model-based manufacturing en-terprise resource management system framework that is modular, scaleable, and built onopen software standards. Deliver a baseline capability for modeling, simulating, and di-recting control over all manufacturing enterprise resources, and enable expansion to dealwith increasing size, complexity, and functionality of organizational processes.

40 mo.,$4.7M

13. InformationDelivery to Pointof Use

Develop and demonstrate model-based technologies that deliver information to the point ofuse, through flexible, affordable systems that provide for heads-up, hands-free operation.Demonstrate sharing of information created in enterprise planning processes (e.g., productdesign) to the four primary “execution systems” of the enterprise: manufacturing, productservice/support, factory maintenance, and training.

24 mo.,$9.75M


Product Realization & Support 2-1

2.0 PRODUCT REALIZATION & SUPPORT

2.1 FUNCTIONAL MODEL FOR PRODUCT REALIZATION & SUPPORT

The Product Realization and Support function of the manufacturing enterprise includes all activities re-quired to conceive, develop, produce, and support the enterprise’s products, including appropriate dispo-sition of the product at the end of its useful life. Product realization is the core of the manufacturing en-terprise mission – the design, fabrication, and support of products to generate revenue and fulfill the needsof the enterprise’s customers and other stakeholders.

For assessment and planning purposes, product realization and support can be divided into four separateyet interrelated elements as shown in Figure 2.1-1 and described below.

Figure 2.1-1. Functional Model for Product Realization & Support

Innovation &Conceptualization

Includes discovery and definition of new and modified products based on theperceived needs of the customer base, documented customer requirements, a newand innovative product concept, or ideas derived from technology advances.

Product & ProcessDevelopment

Includes those activities required to create designs and specifications sufficientto enable cost-effective production of products that conform to their require-ments. This function also includes developing and implementing any new ormodified processes required to manufacture the product.

ManufacturingExecution

Includes all activities associated with transforming and combining raw materialsand components into a completed product ready for delivery to the customer.

Life-CycleSupport

Encompasses all activities associated with ensuring that the product is main-tained over its intended lifetime, including provision of spares and consumables;repair and servicing; and recycle, disposal, or other conversion at the end of theproduct’s life.

These functions are highly interdependent with the other functions of the enterprise as indicated in Figure2.1-2. The aggregation of all these functions creates a value chain for creating and delivering value tocustomers. The value chain encompasses and integrates every organization, resource, and knowledgeasset involved in delivering value – from initial needs/opportunity identification and development to pro-duction, support, and final disposition at the end of the product’s life.



Figure 2.1-1. Product realization and support processes are highly interdependent withall other processes in the enterprise value chain.

2.2 CURRENT STATE OF PRODUCT REALIZATION & SUPPORT

This section provides an overview of the current state of modeling and simulation (M&S) technology andapplication for product realization and support processes, highlighting some best examples of currentpractice. It also identifies areas where advances – and changes in industry practices – are needed to real-ize the full potential of model-based enterprise concepts.

Model-based product design and manufacturing technologies are in wide use in all sectors of industry andshow great promise for being the enabling mechanism for every step of the product life cycle – enablingrapid development and highly efficient production while eliminating the cost and time of physical proto-typing. Table 2.2-1 notes some of the key attributes of the current state of practice in this area, and Table2.2-2 provides a more detailed assessment for each of the functional elements.

Table 2.2-1.General Observations on Current State of Product Realization & Support

Lagging Practice State of Practice Leading-Edge Practice

• No integration or interoperability• Use limited to specialists; no collabo-

rative access• Limited model characterization/

validation, low fidelity• Spreadsheets most frequent tool• Lack of understanding of economic

justification of modeling (how to assessnontraditional cost savings)

• Stand-alone, single-domain mod-els

• Excellent full geometry models• Hybrid use of modeling with con-

ventional processes• Process systems verify perform-

ance against process models;models used off-line to debugproblems

• Little feedback from product useto refine models

• Integrated performance/geometry and tolerance/variation models

• Full life-cycle cost models• Virtual prototyping replaces

much physical prototyping• Model-based sensing & control

systems taking on more self-diagnosis and real-time controlin manufacturing processes



Table 2.2-2.Current State of Product Realization and Support

Element Lagging Practice State of Practice Leading-Edge Practice

Innovation &Conceptualization

• Little or no use of M&S in design phasebeyond simple CAD

• Iterative design through multiple physicalprototypes & redesign

• Incompatible & incomplete tools for de-sign & analysis

• Product data exchange common butusually requires human intervention

• Execution of design optimizers too slowto permit comprehensive analysis

• Limited skill sets & resources• Limited understanding of relationship

among key parameters• No integration of design & requirements

data

• 3D solid models that drive 2D design• Tools for logic analysis• Optimization of single discipline view of

design• M&S used for specific aspects of design• Extensive model-based analysis of alterna-

tive concepts (parametric & other)• Costing & performance modeling used, but

not integrated for optimization of total prod-uct & process

• Limited analysis of producibility, maintain-ability

• Modeling not yet yielding faster generation& realization of product ideas

• 3D model used throughout process• Analysis Integrated with design• Multidisciplinary optimization of design• Parametric/variant design

Product & ProcessDevelopment

• Little up-front attention to life-cycle issues• Primarily paper-based design, especially

in process design• Little fundamental understanding of

process or materials science• Limited ability to map design to manu-

facturing features• Knowledge represented as parametric

models• Little support for electronic querying of

CAD models• Design tools impose unnatural con-

straints in design process• No mapping of design features to manu-

facturing features/processes• Predictive modeling only in very narrow

domains

• Understanding of processes & materials• Activity modeling• 3-D geometry models used• Finite element modeling• Proprietary performance models• Tolerance models• CAD models for interference/fit• Kinematic, dynamic, & thermal models• Sensors expensive & provide only indirect

monitoring of product’s closeness to design• Process models widely used in continuous

process industries, but little real-time link-age between process modeling & processexecution

• Limited integration of workflows: Concur-rent vs Synchronous engineering, wheredifferent work streams not always workingon same targets/same cadence

• Limited access to design process/modelsgranted to customers & supply chain mem-bers

• Scientific models (first principles) forprocess design & material properties

• Process capability in design decisions(robust design)

• Manufacturing best practices leadmanufacturing design – more than pro-ducibility & taking IP & making betterproducts

• Prediction of microstructure based onchemical & metallurgical properties

• Process simulators• Circuit layout & simulation• Immersive environments for virtual ve-

hicles• Automakers (and other industries) use

models for design reviews, testing, etc.• Distributed models run in own domains

but communicate w/ each other• Spectrum multi-physics model• Full product models• Robust proprietary specialty models for

magnetics, kinematics, airflow, etc




ManufacturingExecution

• Many models not physics-based; limitedfidelity/verisimilitude to real world

• Lack of process & materials knowledge• Little or no material characterization

• Poor accommodation of variable material(inclusions, defects, chemistry, condition,dimension, etc)

• Lack methodology for equipment model-ing

• Lack science to allow predictability oftooling performance

• Not flexible in fixturing design to accom-modate material variation

• No tying of tools, fixturing, & machinestogether in models

• Smaller firms still use manual approach• In-process gages not robust & do not

work well in production environment

• Difficulty of integrating sensors with con-trols & process knowledge

• Actively growing some good process models• Commercial tools – CAPE, CAPP, Cognition,

DFM/DFA, & tolerance modeling apps• Plant layout modeling is OK, but info transfer to

other apps is lacking• Little understanding of material transformation

science• Little use of analytical models for process appli-

cations• Proprietary controllers• Limited use of sensors

• Test & inspection based on empirical ratherthan analytical process models

• Test, inspection, & validation are significant costdrivers

• Parametric acceptance as a basis for estab-lishing product quality

• Processing plan & control system designed forcontinuous process products without considera-tion from plant floor & control parametersneeded — causing more cost for control systemintegration & configuration

• Control models for continuous process productshard-coded for steady state plus expected ex-cursions; cannot handle unexpected

• Little or no ability for real-time model updatesbased on process performance

• Increasingly agile automation (can usecapital equipment to do multiple tasks)

• Limited modeling in real-time the cur-rent state of the product activities andfactory, fed by sensors (now beingdone to a limited extent) e.g., with RF

• Electronics & continuous process in-dustries lead other sectors by widemargin

• Excellent models available for kinetics& discrete events, molding, casting,forging

• Some processes (e.g., stamping) highlyautomated, well understood and mod-eled

• Some use of Design for Assembly(DFA) planning tools

• Simulation tools (e.g., Vericut, Deneb)used to validate control programs

• Some sensors in continuous processestaking on more intelligence, self-diagnosing process, and self-correctingwith actuator role

Life-CycleSupport

• Predictive modeling only in very narrowdomains

• Very little modeling of product endures tosupport actual use

• Poor, non-centralized record keepingamong supply chain partners

• Little or no support cost info collected atproduct level

• Design for maintainability, reliability• Spreadsheets used to predict quantities & costs

• Almost no telemetry or feedback from product inuse to affect future designs

• Abundance of data from all life cycles createsmore vulnerability to liability claims

• GIS-based models support distributionplanning

• Use of product models & simulations tosupport training & trouble-shooting

• Consideration of final disposition ininitial design

• DoD modeling part obsolescence

• Some prognostic tools for predicting lifeexpectancy

• Some companies using GIS-basedproduct feedback & awareness tocommunicate maintenance needs, de-sign problems



Leading companies in every sector have made significant investments in M&S tools to achieve a differ-entiating product capability or to respond to critical business needs. However, even in the technology-intensive aerospace sector, industry lacks the ability to perform true multi-disciplinary optimization ofproducts in a model-based environment. Current modeling capability centers on use of an increasinglycapable base of commercial 3D design tools and analysis applications, supplemented by custom modelsand simulations developed in-house to meet the needs of a specific product or product family.

Increasingly, companies are also realizing the needto capture model-based product knowledge tosupport products throughout their life. Althoughtrue model-based life-cycle management is not yetpossible, tool vendors are addressing this ex-panded scope with product life-cycle management(PLM) software applications. Current PLM toolsare typically extensions of product design tools.

As pointed out by CIMdata,1 PLM is not a tech-nology per se. It is instead a business approach tomanaging the complete set of product definitioninformation – creating that information, managingit, and disseminating and using it throughout thelife cycle of the product. PLM is an approach inwhich the processes are as important, or more im-portant, than the data. PLM is as much concernedwith how a business works as it is with what isbeing created, focusing on:

• Secure, managed access and control of prod-uct definition information

• Maintaining the integrity of the productdefinition and related information through-out the life of the product

• Managing the business processes used tocreate, manage, disseminate, share, and usethe information.

However, despite a greatly improved ability toshare design information in collaborative envi-ronments, companies continue to limit access outside the walls of their immediate organizations. This isdue in part to the need to protect proprietary product and process data from competitors, and in part to anindustry mindset that information exchange with external stakeholders (e.g., customers, suppliers, regu-latory agencies) must be tightly controlled in order to manage expectations.

Capturing “Reality” in Models

Modeling of products and processes with complete real-world fidelity is not yet possible. Current toolssupport models with sufficient depth of detail and complexity to provide high geometrical precision andmeet a large percentage of different disciplines’ needs for information to support product development.Present simulation tools are limited in the types of information and the level of detail they deal with, andproprietary formats make it difficult to exchange information among different functions, disciplines, andtools. Current capabilities are lacking in areas such as design allowances, reliability, producibility, ac-

1 http://www.cimdata.com/PLM/plm.html.

Viewing the Productfrom Anywhere in the Enterprise

With model-based collaborative tools such as AutoVue andEnterprise VisView, all enterprise stakeholders today canaccess 3D product design data and provide feedback at allstages of the design process. Customers, engineers, suppli-ers, and manufacturing staff can review, analyze, and inter-act with product designs to improve performance, quality,and other factors – helping avoid costly errors and reducetime-to-market. Advanced viewing features allow users torotate, pan, and explode assemblies; take precise meas-urements; section models; and more.

The current generation of these applications integrates withdocument management, PLM, and enterprise resource plan-ning (ERP) systems to provide embedded viewing andworkflow control.

http://www.cimmetry.com/cimweb.nsf/pages/CATIA_viewer



commodation of uncertainty, and other functionsthat are essential to translating products fromconcept to production. There is also a lack oftools that support high-fidelity cost modeling ofproduct components or processes to guide designfor affordability earlier in the product life cycle.

The limited ability of today’s M&S tools to in-terface with each other in an integrated fashion isa major barrier to multi-function optimizationand process planning as an automatic element ofproduct development. The major software ven-dors are addressing needs for multidisciplinarycoordination by adding PLM functionality andimproving integration with their tool or amongtheir product lines. For example, ParametricTechnologies Corporation (PTC) markets morethan 50 of its tools as an integrated suite2, andCATIA’s AutoVue provides users of variousERP and document management tools the abilityto review and comment on designs from theirdesktops. Some tools are appearing that addressthe problem of communication between com-peting applications, such as the ProEngineerPlug-in for CATIA V5 data. Despite these ad-vances, for most manufacturers product optimi-zation remains an iterative, primarily manualprocess managed using disciplines such as con-current engineering and integrated prod-uct/process development (IPPD).

Another key issue is that current product mod-eling capabilities make little provision for recon-ciling the as-built design to the engineered de-sign. The ability to accurately model changes as the product ages and is put to use by its customers isalso a distinct gap. Also, product performance models are usually limited to normal operation; failuremodes and effects are typically defined through off-line analysis, and are not coupled to the actual elec-tronic product definition.

Ease of Use

A final barrier with current modeling and simulation tools is their complexity. Most modeling applica-tions require specialized training, and many companies cannot afford such training beyond a limited,business-critical implementation. The level of skill in today’s manufacturing workforce is insufficient tosupport a pervasive modeling and simulation environment. Easier-to-use interfaces and “smart” built-intraining interfaces are needed to facilitate wide use of model-based tools and practices. Here again, theenterprise must balance the need for high fidelity of models in critical engineering and business functionsversus the need to provide lesser and tailored “subsets” of the same models for other purposes, includingexternal information sharing and collaboration.

2 http://www.ptc.com/products/sw_landing.htm.3 http://www.cadserver.co.uk/common/viewer/archive/2001/May/10/feature10.phtm.

3D Design Tools Enable RapidCustomization to Meet Customer Needs

National Paintball Fields (NPF) operates Europe's largestpaintball venue and is a leading manufacturer of paintballmarkers (guns). These high-precision “weapons” propelpaintballs at over 200 miles per hour and contain sophisti-cated electronics to monitor performance.

Professional and tournament paintball players value indi-viduality, so NPF responded to demands for customizedproducts with anodized mixed-color finishes and cut andcarved surfaces tailored-made for each customer.

"As demand grew for more sophisticated and individualsculpting,” said NPF’s Nick Marks, “both the CAD and theprogramming for the CNC systems were a major bottle-neck to production. Even working long hours and week-ends we could only manage two personalized markers perweek."

To address the bottleneck, NPF implemented an integrated3D design and manufacturing solution based on ProEngi-neer, expanding its capacity to design and machine per-sonalized markers by a factor of 10.3

Defense contractors are using ProEngineer, CATIA, Uni-graphics, and similar tools to do the same kind of customi-zation for real weapon systems. Horizontal technologyinsertion (HTI) techniques enable sharing of critical sub-systems such as lasers and sensors for multiple systems.This greatly reduces the time and expense of system up-grades, enabling the military services to share develop-ment costs and improve affordability in delivering new ca-pabilities.



Given the current state of the art, mostcompanies have not yet seen the move towidespread model-based enterprise proc-esses as one that is clearly worth the costand effort. However, the savings providedby these tools can be significant. In theautomotive industry, model-based toolshave helped reduce the time required tomove a new car design from concept to theproduction line from 3 years to about 14months.

In the defense sector, programs such as theF-35 Joint Strike Fighter (JSF) and FutureCombat Systems (FCS) are relying onM&S technologies to meet DoD’s goals toreduce acquisition and support costs by50% compared to current weapon systems.Most of this savings is targeted to comethrough use of Simulation Based Acquisi-tion (SBA) and systems engineering prin-ciples. SBA applies detailed modeling toidentify the cost impact of different systemrequirements, and trade off cost versusbenefits. Systems engineering ensures thatthe requirements are driven down to thelowest level of design, and uses modelingand simulation to optimize the designthrough system and subsystem-level trade-off studies.

Process Industry Needs

While many of the attributes of the currentstate of model-based product realizationand support apply to all types of manu-facturing, the process industries (e.g., pro-ducers of chemicals, foods, and processedraw materials) have unique needs and con-cerns. Current manufacturing processmodeling and simulation activities in thissector largely focus on development andtroubleshooting of selected portions ofspecific unit processes. While these toolsare valuable, they are incomplete and lack interoperability for application to other functions such as proc-ess planning and product/process optimization. Comprehensive standards for process modeling and inte-gration, interoperable models for individual unit processes, and the means to share these resources areneeded to enable the industry to build a rich base of process models that can be “plugged together” to cre-ate factory-level models.

In this type of manufacturing, developers typically apply (or create) a theoretical model of the materialsand transformations required to create a desired product. The model is validated through iterative lab-scale testing and then updated as the process is scaled up to ensure it performs as intended for production.

Model-Based Analysis Tools Help Santa CruzBicycles Improve Performance and Profits

To bring a new suspension design to market, Santa Cruz Bicycles(SCB) used a mixture of analysis and modeling capabilities oper-ating from PTC’s Wildfire 2.0 interface. These included behavioralmodeling for initial analysis and design; MCAD to give the bike itsstructure; advanced surfacing for aesthetics; mechanism dynam-ics to analyze clearances and loads; and structural simulation forfailure analysis.

"The most important capability for us is behavioral modeling,"says SCB’s David Earle. This allows up-front capture of designintent explicitly within the CAD program. The engineers use it tolocate important pivot points, and keep track of the wheel pathand shock rate.

The finite element analysis of the main link examines the stresseslikely to be applied to one component of the virtual pivot-pointtechnology.

ProEngineer’s behavioral modeling tools obtain probabilistic opti-mization results: sensitivity studies to determine the effects ofspecific parameter changes; optimization for specific require-ments; feasibility studies to find possible solutions without specificrequirements; and multi-objective studies based on design of ex-periments. Users can go back and forth among the tools and saveresults as design features.

“Once we have the information we need from behavioral model-ing, we use CAD and create the hard form of the bike around thepivot points," Earle says. "We can create the ‘look’ we want withadvanced surfacing later on. What used to take six or sevenhours now takes five minutes. By saving so much time at eachstage, the engineering department gained an additional 415 min-utes per simulation, and we used that time to refine the suspen-sion system further, to levels previously impossible."

http://www.deskeng.com/articles/04/july/cover/main.htm



The engineers rely on flowsheets and material balance calculations in specifying process equipment,making extensive use of CAD/CAE tools to design the process systems with the requisite throughput, re-liability, and controls. However, fully bridging the gap between the theoretical model and a reliable pro-duction process requires manual translation of product attributes and transformations into executableprocess instructions and control parameters.

High-profile processes such as metal forging have been the primary focus of material behavioral model-ing, simulation tools, and user interfaces. In the area of large structural forgings, for example, die andprocess steps are now exercised in a 3D finite element modeling (FEM) environment prior to any trials.

The aerospace industry sees two fundamental issues associated with unit process models: 1) the need to“fill the gaps” by continuing to develop simulation tools and validated models for key unit processes; and2) assuring the ability to integrate these tools to create a comprehensive simulation suite based on com-mon data models.

Lack of depth and precision of the underlying physics and math in process models is a fundamentalproblem that must be addressed. Current models provide an approximation of the reality of a process, butdo not accommodate variability or uncertainty sufficiently to mitigate risk without extensive prototypingand testing.

2.2.1 CURRENT STATE OF INNOVATION & CONCEPTUALIZATION

Currently, it is not possible to automatically generate a model of a product from an input of customerwishes or specifications, and few modeling tools are available to help turn ideas into “executable” productconcepts. Monte Carlo simulations have been used for years in the defense community to evaluate theeffectiveness of new weapon concepts, but these tools are limited to looking at discrete performance vari-ables (e.g., range and probability of kill) in order to define top-level requirements for a particular system.DoD programs such as WARSIM and ADST are advancing interactive M&S capabilities to create simu-lated battlefield environments where new system concepts and tactics can be explored in the virtual realm,and where users can train without the need for costly specialized simulators. Such capabilities are vital inhelping to optimize conceptual designs on the front end of the requirements definition and systems engi-neering process.

The current generation of requirements management tools (SLATE, RequisitePro, and others) do a goodjob of creating a database of parsed and linked requirements to aid the systems engineering process, butthey do not interact with CAD-based design systems to provide requirements in a form directly useable bydownstream design and manufacturing applications.

In continuous process industries, a product concept may be modeled well, but there is no direct means oftranslating that model into an executable process or parametric attributes to create the envisioned product.

Product definition is a critical starting point in the development of any new product. However, there are anumber of common shortcomings to the process of product definition in many companies: 4

• No defined product strategy or product plan

• Lack of formal requirements as a basis for initiating product development

• Product requirements developed without true customer input

• Marketing requirement specifications (MRSs) are completed late, after development is underway,and are typically incomplete, ambiguous, or overly ambitious

• Engineering has little or no involvement in development of the MRS, and thereby lacks a true un-derstanding of the requirements

4 Product Definition, Kenneth Crow, DRM Associates, http://www.npd-solutions.com/pdef.html.



• “Creeping elegance” or a constantly evolving specification that requires increasing developmentscope and design iteration.

There are impressive advances in visualization technologies, simulation capabilities, and integration ofinformation and analysis tools, but these are typically deployed only in best-practice applications thatwarrant the significant investments required. One best-practice example of a tool that can analyze multi-ple design options is Visteon’s Unified Parametric Vehicle™ (UPV). Auto companies apply UPV in de-sign and performance prediction for powertrain and climate cooling systems. Weather conditions, drivingconditions, interior cooling, and powertrain cooling requirements can be modeled to analyze design op-tions for engine thermal management systems against criteria such as weight, fuel economy, emissions,and occupant comfort. This allows cost-effective evaluation and refinement of a wider scope of designoptions in a shorter amount of time, with fewer downstream engineering and manufacturing changes.

The linkage between M&S for design optimization and for product and process concurrency is strength-ening. In the most advanced applications, designers using desktop “virtual cockpits” can launch analyti-cal simulation tools to evaluate design alternatives. With the computing power available today, manyanalyses can be performed in a few hours, at an acceptable level of fidelity for conceptual evaluation.However, it is still generally true that there is minimal ability to capture customer preferences as an inputto the product requirements definition step that drives the product realization process.

Two other ideas should also be noted: 1) product conceptualization is not the only time that communica-tion with the customer should take place. The customer and supply chain members should have visibilityinto the product at every phase of its life cycle; and 2) product conceptualization should be viewed as acontinuous process. Progressive enterprises take a stewardship approach to their products, continuallyseeking ways to improve or upgrade the products to better serve the needs of all value chain members.

2.2.2 CURRENT STATE OF PRODUCT & PROCESS DEVELOPMENT

Current M&S tools for product and process development are generally dedicated to single functions orprocesses, and tradeoff tools for optimization based on product, process, and resource options are in theirinfancy. The ability to optimize based on an assessment of product performance, process capability, andresource availability has been demonstrated in a few specialized environments, but is not widely used.

Despite the widespread availability of CAD tools,5 creation of models is rarely on the critical path ofprocess development for discrete manufacturing. Models of processes are often created at a high level todesign manufacturing flows or at a detailed level to help diagnose a problem, but are rarely used to createand optimize product and process designs as standard practice. Product and process development havehistorically been accomplished by testing a design to see how well it works, then modifying and testing itagain. Modeling and simulation of processes is particularly expensive and time-consuming, and thus islimited to applications with a high return on investment.

In continuous process industries, the product typically starts as a model of some material transformationprocess, and the process model drives the design of the product and the process systems for its manufac-ture. However, the process design is usually made without detailed consideration of control parameters.This is a major deficiency, considering that design decisions determine up to 40% of the cost of processcontrol systems.

The large investments required to implement model-based product and process development present amajor barrier as manufacturers continue to focus on short-term profits ahead of life-cycle value. The lackof good awareness and confidence in process simulation tools makes it difficult to secure support for theneeded software development, even though they have potentially large payoffs in time, resources, andprofitability. Government investment in this area has been lacking for similar reasons. Model validationand verification are key needs to overcome these barriers in the manufacturing environment. Currently,

5 CAD tools are currently estimated to have an installed base of 20 million users. (http://www.jonpeddie.com/special/CAD.shtml)



quality assurance requirements for safety-critical products (e.g., defense products) do not permit process-based validation and verification.

In areas where modeling and simulation is being applied, the results have been positive. Simulation toolsare delivering excellent returns on investment in areas such as forging and spin forming, supporting crea-tion of complex net shapes optimized for performance and cost-effectiveness. Model-based tools formold design, pour, solidification, and defect prediction in development of complex investment castingshave delivered significant improvements in manu-facturing yields.

Model-based product performance simulations arebeing used extensively in product certification.The application of simulations to conduct destruc-tive testing has greatly reduced the need for physi-cal tests. Hard particle and bird ingestion models,for example, are widely used today in certificationtesting for gas turbine engines. These capabilities,although demonstrating the real value of model-based simulation tools, still lack truly accuratepredictive capability – and the ability to incorpo-rate the uncertainties necessary for confident vali-dation.

Visualization technology has progressed rapidly,and the technology is outpacing its use in theproduct design environment. As mathematicalrigor and geometric accuracy in modeling catch upwith display capability, application of these tech-nologies will expand. Today, 3D designs areviewed and archived in a 2D CAD medium. AsCAD capabilities are implemented in 360-degreevisualization environments with functionalitiessuch as tolerancing and collision detection, thevalue of these technologies will increase tremen-dously.

In many areas, computational complexity still pre-vents simulation tools from supporting adequateand timely decision-making in the product designprocess. There is little knowledge of the underly-ing physics of most materials and transformationprocesses, limited ability to reuse knowledgeabout a product, and few tools or methods thatenable product designers to electronically take nonphysical factors into account. Modeling the physicalrepresentation, performance, cost, producibility, and life-cycle features of a product demands robust ca-pabilities to capture, transform, translate, and exchange knowledge and data. The increasing complexityof new products and technologies also increases the demand for concurrent, multi-disciplinary optimiza-tion of products and processes.

Interoperability continues to be an industry challenge, barring efforts to achieve integrated systems thatuse and apply knowledge from partners and supply chain members in the design process. The PDES/STEP initiatives have made good progress in improving exchange of product definition data, and other 6 http://www.solidworks.com/pages/successes/viewsuccess.html?record=865.

Integrated Design Tools Making anImpact on Mars as Well as Earth

Alliance Spacesystems, Inc. (ASI) designs and manu-factures mechanical systems, robotics, structures, andmechanisms for spacecraft and scientific instruments,including the robot arms used on Spirit and Opportunity,the two rovers developed by NASA for the Mars Explora-tion Rover mission.

The Mars project presented significant challenges. Usingthe SolidWorks mechanical design system, ASI was ableto develop the required highly precise, complex mecha-nisms in collaboration with NASA scientists to meet acompressed design schedule with limited resources. In-tegrated COSMOS analysis software enabled ASI to testand optimize the design of parts and assemblies to meetthe rigors of the harsh Martian environment.6

Companies such as Swagelok, a leading supplier of pre-cision fluid system components, also use applicationslike SolidWorks to generate designs more efficiently andreduce product development time. Integration with down-stream applications such as FEA, CFD, and PDM soft-ware enabled Swagelok designers to engineer an en-tirely new product line for the biotechnology and pharma-ceutical markets in less than a year – approximately halfthe time of more conventional systems.



efforts are under way. Following the lead of the graphic design software industry, the major CAD ven-dors are beginning to roll out “plug-ins” enabling importation of files created in competitors’ tools. Thesecapabilities remain far from seamless, however.

Materials engineering and manufacturing process design applications are currently not well integratedwith product design and visualization tools. Geometric representations are not mathematically complete,nor are they sufficiently precise to be directly useable in process design and control. Process simulationsare applied independently from design and, in general, results are communicated manually to the designteam. Material modeling is not scaleable and has only limited ability to account for variability in materialconstituents or quality (e.g., impurities).

Continuity across product and process modelsis necessary to tie material properties at theirmost fundamental level to material propertiesas they apply to processes and products. FordMotor Company engineers are developing thecapability to integrate materials and manufac-turing process attributes and models with thegeometric representation of product, from boththeir own design environment as well as fromsupplied subsystems and components.

Integrated product/process models are widelyregarded as a need, but the enabling tools havenot yet evolved to support their realization.Electronic product data exchange is common,but often requires human intervention to correctthe results. Many engineering and computingtools exist to facilitate transition of a productdesign from the conceptual stage to a detaileddesign. For discrete products (i.e., manufac-tured parts and assemblies), the CAD tool thatgenerates the conceptual design is usually thesame one used for detailed design. The soft-ware tools used for analysis and simulation areusually separate products from the CAD sys-tems, and the degree of integration of these tools with CAD systems varies widely. Where designs arecreated by organization working in collaboration, the use of different CAD systems can severely compli-cate the integration of CAD models with analysis and simulation tools.

Interoperability of simulation tools is greatly lacking in the process realm. While discrete simulationsmay deal with product stress and temperature profiles during individual processes, they seldom deal withthe total performance profiles of products and processes across multiple operations, and even more rarelydo they enable concurrent optimization of multiple product and process parameters.

Lack of standards is a major concern in all model-based simulation applications. Compatibility in productdata exchange, standard representation of product and process, compatibility of simulation systems withprocess information systems, scaleability from micro to macro levels, all must be addressed as we moveto the next level of cost-effective, high-performance simulation. There is little incentive for suppliers tomodel complex assemblies in detail, because the multiplicity of incompatible systems limits the scope ofutility and cost-effectiveness. Currently, integration and optimization of capacity is difficult in a globalmanufacturing environment and distributed supply base. Factors contributing to this condition include 7 http://www.3ds.com/en/home.asp.

Whirlpools Applies Digital ManufacturingRecipe to Shorten Time to Market

Whirlpool Corp. is embarking upon digital manufacturingusing DELMIA Process Engineer and V5 DPM Assemblytools linked by a Manufacturing Hub.

Production manager Anders Claesson explained, “Withthree new microwave platforms about to be introduced andno integration between our materials and planning system,we recognized it was a good time to move to a digital envi-ronment.”

Before adopting the software, Whirlpool ran a pilot programwith application engineers stationed on-site helping to im-plement and integrate the tools and work flow.

The engineers are now gearing up for the next product line.The Manufacturing Hub, a repository that stores both his-toric and current product, process, and resource information,enables engineers to continuously update and share data tobetter manage all processes and equipment orders. Manu-facturing processes can be created and evaluated includingtime analyses, rough balances, ramp-up scenarios, andcapacity analyses. The scenarios are stored in the Hub,allowing engineers quick access to information for reuse andto support decision making.

“Ultimately, we anticipate that the technology will acceleratetime-to-market through faster product and process verifica-tion and validation”, said Claesson. “Manual data transfersshould become a thing of the past.”7



proprietary information concerns, adversarial OEM/supplier relationships caused by a low trust factor,and the lack of cost visibility across the supply chain.

Industry lacks collaboration strategies to solve these basic issues, and export control regulations imposeadditional restrictions (particularly in the aerospace/defense sector) on sharing of product and process in-formation throughout a supply chain.

2.2.3 CURRENT STATE OF MANUFACTURING EXECUTION

The manufacturing process uses raw materials and a detailed production plan to create deliverable prod-ucts. The process must be faithful to the design to produce the product that was intended. In most cases,the manufacturing process, through human guidance, is refined and tailored to produce product conform-ing more closely to the stakeholders’ needs than is communicated by the design. In these cases, knowl-edge is added to the initial design information to produce a better result.8

For discrete products, finished articles are produced through the direct, complex interaction of tools andmaterials; e.g., machining of a part. In the case of continuous product processing, factory control systemsare generally simple and indirect, using secondary effects to accomplish desired results. For example,instead of commanding an absolute flow rate, we command a valve to turn and monitor the resultingchange in flow, then adjust the valve again to obtain the desired result.

In assessing the current state of model-basedmanufacturing process execution for eitherproduct domain, the key question is, “how ef-fective is a process in converting raw materialsand design information into the right productthat satisfies all stakeholders?”

Model-based simulations today add little tomanufacturing execution in traditional proc-esses. Starting a production line with a newproduct presents many unknowns, and manyprocesses – especially those supporting complexproducts – require a great deal of experimenta-tion, tuning, and enhancement early in their lifecycle. Production managers manually tweakprocesses for lower sensitivity to environmentalchanges and variability in raw material proper-ties, and continuously improve fixtures, tooling,and workflow aids in their effort to optimizeproduction. This is most often done withoutusing M&S tools unless some problem is be-yond the shop engineer’s capability to solve.

Process models are sometimes used to controlthe process, or at least monitor the productionrate. For example, in stereolithography theproduct definition is directly used to generatethe control parameters used to fabricate the part.In continuous processes, newer sensing andcontrol systems are taking on capabilities of

8 First Product Correct, IMTI Inc., October 2000.9 Abstracted from June 1, 2004 article By James Wallace, Seattle Post-Intelligencer,

http://seattlepi.nwsource.com/business/175791_composites01.html.

Virtual Manufacturing is Key toRapid Final Assembly for Boeing 7E7

Manufacturing of the 7E7, Boeing’s first new jet since the777 has received much attention for using composite wingsand fuselage instead of metal, but there is another majordifference.

A dozen years ago, the 777 was the first digitally designedcommercial airplane; no physical prototype had to be built.This time, engineers will not only design the plane digitallybut also the entire development and manufacturing processand the aircraft’s entire life cycle.

Before the first 7E7 part is made, the plane will have beendigitally defined and produced; so will the tooling and theassembly processes. Boeing and its partners will create avirtual-reality airplane, and everything needed to build it,from inception to rollout.

From Japan, Italy, and the U.S., the composite structureswill come into the factory certified, tested, and ready forfinal assembly. A moving line will carry the center fuselagesection slowly down the factory floor as other sections –wings, front fuselage, and aft fuselage – are joined to it.

In just 72 hours, a 7E7 will be assembled and ready forpainting and delivery.9



process actuators and thus more ability to self-diagnose and make adjustments in the process, based onembedded algorithms.

Many of the barriers to wider use of emerging model-based technologies relate to insufficient scientificunderstanding of processes and materials, which limits the ability to create an effective model. We mustimprove the ability to model processes and understand the situations or product requirements where thoseprocesses should be used. The ability to create accurate models of unit processes is key to model-basedplanning and control of end-to-end production lines, a concept pioneered by the DOE/Industry Technolo-gies Enabling Agile Manufacturing (TEAM) program in the mid-1990s (Figure 2.2.3-1).

After the product and processes are defined, infrastructure such as tooling and fixtures must be put inplace. This activity is still done largely by humans using CAD tools and physical mockups. Assembly,testing, packaging, and shipping must all be similarly supported, and process plans must be generated and

Figure 2.2.3-1. The ability to create accurate, interoperable models of unit processes is key tomodel-based planning and control of end-to-end production lines.



distributed to shop supervisors along with instructions and training for the production staff. Much of thisinformation is still generated by humans, although progress has been made in use of modeling and visu-alization tools to automate tasks such as creation of manufacturing process plans.

The major barrier to achieving successful production of first articles is inability to produce a robustprocess model as part of an integrated product/process design. Inability to accommodate change orvariation in process models is a pervasive issue, as is limited ability to incorporate underlying science toaccurately predict performance. Models and simulations are usually specific to one part of the productionprocess, and there is little integration across parts, fixtures, tooling, and material properties. The inabilityto understand, model, and control processes in three dimensions is certainly a major barrier in materialprocessing. Cooling rate variability, for example, is a major uncertainty factor in casting as well as influid processing operations.

It is possible today to characterize or approximate the dynamics of a machine or processing system withthe goal of compensating for temperature fluctuations, wear patterns, and other variations, althoughprecise modeling is difficult and expensive. However, the better that process transformations can besimulated by associated transformations in the related models, the greater the capability will be forproducing correct product on the first pass and every pass.

2.2.4 CURRENT STATE OF LIFE-CYCLE SUPPORT

While modeling and simulation are becoming increasingly valuable in product development, theapplication of these technologies to support the other phases of the product life cycle remains limited.Automated tools have transformed the way product support requirements are managed, customers aresupported, and products are maintained, with the automotive and aerospace sectors leading theimplementation of new processes, tools, and techniques. However, modeling in the area of life-cyclesupport remains largely limited to use of CAD tools to design support equipment; spreadsheets tocalculate quantities, costs, and reliability; geographic information system (GIS)-based models to supportdistribution planning; and custom simulations to support troubleshooting of product support problems.10

Training is an area where model-based simulation technologies are delivering great value, but benefitshave been slow to materialize because training is undervalued in the product equation – traditionally be-cause the product design must be locked down and in production before starting development of costlytraining aids.

However, advances in desktop computing power are enabling system designers to shift more training tolower-cost generic platforms. Government initiatives such as the Marine Corps’ Aviation SimulationMaster Plan program and the Navy’s Generic Reconfigurable Training System are pursuing improve-ments in training cost and effectiveness through use of common modeling technologies and simulatorsthat support multiple training requirements from a common baseline. Virtual reality (VR) techniques,driven in large part by advances in the video gaming industry, are enabling pilots and equipment opera-tors to accomplish more training with less reliance on costly hardware-based simulators.

Modeling with 3D CAD and VR tools is improving the quality and safety of training while reducing thecost of developing and maintaining training materials. Product models generated by designers are nowbeing ported directly into training media, reducing the cost of creating training content. This also enablestraining designers to work with product designers in collaborative engineering environments to producecomplex multimedia training materials. Assembly models and simulations developed to optimize productmanufacture are being used directly to train maintenance and repair staff, and VR techniques enable op-erators of hazardous processes to gain proficiency in a completely safe environment.

10 Modeling & Simulation for Affordable Manufacturing, IMTI, Inc., January 2003.



CAD designs can also be downloaded to stereolitho-graphy systems to produce physical models, thus re-ducing costs associated with creating training aidswhile providing exact form/fit replicas – which is in-valuable for maintenance training.

Reliability and maintainability (R&M) have long beenimportant factors in product design. However, beyondthe use of spreadsheets for calculating reliability as afunction of parts count (or as a function of the knownand predicted reliabilities of each of a system’s com-ponents), until recently the use of simulation tools inthe R&M arena has been limited. Practical modelingcapabilities at military maintenance and repair facili-ties are virtually non-existent, due largely to a combi-nation of limited budgets, cultural resistance tochange, and a predominant focus on simply getting thework done. Resources to support the needed technol-ogy investments are limited, since these are alreadystressed to meet day-to-day operational requirements.

In the commercial sector, investments in modeling andsimulation for design are paying dividends in terms ofhelping deliver products that are more reliable andeasier to maintain. The evolution of 3D CAD to sup-port assembly modeling for reduced cost and im-proved quality in manufacture has yielded additionalbenefits of making products easier to service and re-pair.

However, many barriers remain. Poor, non-centralized recordkeeping means that it is difficult (ifnot impossible) to develop the rich databases requiredto understand maintenance and repair history and theassociated cost of a product, much less make informedpredictions. Feedback from the field to the factory istypically limited to basic warranty service information, which is inadequate for detailed modeling. Manu-facturers often lose visibility of what happens to their products after delivery, and communication be-tween primes, users, and support functions is fragmentary unless there is a serious problem. In thesecases the prime organization focuses its M&S assets to analyze the problem, work with the customer todetermine root causes and corrective actions, and implement required changes.

System of Systems…is a concept that emerged over the past decade inthe defense community with the recognition that wecan no longer afford to design and support complexweapon systems as stand-alone products. In themilitary environment, individual weapons must worktogether as an integrated system to accomplish theirindividual and collective objectives. This concept iseven more important for the organizations that sup-port these products – maintaining and servicingthem, providing training, troubleshooting problems,and coordinating the often conflicting requirementsof different stakeholder organizations.

M&S in logistics supply chains range from limited tononexistent. Recurring problems and issues inmaintenance and repair of specific products are re-ferred back to the supplier or prime contractor, whichimposes long delays in problem solution. Aggrega-tion of the information about the products and sys-tems and their problems into an integrated set ofmodels would provide tremendous improvement.

Much work must be accomplished to turn “system ofsystems” from a principle into tools and applicationsfor the future manufacturing enterprise. M&S is acritical enabler of this transformation. Currently thereis no accepted modeling framework to support con-current evaluation, optimization, and management oflife-cycle requirements for complex products thatshare a common operational environment.



2.3 FUTURE STATE VISION & GOALS FOR PRODUCT REALIZATION & SUPPORT

Seamlessly integrated M&S tools will enable distributed teams to quickly create product and processdesigns that achieve the best balance of performance, cost, robustness, and other factors. The productmodel and underlying knowledge base will control all processes across the product life cycle, capturingand sharing data to drive continuous improvement throughout the enterprise. Manufacturing proc-esses will be designed and qualified entirely in the virtual realm, drawing on scientifically accuratemodels of materials, unit processes, and equipment. The resulting model-based knowledge base willsupport all aspects of maintenance, training, and life-cycle support.

The ultimate vision for product realization processes in the model-based enterprise is the ability to seam-lessly move back and forth between the virtual representation of the product and its processes, and thephysical reality of the processes as they occur in real time. The product model will monitor and guide theproduction process and support analysis and decision processes to address changing requirements anddeal with off-normal conditions and other problems. This tightly coupled virtual/real representation ofthe product and processes will be visible to all value chain members in real time as they perform theirfunctions, showing the status of the manufacturing process and their position in that process. This visionof a seamless value chain cannot be realized without the unifying base of the comprehensive productmodel.

The model-based product realization environment of the future (Figure 2.3-1) will consistently deliverbest designs to satisfy a balanced set of objectives for the enterprise and its stakeholders. Rich andmathematically accurate visualization environments, augmented by powerful analytical tools, will allowusers to interactively refineobjectives and preferences inperforming trade-offs foroptimization. As each pref-erence is specified, the userwill see its impact on per-formance, cost, deliverytime, aesthetics, and otherattributes of interest.

The resulting product modelwill not be merely a productrepresentation coupled to adatabase of physical attrib-utes. Rather, it will be a to-tal product definition that iscontinuously linked to allsources of technical andbusiness information thatdefine and affect it through-out the life cycle. Thisknowledge base will notonly enable the enterprise toradically improve its abilityto design, produce, and support its products, but will provide a total audit trail of actions taken and sup-porting rationale. This will significantly improve the ability of the enterprise to address and favorablyresolve potential liability issues throughout a product’s life.

Figure 2.3-1. Future design and manufacturing systems will operate fromproduct models that link to all relevant information across all enterprise proc-

esses and the entire product life cycle.



Future manufacturing process designers will draw on a comprehensive library of validated, thoroughlycharacterized models and simulations of common materials, unit processes, and manufacturing equipmentto integrate optimal process designs for individual products. Resources internal or available from supplychain partners will be modeled in terms of their characteristics and availability for any enterprise project.Interoperable, scaleable models that understand – and actively search for – the information they need forcompletion will be standard tools for product and process engineering and manufacturing execution.They will autonomously determine and search for the information they need to satisfy the requirements ofthe specification and production plan, using intelligent digital advisors to guide human users in makingbest decisions at every step.

Equipment and tooling manufacturers and material commodity vendors will provide validated 3D models,performance simulations, and supporting data as a standard part of their equipment and products, withstandards ensuring the ability of different models to integrate in plug-and-play fashion. This will enableprocess designers across a supply chain to quickly create accurate virtual production lines, filling in gapsonly as needed for product-specific tooling and proprietary processes. Virtual test environments will en-able product and process designers to subject their designs to “test to destruction” rigor without makingphysical prototypes.

The manufacturing execution team will use process simulations coupled with certified material, equip-ment, and process models to optimize the manufacturing strategy, testing and “producing” product in thevirtual realm to verify readiness for production. These same models will control the product manufac-turing process, with low-cost sensors and intelligent monitoring systems continuously comparing per-formance against the process models to keep the systems running in continuous conformance with re-quirements and specifications.

Model-Based Product Realization Environment: A System of Systems

Future M&S tools will support not only the development of new individual products, but will enable bet-ter management of complexity in “systems of products.” This will enable manufacturers to build on ex-isting capabilities to get optimal results on a new product, and optimize the new product with respect toall other products with which it will interact in operational use. Product and process models will seam-



lessly integrate in the enterprise’s system-of-systems environment – with appropriate security for data andcommunications – to evaluate the impacts of technical and business decisions on existing products. Thiswill provide life-cycle cost savings for product support through streamlined maintenance and coordinatedtechnology management. Product and process models will also exchange information with “gatekeeper”models supporting the enterprise’s resource management and strategic management functions. This willallow users to evaluate the broader impact of decisions on other enterprise operations.

Cross-Cutting Modeling & Simulation Needs for Product Realization

Before addressing specific needs for the functional elements of Product Realization, there are a number ofcapabilities that must be developed to support the broader vision of the model-based enterprise. Con-tinuing advances in computing speed and capacity, for example, are vital to enabling fast – ultimatelyreal-time – running of analytical codes and generation of model outputs such as complex visualizations.Application interoperability issues, particularly for product definition, must also be resolved by the ven-dor community. Information security must also be assured to protect the sensitive data of enterprise part-ners and organizations, particularly those involved in national security programs.

While these general improvements in underlying technology are not specifically addressed by the Next-Generation Manufacturing Technology Initiative (NGMTI), there are a number of goals and requirementsthat cross-cut and support multiple product realization functions. These are listed below and included inthe roadmap presented in Section 2.4.

• Goal 1: Flexible Representation of Complex Models – Provide product and process modeling tech-nologies that enable capture and representation of all realization and support attributes in a compre-hensive, computer-based model that conveys a complete understanding of the model’s purpose. Themodel will enable real-time selectable, customizable views by different types of users or applications,and be reconfigurable to accommodate new business rules, new functionality, or changing technology.(L)11

– Full Model Representation – Develop technologies and standards enabling creation of a complete,mathematically accurate model that allows all enterprise systems and modeling tools to interact withit through standard interfaces. The resulting models must be able to completely capture and commu-nicate customer requirements, design intent, physical and nonphysical attributes and their relation-ships, and functional performance, and include parametric feature definition for design and manu-facturing. Include the capability to accommodate changes in business rules and track different ver-sions of the product/process model over time. (M-L)

– Multi-Model Federation – Develop techniques and standards that enable complex models to bequickly assembled by integrating physical representation models with material, process, quality certi-fication, and other supporting models to yield a complete, federated model of a product or process.(S-M)

– Automated Abstraction – Develop techniques for automated generation of specialized “views” ofmodels at desired levels of detail for different enterprise functions (e.g., technical review, cost analy-sis, project planning) for any production-related application or decision process. Include the capa-bility to quickly and automatically expand, collapse, or de-feature the model to provide the correctdata and detail required for a particular application or use. (M)

– Graphical Representation – Develop techniques to provide a graphic visual representation of aproduct or process in order to facilitate a full and complete understanding of the product or processand its attributes, throughout the life cycle, specific to the needs of an individual user or function.(M)

11 The S-M-L designations identify a nominal timeframe for delivery of the specified capability, where S (Short) = 0 to 3 years,

M (Medium) = 3 to 5 years), and L (Long) = 5 to 10 years.



– Multi-Sensory Representation – Develop interface methods and representation standards enablingmodels and simulation environments to incorporate tactile, sound, smell, and other useful sensoryattributes of a product or process in order to represent customer requirements and allow more accu-rate representation of the modeled reality in the virtual realm. (M-L)

– Model Version Archive – Provide a system to manage different versions of the product/processmodels as changes occur over time, with linkage to the changing business rules, requirements, ortechnologies that led to the different versions. (M-L)

• Goal 2: Plug & Play Collaborative Modeling & Simulation Environment – Provide a standards-based and easy-to-use collaboration environment to optimize the value chain by supporting convenient,inexpensive integration of complex product models using components and designs from multiple valuechain members, where any model is interoperable and plug-compatible with any other model and withany standards-compliant application. (M)

– Product/Process Model Integration Standards – Develop standards for integrating material mod-els, manufacturing process models, business process models, and product models into a collaborativeenterprise M&S environment with interfaces that are intuitive enough to use without extensivetraining. The approach should accommodate capture of product and process performance require-ments; a method to locate available models and supporting data; methods for identifying and resolv-ing gaps and conflicts; methods to create federated models; and methods to share information amongthe models without compromising data integrity or information security. (S-M)

– Plug-and-Play Vendor Models – Develop standards and protocols that enable vendors to supplyplug-and-play product and process models for purchased parts, components, and equipment that canbe quickly and transparently integrated into larger product and process models. (S-M)

– Collaborative Analysis Systems – Develop a framework for integrating current and future analyti-cal tools into engineering and business management workgroup applications to provide a collabora-tive simulation environment with decision support tools for performing technical and business trade-offs. (M)

• Goal 3: Affordable Shared Model Libraries – Establish an industry-wide network of shared librariescontaining validated, well-characterized models that support plug-and-play simulation, proprietarytailoring, and optimization of designs for products, processes, and operations. (M)

– Framework for Model Library – Develop a broad-based framework to provide validated, interop-erable models that support multiple enterprise applications (design, manufacturing, product support,etc.). Establish standards for secure, shared access and for validation and characterization of modelsprior to release to the library. (S)

– Model Library Management Approach – Develop a methodology for populating, updating, main-taining, extending, and ensuring the data quality/security of the shared model libraries, including theuser interface and support tools. (S)

– Science-Based Materials Model Repository – Establish an industry-wide shared repository of vali-dated, well-characterized models and simulations for materials database to support product and proc-ess modeling and analytical simulation. Define and establish linkages to certified/certifiable indus-try, academic, and government sources to populate and update the database. (M)

– Validated Process & Equipment Model Repository – Establish an industry-wide shared repositoryof validated, well-characterized models and simulations for processes and equipment based on in-dustry priorities and value to multiple industry sectors. (M)



– Process Labor Standards Knowledge Base – Develop and establish a database of labor standards(i.e., time standards and skill/certification requirements) for all direct and indirect manufacturingprocesses and functions that interface to process design, simulation, planning, and resource manage-ment systems. (S)

• Goal 4: System-of-Systems Modeling Capability – Provide integrated system-of-systems modelingcapabilities to guide and improve product and process design decision-making for different producttypes and manufacturing sectors. (M-L)

– System-Level Product Modeling – Develop system-level modeling capabilities for classes of prod-ucts and processes, whereby a comprehensive decomposable product model captures or links to alltypes and levels of information needed to support all aspects of development. (M)

– Intelligent, Hierarchical, Composable, Shareable Models – Establish interface standards that in-telligently support creation of complex models at successive levels of detail, enabling integration ofindividual product models into system-of-systems models that support deep understanding of inter-dependencies and interactions. Initially focus on enabling integration within related product familiesfor one selected manufacturing sector. (M)

– Scaleable System-of-Systems Simulation Architecture – Implement scaleable architectures for aproduct/system/business M&S environment supporting thousands of component elements and doz-ens of modeling and analysis applications. Develop standards for supporting scaleable architecturesand for interfacing architectural components and tools. Evaluate existing standards efforts in ex-ploring approaches to this requirement.12 (M-L)

– Secure System-of-Systems Data Management – Develop capabilities for compartmentalization,security, risk assessment, and long-term management of data to support system-of-systems modelingthat integrates information from multiple sources having different security constraints, levels offunctional detail available, and levels of risk and uncertainty. (S)

– Self-Completing Models – Develop the capability to create models that know their own attributesand can interact with other model objects to complete a resulting superset of attributes, relationships,and behaviors. (L)

• Goal 5: Intelligent Models & Modeling Environments – Develop intelligent modeling capabilities toautomate and accelerate labor-intensive modeling tasks and reduce the need for human intervention inmodeling processes, enabling M&S functions to be automatically invoked at required stages as a prod-uct or process evolves from conception to production. (M-L)

– Common Modeling Semantics – Develop a standard, industry-wide terminology for representationof different model features and attributes, whereby a common and complete understanding is con-veyed regardless of context, and like features can seamlessly transfer from one domain, model, orlevel of abstraction to another. (S)

– Automatic Model Conversion – Extend current CAD applications to automatically generate the in-put required (e.g., mesh or flat file or model subset) for specific analytical tools. Prioritize desiredtool compatibility across industry sectors and work with CAD vendors to accomplish the needed ex-tensions and provide real/near-real-time processing capability. (M)

12 Standards to be evaluated include the Object Management Group’s Model Driven Architecture, the ISO Reference Model for Open Distributed

Processing, the DoD High Level Architecture, and the ISA 95 Enterprise Control System Integration standards being developed by the Instru-mentation, Systems and Automation Society.



– Adaptively Detailed Models – Provide models that can adaptively offer de-featured abstractions ormore detailed representations and datasets to suit the requesting function, providing a version readyfor use or analysis by that function. (M-L)

– Automated Requirements Linking – Develop methods and approaches enabling product and proc-ess models to automatically search for requirements that impact their domain or function (e.g.,safety, health, and other regulatory requirements) and interact with the model owner to ensure theserequirements are addressed as the model is developed. (M)

– Self-Composing Models – Develop methods enabling models to automatically search for, acquire,and integrate existing information and “sub-models” needed to complete their intended design. In-clude the capability to automatically populate manufacturing process simulations with equipmentmodels, material models, etc.; and the capability for product models to automatically extend them-selves with material models, part/component models, and similar assets available from the model li-braries accessible to the enterprise. Include the capability for models to mine for information aboutproducts, processes, and systems with which the product or process will interact in operational use.(M-L)

– Model Response to Factory Feedback – Develop analysis techniques that enable product and proc-ess models to “learn” and modify themselves when feedback from manufacturing processes andproduct performance data indicate faults in existing product/process designs. Include the ability todifferentiate between substantive differences (requiring model modification or adjustment of processequipment) versus normal deviations within acceptable tolerances, and record the deviations appro-priately as part of the product’s batch history or unique configuration record. Include analysis con-cerning level of backup documentation needed to demonstrate appropriate response to any off-normal feedback (and thus address liability protections). (M-L)

– Self-Monitoring Product & Process Models – Develop tools and methods that enable product andprocess models to monitor the enterprise knowledge base and respond appropriately (e.g., propagatea change or issue an alert) whenever the data underlying the model – or the requirements the modelis intended to fulfill – change. (M-L)

• Goal 6: Enterprise-Wide Product/Process Cost Modeling – Provide cost modeling systems andtechniques that integrate all required data, from within and external to the enterprise, to support high-fidelity analysis of development costs, production costs, life-cycle support costs, profitability, financialrisk, and other cost attributes of a product, process, or operation. (L)

– Integrated Cost Modeling Application Architecture – Develop a cost modeling application struc-ture that provides for capture and linking of all sources of cost – acquisition, nonrecurring design anddevelopment, engineering changes, recurring production, product ownership and support, retirement,regulatory factors, etc. – into product, process, and operations models. Include the capability to in-terface with applications and business systems to support real-time decision making in all phases ofproduct, design, manufacture, and support. (M-L)

– Common Cost Model Templates – Develop and validate a series of cost model templates that iden-tify the major cost elements for common product and part families, materials and manufacturingprocesses, life-cycle support processes, business operations, and other sources of cost in differentbusiness sectors (e.g., aerospace, automotive, chemical). (S)

– Product/Process Family Cost Models – Develop suites of generic, PDM system-compatible pro-duction cost models for common product and process types. Include the capability to automaticallytailor a generic product or process cost model to include additional features or attributes included ina specific design. (M-L)



– Unified User Interface for Cost Modeling – Develop easy-to-use interfaces that enable differentusers (engineers, estimators, etc.) to generate and apply accurate, comprehensive cost models for anyenterprise function. (M)

– Actual Cost Capture – Develop mechanisms for capture of actuals for all costs (recurring, non-recurring, direct, and indirect) associated with product manufacture and other enterprise operations,and feed this information back to PDM and financial management systems to refine cost model fi-delity. (S-M)

– Model-Based Estimating – Develop costing tools that decompose product and process models andinterface with the enterprise’s cost history knowledge base and financial systems to automaticallygenerate bases of estimate (BOEs) for development and production based on programmatic require-ments and historical costs for similar products, labor rates, supplier quotes, intellectual property li-censing fees, and current rates and factors. Include the capability to automatically score BOEs forconfidence level and flag areas of risk or uncertainty for management/engineering attention. (L)

– Automated Cost Modeling – Develop cost modeling applications that automatically generate billsof material from the product or process model; calculate and communicate the effects of a change inone parameter across the entire cost model; and perform dynamic updates (with appropriate alertsand approvals) from enterprise data sources to ensure currency. (M)

– Integrated Life-Cycle Cost Modeling – Develop methods for integrating life-cycle considerationssuch as maintenance, repair, sparing, customer service and support, recycling, and disposal, includ-ing these future costs into product, process, and operations cost models. (M)

– Integrated Supply Chain Cost Modeling – Develop and unify product modeling standards andtechniques to enable seamless, automated interfacing/integration of product and process cost modelsamong partners and suppliers, with provision for protection of sensitive data (e.g., rates and factors).(M)

– Cost Sensitivity & Uncertainty Modeling – Develop analytical applications and information elici-tation methods that use probabilistic, statistical, and other mathematical analysis tools to calculatecost sensitivities and quantify uncertainties for any aspect of recurring or nonrecurring cost. Providethe capability to link uncertainty models directly to product, process models, and operations to en-able automatic updating of impacts and risk factors in response to changes. (M-L)

• Goal 7: Model-Based Life-Cycle Configuration Management – Provide a model-based configura-tion management capability supporting product evolution from requirements, design, and manufactur-ing to operation, maintenance, and end-of-life disposition. Include capability to manage and properlyassociate all data, information, and knowledge related to the life-cycle processes of a product and de-liver appropriate views of the information to business functions that need it. (M-L)

– Model-Based Configuration Management Frameworks – For common types of products, developgeneric life-cycle frameworks that support automated prompting, generation, and distribution ofvarious models at the appropriate point in the development process. Include the capability to alertresponsible functions/personnel when a particular model requires creation, and to provide an initialmodel shell and data requirements definition that give users the most complete possible starting pointfor the required work. (S-M)

– Automated Change Management – Develop a process and notification/authorization scheme forreviewing, approving, documenting, and communicating changes in configuration-controlled modelsto all affected functions and individuals, including customers and suppliers. (M)

– Automated Change Propagation – Develop the capability to automatically ripple the effects of anyone change to a product or process model to all other product and process models (e.g., tooling,



training, maintenance documentation) that are affected by the change. Include the capability toautomatically update associated analyses, cost estimates, bills of material, purchase orders, and otherdependent information and feed the changes to the change management system for dissemination.(M-L)

– Remote Product Upgrades – Provide mechanisms for some classes of product to be modeled, pro-duced, and shipped with unexpressed features (potential future product upgrades) that can then beactivated either remotely or with minimal service when the new features are ready for implementa-tion. Include the capability for product models to manage and reflect the changes to products in thefield, and to accurately reflect the impact of the changes to product life-cycle cost. Such changesmight include disposition of components at the end of product life, or reuse of components for otherpurposes. (M-L)

– Enduring Data Storage – Develop the capability to preserve the accessibility and integrity of ar-chived electronic definition records and associated data throughout and beyond the life of the prod-uct, process, or facility. Include the capability to automatically verify the accuracy of retrieved flatfiles and models generated in applications or versions no longer in use; and establish industry stan-dards for ensuring backward compatibility of product definition applications. (M)

2.3.1 VISION & GOALS FOR INNOVATION & CONCEPTUALIZATION

Future enterprise members exploring concepts for a product will interact in a high-fidelity simulationenvironment to evaluate problems and solution options, define product goals and objectives, under-stand the impacts of choices, and create robust design concepts with complete confidence in technicaland business performance.

In the future, modeling systems will assist design teams and customers in defining problems and trans-lating them into opportunities; in exploring the bounds of what is possible; in rapidly defining and evalu-ating the merits of different options in terms of dollars, time, and performance; and in reaching agreementon the best ways to translate innovative ideas into reality.

Customer preferences and objectives – captured through direct interaction with customers as well asthrough market analysis – will drive the definition of requirements that represent the best balance of tech-nical and business performance attributes. The conceptualization process will be accomplished using amodel-based application environment where the team has ready access to visualization and analyticaltools to assess the impact of their choices in near-real time; and which provides intelligent decision sup-port to ensure that both the products and the processes used to create them are the most effective and effi-cient possible.

This environment will support continuous update of the product and process baseline, optimization of de-sign features and parameters, communication with stakeholders, characterization of uncertainties, andinterpretation of preferences in definition of requirements. The result will be a product concept that allparties agree can be designed, built, delivered, and supported within the defined cost and schedule andwith clearly defined levels of risk.

Rapid exploration of many product and process design options, coupled with a rich toolbox of analyticalsimulation tools, will greatly shorten product development time by reducing – ultimately eliminating – theneed for physical prototyping for all but the most safety-critical applications.

To put this capability into a use scenario, product commissioning in the future may begin with the cus-tomer and designers immersed with the product in a VR environment where product options can be se-lected and evaluated with full visual and tactile feedback. The users will receive a complete and accurateaccount of the result of their selections in terms of product performance, cost, and delivery timeline. Theaccumulated selections will be captured to modify the baseline product model and launch the manufac-turing order (or launch subsequent detailed engineering activities for developmental products).



Goals & Requirements for Innovation & Conceptualization

• Goal 1: Interactive Concept Definition Tools – Provide interactive visualization/representation andanalytical tools that quickly guide the exploration of a product concept to satisfy relevant objectives(performance, cost, schedule, etc.) for all participants in the enterprise value chain. (M)

– Natural Interface for Concept Definition – Provide interactive M&S capabilities that allow hu-mans to interact naturally and directly with design systems in exploring potential solutions to cus-tomer problems and market opportunities. Include the capability to define and evaluate alternatives,make optimal choices, and create initial product models – automatically, to the level of detail neededto support subsequent design and engineering – sufficient to gain concurrence on the preferred solu-tion. (M)

– Real-Time Decision Support for Conceptual Design – Provide decision support tools that interfacewith CAD/PDM-based design tools to define tradeoff opportunities, bound trade spaces, aid the de-signer in selection of best solutions, and automatically document the results of tradeoff analyses. (M)

– Conceptual Performance Modeling – For selected classes and types of products, develop perform-ance modeling applications that monitor the conceptual design to provide an on-screen estimate ofperformance, and advise the user on tradeoffs that may enhance performance (e.g., lighter weight forbetter energy efficiency, or heavier-duty construction for longer life). (M)

– Conceptual Modeling for Producibility & Affordability – Develop a general-purpose modelingsystem that interfaces with the conceptual design system to support producibility and affordability(and other “ilities”) tradeoffs for major product families (e.g., mechanical and electrical, structures,chemical products). Incorporate science-based material models to support requirements analysis anddecision-making for product engineering and manufacturing planning. (M)

• Goal 2: Immersive Product Conceptualization Environment – Provide integrated M&S tools thatallow rapid creative exploration of technical and business options, and provide the customers with richvisual/sensory feedback and real-time analytical support to assess the impact of their choices on prod-uct performance, cost, and other attributes. (L)

– Immersive Conceptualization Interface – Develop tools for real-time, intuitive interaction in im-mersive visualization environments that enable users to conceptualize products without requiring ex-pert skills in the modeling tools. (M-L)

– Virtual Environment for Prototyping – Develop visual prototyping tools that allow the user to in-teract with and manipulate the conceptual product in a physically accurate simulation environmentwhile assuring the technical integrity of the resulting product concept. Include the capability to notonly manipulate the design, but to exercise the conceptual product in a virtual usage environment.(M-L)

– Automated Featuring – Provide the capability to automatically recommend and integrate specificproduct features based on captured knowledge of features 1) already available in similar products, 2)being developed elsewhere in the enterprise or by its partners, or 3) being produced by the enter-prise’s competitors. (M)

– Automated Requirements Generation – Provide the capability to automatically generate anddocument (and update) initial product design and manufacturing requirements directly from the con-ceptual product or process model. (S)



• Goal 3: Comprehensive Factors Integration – Provide the capability to integrate all aspects of prod-uct preference in the concept definition process, including business and operational factors beyond theimmediate visibility of users – including supportability, manufacturability, risk/uncertainty, and safetyand environmental issues. (M-L)

– Manufacturability Assessment – Develop conceptual design evaluation tools that interface with theenterprise’s process and facilities knowledge base to assess the manufacturability of the product con-cept and provide feedback on options for improvement in terms of feasibility, cost, quality, and otherfactors. (L)

– Risk Assessment – Develop and integrate tools for automated assessment of technical, cost, andschedule risk into the conceptual design environment. (M)

– Safety & Environmental Assessment – Develop automated tools for assessing safety and environ-mental compliance attributes (and associated liability issues) of the conceptual design and advisingthe design team in resolving potential compliance issues. (M)

– Supportability Assessment – Develop automated tools for assessing the support attributes of theconceptual design, including reliability, maintainability, and logistics support factors (e.g., compati-bility with existing product support infrastructures for similar products). (M-L)

2.3.2 VISION & GOALS FOR PRODUCT & PROCESS DEVELOPMENT

The initial conceptual model of a product will be rapidly broadened and deepened into a high-fidelity,mathematically accurate representation that contains or links to all data required to drive developmentand subsequent manufacture, use, and support of the product. Materials and process plans will beautomatically optimized according to business priorities, capabilities, and internal and external re-sources, with intelligent advisors guiding designers to arrive at the best balance of issues such as per-formance, robustness, environmental concerns, and cost.

In the future, product models will no longer be simple physical representations coupled to a database ofdimensions and other physical attributes. Instead, the product model will be a complete virtual productcontaining or linking to all information related to its design, manufacture, performance, use, and life-cyclesupport.

Product data will be man-aged using a hierarchicalstructure that enablesautomated generation ofmodels for any purposefrom the master productmodel. The productmodel will possess (viaembedding or linking)sufficient information todrive all analytical appli-cations (Figure 2.3.2-1)and manufacturing proc-esses. Further, it will sup-port the ability to createcustom abstractions forspecific analyses. Design-ers will be able to call upcustom views of any

Figure 2.3.2-1. Analytical applications will be integrated through a desktopinterface that enables users to take full advantage of capabilities hosted

anywhere in the enterprise supply chain.



product or process information to any level ofdetail to support development tasks and businessdecision processes.

New interface methods will enable designers tocreate and manipulate models in new ways.Aural interfaces will allow users to launch tasksand commands via verbal input, and VR systemswith tactile feedback mechanisms will let de-signers shape their concepts with their hands in-stead of a keyboard and mouse.

Smart Models

Future models will “plug-and-play” via self-describing interfaces. Every product and processmodel will understand its own behavior, its owninput needs, and its own output capabilities, suchthat when a new element is added to the emerg-ing product or process design, it will negotiatewith the models of all other elements to “fit in”without human assistance.

Self-analytical functions will help models vali-date themselves automatically. Specification ofa particular material of construction, for exam-ple, will prompt the product model to automati-cally search the enterprise knowledge base andcall up the properties/physics model of the sub-ject material. The model will verify the selectedmaterial against the defined performance re-quirements and alert the designer to any potentialproblems, offering alternatives for better per-formance or reduced cost based on the designguidelines. Validated models of standard mate-rials and components will be shared across industry. Such models will also be a required deliverable ofany contract, facilitating use by all members of the product’s supply chain.

Future models will accurately predict how a product will behave in its operating environment, and how itwill react to external events and changing conditions. This will enable evaluation and optimization ofreliability, maintainability, safety, environmental impact, and other life-cycle factors.

From Design to Production

Automatically generated manufacturing process plans – updated as the product design evolves, with directlinkages to enterprise capacity and resource models – will enable proactive resource planning and alloca-tion. Advisory tools interfaced to the enterprise knowledge base will guide managers in optimizing ca-pacity requirements and utilization during the early process planning and manufacturing execution stages,shortening the time required to ramp up to sustained production rates with six-sigma quality in every unit.

Model-based product realization systems will eliminate all but mandatory physical testing, greatly reduc-ing the time and cost of moving products from concept to production. While product testing will not dis-appear, it will eventually be used only where physical validation is specifically required (e.g., for safetycertification). In those cases, the model will capture the results and augment the value of physical testingby using the results to deepen the science basis underlying the contributing models.



Linkage of the product and process development environment to the enterprise knowledge base – and theenterprise’s business systems – will enable continuous, accurate visibility into the cost impact of designdecisions. All forms of cost – acquisition, nonrecurring development, engineering changes, recurringproduction, and product ownership and support – will be thoroughly understood and always available as areal-time input for decision-making in all phases of the product life cycle. Costs throughout a supplychain will be integrated using cost models standardized for particular industries, with appropriate protec-tion of proprietary information. The ability to certify the enterprise’s model-based estimating process willgreatly reduce the time required to generate quotes, freeing engineers to focus their talents on the designprocess. This will also give customers clear visibility into acquisition and life-cycle costs as a productmoves through development, enabling fast identification and response to potential overruns.

Goals & Requirements for Product & Process Development

• Goal 1: Automated Comprehensive Product & Process Design – Provide the capability to createand optimize a complete product and process definition containing or linking to all related specifica-tions, requirements, analytical results, and other pertinent information. (L)

– Common Product & Process Specification Standards – For different industry sectors and producttypes, develop standards for defining product and process specifications that can be accessed by thedesign system and which are consistent with the parameters, attributes, and features on which prod-uct and process designs in each sector are based. (S)

– Design Knowledge Base – Develop a knowledge base of certified materials, commercial compo-nents, and manufacturing process and equipment data and models that is accessible and directly use-able by human designers and automated design tools. (S-M)

– Sector-Specific Design Knowledge Bases – Extend the basic design knowledge base with sector-specific information and knowledge to support the unique needs of different industry sectors. In-clude appropriate provisions for security and control of proprietary, export controlled, and classifieddata consistent with applicable regulations13. (M-L)

– Unified Performance Evaluation Applications – Assess existing performance evaluation applica-tions (i.e., analytical tools) and develop a framework for integrating those applications into a unifieddevelopment environment for specific classes of products and processes. Conduct a gap analysis todefine the extensions required for various tools to support the integrated environment and identifywhat new analytical capabilities need to be developed, then initiate development of the missing ca-pabilities. (M-L)

– Rapid, Science-Based Product/Process Design Optimization – Develop the capability to auto-matically create a complete and unambiguous, computer-sensible product and process definition thatincludes all underlying technical information needed to manufacture the product (e.g., material prop-erties, boundary conditions, transformation physics, tolerances, loads, constraints). Develop M&Sapplications to provide rapid exploration, evaluation, and selection of best options in product andprocess design. Include considerations such as structural performance, ability to create a part with asingle pass, available production equipment capacity, and ease, speed, reliability, and cost of assem-bly as well as future disassembly/reassembly for downstream maintenance. Include the capability toautomatically repair or flag features that require nonstandard tools, fixtures, or assembly aids. (M-L)

– Material & Process Advisors: Create knowledge-based process advisors for individual materialsand manufacturing processes to support a variety of design and engineering functions, including thecapability to validate and verify designs. Develop a methodology to capture the requisite knowledge

13 National security requirements expressly prohibit the connection of classified processing systems to externally accessible networks such as the

Internet or corporate intranets. Solutions for shared design information must therefore accommodate importation of stand-alone knowledgebases into classified environments via removable media (e.g., DVD).



and develop rule sets for existing materials and manufacturing processes based on published hand-books and other standard industry references. (M-L)

– Material Transformation Model Repository – Establish an industry-wide shared repository ofstandardized, validated transformation models and analytical tools to support design, optimization,and troubleshooting of transformation processes. (M)

– Model-Based Material Transformation – Develop applications to design transformation processesfor the best result from a scientific understanding of the interactions involved, enabling processing inways that deliver compliant product with minimum waste. (M-L)

– High-Fidelity Multi-Process Analysis – Develop a suite of analytical applications that accuratelypredict the results (including time and cost components and environmental considerations) of differ-ent manufacturing processes and materials for a given design of a component, part, or formulation.(M-L)

– Manufacturing Capability Interface – Develop product model interface mechanisms that providereal-time access to information that affects producibility and production cost, including mate-rial/commodity/supplier availability as well as enterprise manufacturing capability and capacity. (M)

– Integral Packaging Design – Extend product modeling systems to include packaging issues as anintegral design factor contributing to minimum product cost and assuring product protection andcompatibility with handling and transport systems. (S)

• Goal 2: Multi-Scale Material Modeling – Develop material modeling and analysis technologies ena-bling scaling of properties and behaviors from micro (e.g., molecular) to macro (product) levels tosupport creation of robust product models that accurately reflect the influence of real-world materialproperties. (L)

– Molecular Material Modeling: For high-priority classes of materials, develop molecular modelingtools able to provide accurate multi-physics modeling, enhance understanding of material properties,and capture, in computer-sensible form, the relationship of molecular composition and distribution tomaterial variability. (S-M)

– Integrated Material Modeling – Develop interfaces from product and process modeling systems tomulti-physics material models and knowledge bases so that the properties and behaviors of the prod-uct and process design accurately reflect the properties of the materials used. (M-L)

– Multi-Scale Material Modeling – Develop capabilities to predict macro-level process behaviors re-sulting from microstructural material attributes, and to address requirements on microstructure to at-tain desired macro properties both in-process and in the finished product. (L)

– Multi-Scale Process Modeling – Identify high-priority needs for micro-scale material models tosupport high-fidelity, multi-physics process modeling and simulation. Initiate the development ofmaterial behavior models for critical micro-scale phenomena (such as grain growth and size frac-tions, dislocations, crystal structure) under different conditions. Provide modeling tools that managethe linkages and information exchange between levels. (L)

• Goal 3: Automated Process Planning – Provide the capability to automatically generate processplans for a quantity of product as the product is being designed, consistent with product attributes,processing capabilities, enterprise and supply chain resources, and enterprise strategic direction. (L)

– Process & Resource Capability Models – Develop tools and techniques for creating process andresource models that capture complete descriptions of enterprise manufacturing resources and proc-ess capabilities, enabling plug-and-play integration of resource/capability models through every levelof the supply chain. (S)



– Process Model Repository – Define required process model attributes and standard formats thatsupport automated process planning and manufacturing execution, and establish a shared repositoryof well-characterized models of common processes for use in different industry sectors. Assess gapsin the process set to define high-priority model needs for critical processes, and initiate developmentof the required models. (S)

– Multi-Level Interoperable Process Models – Develop capabilities and standards for integration ofmulti-element process models at the unit process, line, shop floor, factory, and enterprise levels. (M)

– Automatic Process Requirements Extraction – Develop the capability to extract process require-ments from higher-level process models in order to evaluate applicability for specific purposes (e.g.,to assess the ability of an existing process to make a new product). (M)

– Planning System Interface to Factory Floor – Develop analysis and planning tools that enable op-timization of process plans within the constraints of product requirements. Create interfaces betweenprocess planning systems, manufacturing execution systems, and management information systemsto guide decision-making for scale-up. Include the capability to optimize manufacturing plans withconsiderations including workflow, throughput, processing time scaleability (compress or expandtime), manpower/skills requirements, and product mix. (M)

– Robust Multi-Step Process Planning – Develop the capability to perform “what-if” and sensitivityanalyses for optimization of multi-step processes and to automatically generate simulations and op-erational control models for process control and verification. Include the capability to characterizeprocess robustness to allow innovation or variation in a complex process. (M-L)

– Technology Insertion Modeling – Develop modeling tools to create structured plans for deploy-ment of process technology advances across the life cycle of a production line or facility, supportinginsertion of new equipment or capabilities to extend process life or meet future requirements for ex-panded capability or capacity or shifts to different products. Include the capability to interface withfinancial modeling and analysis applications to evaluate issues related to capital expenditure and re-turn on investment. (M)

– Process Planning Direct from Product/Process Design – Develop generative planning systemsthat operate directly from product and process definitions to provide all information needed to driveproduct manufacture. Include the capability to incorporate material and unit process models throughinterfaces to enterprise product/process/material model libraries and knowledge bases. (L)

– Executable Product Models – Develop the capability to integrate process knowledge into productmodels sufficient for the product model to provide all information necessary to execute a manufac-turing process, including material flow, actuator commands, assembly steps, and inspection. (M)

• Goal 4: Tools to Manage Development Uncertainty & Risk – Develop modeling and simulation aidsthat enable effective management of risk, uncertainty, and sensitivities in product and process devel-opment. (M-L)

– Uncertainty Bounding Techniques – Develop mechanisms, techniques, and protocols for identify-ing, quantifying, and providing adequate margins for uncertainty and risk in complex product andprocess models. (M)

– Robustness Evaluation – Develop performance modeling systems that determine the sensitivities ofthe design, quantify uncertainties, and define the robustness of product solutions. (M)

– Automated Risk Scoring – Develop a modeling utility that automatically assesses a proposed prod-uct or process design and scores it for technical, schedule, and business risk based on technologymaturity (e.g., technology readiness level) and design uncertainties; and creates a prioritized charac-terization of the detected risks. (M)



– Multi-Scale Uncertainty Management – Create methodologies for accounting for and tracking theuncertainties associated with materials, component designs, subsystem designs, and other develop-mental elements in system-level product and process models. (M)

– Product/Process Risk Mitigation Tools – Develop modeling tools that capture risk items from theproduct/process design and aid in development and monitoring of mitigation actions. Include busi-ness-case templates to quantify the benefits (cost savings, performance, and life-cycle advantages) ofa high-risk product or process element to support management decisions. (M)

– Automated Risk Minimization – Develop a modeling utility that draws on product and process ex-perience captured in the enterprise knowledge base (or shared knowledge bases maintained by in-dustry sectors) to recommend lower-risk alternatives for risk elements identified in a product orprocess design. (M-L)

• Goal 5: Model-Based Product Assurance & Manufacturing Process Validation – Develop method-ologies and standards for virtual testing methods that use simulations in lieu of physical testing inproduct and process development, over time eliminating all but mandatory physical tests. (L)

– Model-Based Quality Assurance – Develop and promulgate quality assurance standards, for differ-ent industry sectors, for verifying product and process models and certifying analytical simulationtools within clear bounds of confidence for specific applications. (M-L)

– Self-Validating Designs – Develop simulation and analysis tools that verify component, subsystem,and system designs against requirements and quality assurance criteria, reducing or eliminating theneed for iterative physical testing in product and process development. (M-L)

– Model-Based Manufacturing Process Validation – Develop sensor-based in-process verificationtechniques sufficient for automatic certification of manufacturing process results based on measuredconformance of process execution against the approved control model, independent of the platform(legacy or current equipment) used to produce the product. (M-L)

– Model-Based Certification of Production Readiness – Develop the capability to test and validatemodels of product designs and their simulated manufacturing processes with sufficient fidelity andaccuracy that production readiness can be certified by simulation. (L)

2.3.3 VISION & GOALS FOR MANUFACTURING EXECUTION

Future factory management systems will directly use product and process models to plan, visualize,and implement the operation, monitoring, and control of process execution. Interdependent processeswill be so deeply modeled and thoroughly integrated with control systems that they interact as a singleprocess, automatically adapting to real-time changes in requirements or the process environment. Thiswill dramatically increase the efficiency, responsiveness, safety, and quality of the entire operationwhile reducing all forms of waste and inefficiency.

Manufacturing execution in the future will benefit from well-understood, physics-based process andequipment models with materials characterization sufficient to eliminate the need for trial-and-error proc-ess development. Processes will intelligently accommodate material and part variability within well-defined limits of uncertainty. This will keep processes running continuously with minimal human inter-vention. It will also enable cost savings through use of variable-quality materials that the process systemscan accommodate without compromising product quality.

The ability to “pre-manufacture” products in the virtual realm will enable the production team to test andoptimize the production plan concurrent with product development. It will enable them to have all re-sources in place at production go-ahead, including equipment, materials, suppliers, and trained staff ready



to execute (Figure 2.3.3-1). It will also enable themanufacturing operation to quickly adjust tochanges in requirements and resource availability,thus eliminating bottlenecks and minimizingdowntime.

Manufacturing systems will self-configure to meetchanging needs based on automatic evaluation ofdefined requirements against operations models.Self-defining interfaces, leveraging standard proto-cols, will enable new process systems to be mod-eled and integrated very quickly in building-blockfashion, with new or customized software inte-grated as soon as code validation and verificationare complete.

Model-based manufacturing concepts will alsodrive fundamental changes in specific manufac-turing processes. Based on a scientific under-standing of materials, process interactions, performance criteria, and product models, material transfor-mation processes will be designed, modeled, and executed to synthesize and manufacture materials withrevolutionary mechanical, chemical, and electrical properties. This will support the evolution of net-shape processes to reduce, and in many cases eliminate, the need for material removal processes such asmilling and grinding. This will eliminate most forms of scrap and material waste, reduce energy demandsand environmental impacts, and reduce product costs. An order of magnitude or greater reduction in timeto produce parts will be achieved.

Better upstream processes will eliminate finishing processes as corrective actions. Finishing steps thatadd functionality will be engineered based on measurable, modifiable properties, and will be tailored toprovide the needed functionality.

Perhaps the most important new function in future manufacturing systems will be the use of real-timeprocessing information to correct and enhance product designs. With the tight integration of product andprocess design enabled by the model-based environment, product designers will have near real-time feed-back on manufacturing performance, and have the ability to quickly refine any aspects of the design thatare impacting production quality, cost, and throughput. Complemented by in-process sensing and me-trology and aubiquitous con-figuration man-agement system,this will provideaccurate captureof the as-builtproduct defini-tion to aid inlife-cycle sup-port and in de-sign of futureproduct en-hancements(Figure 2.3.3-2).

Figure 2.3.3-1. Advanced simulation techniques willenable process operators to train virtually, reducing

the time, cost, and risk of certification.

Figure 2.3.3-2. The ability to capture the characterization basis for every product will pro-vide a total knowledge base for life-cycle support and improved design of future products.



Model-Based Control

Future manufacturing process systems will be controlled and managed using a rich base of scientificallyaccurate and deeply characterized material, equipment, process, and product models. Manufacturingprocess parameters and control information will be downloaded directly from the product model and usedto drive, control, and monitor all manufacturing processes (Figure 2.3.3-3).

Process control systems willinterface bi-directionally –with the processes they con-trol and with the factorycontrol model. This model,equipped with robust defini-tions of process require-ments, characteristics, capa-bilities, constraints, and at-tributes, will use real-timefeedback from process sen-sors to tune operation forcontinuously optimized per-formance. Analytical appli-cations integrated into themonitoring and control sys-tem will provide deep in-sight into process perform-ance, enabling fast responseto problems.

Process models will acceptreal-time input from thefactory floor sensor net and dictate corrections based on captured knowledge of the physics of the proc-ess. For example, instead of issuing commands in the form of a stored program, a machine-specific proc-ess model will accept direct feedback of measured product attributes to detect and correct out-of-specvariations. The higher-level factory model will monitor the sensor net to continuously update status andlaunch analytical applications to accurately predict future operational performance and to forecast re-quirements for additional or modified resources.

The shop floor control systems of the future will not only integrate all the production-related informationflow to and from the shop floor, but will also collect, distribute, and report key information concerningshop operations. The current function of distributing processing instructions to machine/process control-lers will be expanded to include work instructions, operations simulations, assistance in resolving off-normal conditions, and other process-related information. Quality information and processing trends willbe continuously available and up-to-date.

The controllers will continuously monitor the health and availability of all process equipment, enablingproactive and just-in-time maintenance to maximize uptime and avoid process upsets. Process controllerswill be built using open, modular architectures that incorporate the level of sophistication required for agiven process, using seamlessly interoperable hardware and software components from a variety of ven-dors.

Material flow models coupled to autonomic material handling systems will route materials and work-inprocess from one operation to the next, removing humans from the loop especially in hazardous proc-esses. Products will be verified in-process as they move through each stage of manufacture, with processsensors continuously comparing actual to predicted to required performance.

Figure 2.3.3-3. Manufacturing process parameters and control informationwill be downloaded directly from the product/process model to drive and

control all manufacturing processes.



The enterprise knowledge base will maintain baseline signatures of all equipment for real-time compari-son to actual process performance. This will enable early detection and proactive correction of off-normal behavior. Continuous, real-time operating status of all equipment, material, and support assetswill be maintained and reported; reactive schedulers will re-route material and re-schedule equipment toadapt to breakdowns and other upsets in operations.

Goals & Requirements for Manufacturing Execution

• Goal 1: Product Model-Driven Manufacturing – Provide the capability for manufacturing controlsystems to execute manufacturing plans derived from the product model. (L)

– Automated Verification of Production Readiness – Develop the capability for product models toverify that all information required to execute manufacturing is complete and accurate based onknowledge available from the enterprise’s manufacturing information system. Include the capabilityto flag areas where information is missing or where the model cannot verify that information is cor-rect (e.g., that a supplier-provided item will deliver on time). (M-L)

– Complete Process Control Models – Develop techniques to incorporate process effector and sensordesign information and product-specific parameter data into a complete process model, to enable theintegrated product/process model to operate as a real-time process controller at the unit process, line,and shop floor levels. (M)

– Model-Based Direct Manufacturing – Develop transformation processes that synthesize and manu-facture materials to produce final parts directly from the product model, based on a scientific under-standing of materials, process interactions, and performance criteria. (L)

• Goal 2: Model-Based Intelligent Process Control – Develop intelligent, adaptive process controllersthat sense material and its geometric/chemical/physical properties in-process and dynamically adaptprocessing parameters to assure continuous production of certifiably correct product. (L)

– Adaptive, Real-Time Process/Equipment Control Models – Develop self-tuning process andequipment control models based on first principles, validated material and process knowledge bases,and continuous feedback of sensor test and inspection data. Include models for legacy equipment aswell as current-generation equipment. (M)

– Rapid Material, Part, & Process Characterization – Develop characterization technology ena-bling fast, accurate in-process assessment of material/part condition and characteristics (shape, com-position, distribution, viscosity, temperature, etc.) and associated processes, to support model-basedprocess monitoring and control. (L)

– Standardized Process Models – Develop robust, standardized definitions of processes such that theoutcome of successfully performing the process is certifiably the same, independent of the actualplatform (legacy or current equipment) that produced the outcome. (S-M)

– Sensors & Sensor Fusion for Process Monitoring – Develop non-intrusive sensors and sensor fu-sion technologies for current and legacy manufacturing equipment that enable the model-based proc-ess controller to recognize and quickly adjust to any in-process variances (e.g., tool wear and mate-rial variation), maintaining the quality of process output. Include the capability to evaluate sensordata to determine if readings from each sensor are credible based on inputs from other sensors, andto take appropriate action to maintain the health of the process if one or more sensors are not func-tioning correctly. (M-L)

– Self-Diagnostic Equipment Maintenance & Performance Monitoring – Develop techniques formonitoring equipment status against the validated equipment model, detecting and analyzing trendstoward out-of-spec performance, and automatically issuing commands/requests for maintenance. (M)



– Model-Based Failure Prediction & Recovery – Develop the technology and tools to accuratelymodel equipment and facility failure modes and effects, identifying predictive indicators for im-pending failures and process upsets. (M-L)

– Operational Feedback to Process Models – Develop the capability to monitor the shop floor andupdate process control models (for one or more unit operations, as appropriate) to adapt to changesin the processing environment, to continuously ensure the correctness of the product being produced.(M-L)

– Enterprise-Wide Control Capability – Provide the capability to support model-based control inmultiple-process, enterprise-wide (supply chain) applications. (L)

– Zero Finishing – Develop model-based process control schemes that eliminate the need for finishingsteps by dynamically managing product surfaces during operations such as forming, assembly, andblending. Include the capability to dynamically identify and recalculate mid-surface locations forthin shells. (L)

• Goal 3: In-Process Validation of Models – Provide means of matching feedback from physical proc-esses (e.g., via sensors, human interaction) against process models and performance metrics, andissuing alerts and requests for actions if the situation does not match expectations. (S-M)

– Mapping Feedback to Models – Provide means of continuously comparing sensor readings or in-strument/equipment measurements or human observations to the performance expectations definedby the controlling models, and taking appropriate action when measured values exceed specified tol-erances or indicate a negative trend. (S-M)

– Corrective Action & Alerting – Develop broadly applicable ground rules to guide system and userresponse to off-normal events, including requests for intervention, prioritization of intervention op-tions, verification of requested action, and issuance of higher-level alerts (and launching of fail-safeactions) to ensure the problem is contained and mitigated. (S-M)

• Goal 4: Zero Post-Process Certification – Establish robust, science-based manufacturing processcontrol models that eliminate the need for post-process inspection by integrating certification as anintegral part of individual manufacturing processes. (L)

– Quality Certification Models – Develop process certification models that enable elimination ofproduct certification as a separate process step through the application of model-based control usinginformation collected in-process. Prioritize processes of interest to different industry sectors and im-plement a phased program to deliver process-specific certification models. (M-L)

– Self-Integrating Measurement Systems – Develop self-calibrating, self-integrating measurementsystems that provide all needed metrology for specific processes based on process models that definethe inspection points for a given process and the inspection parameters and acceptance criteria for agiven product. (M-L)

– In-Process Intelligent Conformance Monitoring – Develop intelligent inspection and measure-ment systems that detect and respond to nonconformances in-process, ensuring continuous adherenceto requirements. Include the capability to automatically flag nonconforming product, route it out ofthe normal production flow, and recommend appropriate disposition (i.e., rework/repair or scrap) tospeed material review board processes. (L)



• Goal 5: Intelligent, Self-Configuring Manufacturing Execution Models – Provide intelligent, self-organizing manufacturing execution models able to integrate the product model with all applications,systems, equipment, and process instructions to form a complete logical representation of what is to beaccomplished, and to ensure readiness to satisfy all requirements for producing correct product. In-clude the capability to monitor execution and automatically adapt to off-normal occurrences orchanges in requirements. (L)

– Manufacturing Planning Model Templates – Develop a series of model-based templates, for ma-jor classes of products in different sectors, that can integrate “sub-models” of processing equipment,unit processes, line operations, and material flows to create an end-to-end model of a given manu-facturing process. (S)

– Manufacturing Execution Standards – Evaluate current standards from ISO, IEEE, SME, ASME,etc. that are relevant to model-based manufacturing, and lead standardization efforts where gaps existto provide increased openness and interoperability of product/process/business models among ven-dors and supply chain partners. (S)

– Generic Equipment Models – Develop generic equipment performance models for families ofmanufacturing equipment (e.g., injection molding machines, three-axis milling machines). (S)

– Equipment Characterization Models – Establish standards and requirements for integration of per-formance characterizations into existing or vendor-supplied models of process equipment (machinetools, valves, process sensors, material handling devices, etc.), including legacy equipment as well ascurrent models. (M)

– Machine-Specific Equipment Models – Develop tools to extend generic or vendor-supplied equip-ment performance models to reflect the as-installed configuration and use real-time sensor informa-tion to accurately report specific equipment system performance. Include the capability to capturethe current signatures for each production machine in its supporting baseline model. (M-L)

– Intelligent Manufacturing Execution Models – Develop methods to integrate needed componentmodels creating an intelligent manufacturing execution model that logically represents what is to beaccomplished. Provide the capability for the model to automatically update itself by recognizing andresponding to approved changes in underlying material/process/product models, or in response to in-formation from the shop floor control system. (L)

• Goal 6: Flexible, Reconfigurable Manufacturing Facility Model – Develop technologies and meth-ods for creating rapidly reconfigurable production lines and resource streams able to use capacity,demand, and unit process models to quickly adapt to changing product and process requirements. (M)

– Scaleable, Interoperable Process Models – Prioritize unit processes and manufacturing equipmentof interest and develop scaleable, interoperable process models capable of going from one to many,or from small to large, or accommodate a defined wide range of input material variability, quicklyand autonomously in response to changes in production demand. (M)

– Robust Model-Based Control – Develop extensions to current manufacturing process control mod-els to add the capability to adapt dynamically to changes in basic process parameters; to remain reli-able and robust within the defined operating envelope; and to automatically respond to failures orprocess upsets with the appropriate action. (M)



2.3.4 VISION & GOALS FOR LIFE-CYCLE SUPPORT

Models and simulations created in product and process development will be used directly in all phasesof the product life cycle, augmenting other modeling tools to provide “hands-on” support to users andsupport personnel. Enterprise value chain members will use real-world performance and mainte-nance/repair/failure data to evaluate products and processes in synthetic operational environments;model options for improvement; and implement changes to enhance reliability, maintainability, safety,ease of use, and other life-cycle attributes.

In the future, a master product life-cycle model linked to live sources of product information and the en-terprise knowledge base will guide all support activities throughout the product’s life (Figure 2.3.4-1).This model will capture the genealogy or “record of assembly” of the product at the instance level, so thatthere is a specific model for every serial-numbered part or product batch. With provisions to protect in-tellectual property, information in this model will be accessible to all manufacturing processes or productsupport functions. The model will reflect changes in the product over time – as it was originally pro-duced, and as it is affected by use throughout its life.

Support planners will use the master product model as a key tool in collaborating with customers andproduct support organizations to optimize requirements and approaches for maintenance, repair, training,and user support. The model will provide all desired types and levels of detail – including what supportassets are required, how many, when and where they will be needed, how they will be delivered, and whatmust accompany them. Support strategies will be based on comprehensive simulations of all aspects ofproduct use and accurate predictions of reliability at the system, subsystem, and component levels. Reli-ability calculations will use these simulations to take full account of the impacts and effects of operationalusage, including the impacts of misuse and sustained operation in extreme (e.g., arctic and desert) envi-ronments.

Figure 2.3.4-1. All product and process models will interact through the enterprise knowledge base, enablingfeedback across the product life cycle to benefit future products and enhance customer satisfaction.



Automated analytical tools will monitor the life-cycle attributes of the emerging product design and flagpotential problems for attention. For example, if a designer specifies a life-limited component containinga hazardous material, the system will flag both issues and draw on the enterprise knowledge base to sug-gest a different component or a design change to alleviate the reliability and safety impacts.

Process Life-Cycle Management

Just as model-based processes will enhance management of all stages of the life cycle for products, theywill benefit manufacturing processes in similar ways. As processes and their enabling equipment andsystems are developed, proven out, and modified over time as a result of technology changes, iterativeimprovements, or functional changes, the information in the process model will be updated to capture aclear version history along with supporting configuration and characterization data. The process “geneal-ogy” will be retained in the model so that the correct version is always readily accessible when needed tosupport the products produced using that version. This will be particularly valuable for products thatmust be supported over many years or even decades – allowing engineers to look back to any point intime to understand how a particular product was built and how the processes and equipment used to buildit contribute to its current condition and attributes.

Model-Based Monitoring of Product

The performance of delivered products will be tracked against their model predictions in order to con-tinuously enrich the knowledge base underlying the models. For many complex mechanical and elec-tronic products, embedded sensors will monitor the system’s condition and autonomously initiate correc-tive actions – e.g., issuing a maintenance alert or intelligently compensating for a detected fault. The sen-sors will compare prognostic and diagnostic results to system models and continually adjust for optimumperformance, including degraded modes of operation. For other types of products, the product life-cyclemanagement system will continuously monitor feedback from the enterprise’s customer service channels,and issue alerts to the product management team regarding potential design issues and opportunities forimprovement.

Continuously collected product performance data will also feed the enterprise business systems, providingtotal visibility of performance to support continuous improvement at the unit process, line, shop, factory,and enterprise levels. Variances from specifications and modeled performance will be automaticallyflagged for analysis and corrective action using model-based applications. This will enable potentialproblems to be detected and addressed very early in the life cycle, thus minimizing the impact of defectsrequiring product recalls and service actions. This will be of particular benefit in pharmaceuticals, foods,electronics, automotive, and other sectors where safety issues have major liability implications.

Designers will use this same information to optimize product improvements and future product designsfor better reliability and other performance attributes. Model-based tools also will enable designers torapidly configure and integrate product elements so that the parts or assemblies most likely to fail, or re-quiring specific maintenance attention, can be quickly and easily accessed using a minimum of specialtools. Designers will also use the model to determine the life-cycle impacts of proposed design changes,linking to the supply chain management system to rapidly get assessments of cost, schedule, and technicalimpact from their suppliers. Product models capturing usage history will provide clear visibility of allaspects of life-cycle performance to all members of the product support chain, enabling accurate identifi-cation and analysis of trends and efficient performance of support actions over the life of the product.

Model-Based Maintenance

Maintenance staff will likewise use the master product model to manage their activities. Repair techni-cians, for example, will be able to call up the product model on their desktop, heads-up display, or otherportable interface, quickly navigate to the area of interest, and bring up associated models, simulations,and instructions for fixing the problem. Analytical tools will enable technicians to troubleshoot complexproblems and quickly evaluate the merits of different solution approaches.



Support staff anywhere in the world will be able tocollaborate in real time with design staff back at theprime manufacturer and supplier sites, using themodel to point out problems and rapidly workthrough suggestions for corrective actions includingchanges to the design or to maintenance/repair pro-cedures. As maintenance, repair, and provisioningservices are performed, the support system will logall product activity and routinely compare actual topredicted performance.

Repair staff will also use the master model to ana-lyze and fix problems without having to go back tothe suppliers. This is critical where a supplier is outof business, or no longer supports the product. Us-ing the master model, repair staff will be able to callup a part design, modify it if needed, and thendownload the part model (with associated machineinstructions) to their in-house fabrication systems formanufacture.

Realizing these capabilities will require a significantchange in the present basis of competition in themanufacturing industry and will probably be feasibleat first only for large contractors in the defense,aerospace, and automotive sectors. Currently, prod-uct information at a detail level is proprietary andclosely held. Customers typically receive only basic documentation such as operation and maintenancemanuals. Achieving true model-based life-cycle support will require the supplier of a part or a product tomake all information about the product accessible in digital form, in ways that can be openly sharedacross the entire supply chain.

Model-Based Training

Future product and process models, created in the design phase, will provide the basis for all training ac-tivities across the product life cycle. Product models, for example, will link directly into training media tosupport specific training needs such as different views of parts and assemblies, and simulations that showhow to operate and maintain the product, how parts fit together, and how tools should be applied to per-form testing, servicing, removal, and replacement. Where communications capabilities (networks, cellu-lar, satellite) enable it, training assets will link directly to the master product and process models. Thiswill allow training content to be automatically updated (with appropriate alerts) whenever a configurationor a procedure changes.

Product users and support personnel will be able to log into the enterprise’s product support system andcall up any desired training on demand simply by clicking on the applicable piece of the model and se-lecting the desired training module. This will enable personnel in all domains to keep current with re-quirements for both new and refresher training. Evolving VR technologies will enable users to immersethemselves in simulated operational environments and receive highly realistic virtual hands-on training ondemand, including collaborative training with other users anywhere in the world. This will greatly reducethe need for special-purpose training simulators, and will enable generic simulators to be quickly and eas-ily customized for different uses.

Mobile Manufacturing Project Aims toKeep Trucks and Tanks Moving

Military vehicles are often located in a war zone orother difficult-to-reach locations, so getting repair partsin the field is extremely difficult. Using a “mobile partshospital” equipped with a rapid manufacturing system(RMS), repairs can now be made in the field usingdigital information to manufacture parts right on thespot or at a regional agile manufacturing cell.

The RMS retrieves part models and manufacturingdata via satellite from an extensive engineering andmanufacturing database. If data for a part is not avail-able, the RMS gathers its own geometric data using a3D laser scanning system. The scanned data can besent to an engineering CAD package and then to aCNC machining center or powdered material deposi-tion system for production.

The system, now in development, will enable the Armyto dramatically reduce the time and cost of gettingtrucks, tanks, and other vehicles back into servicewhile reducing the strain on logistic support pipelines.



Goals & Requirements for Life-Cycle Support

• Goal 1: Model-Driven Support Over Full Life Cycle – Provide the capability to manage all productlife-cycle support activities using model-based processes, by extending the product model includingtraceability through all configurations, adding life-cycle simulations, and capturing and applying in-formation from the product’s operational environment. (L)

– Robust Requirements Modeling Tools – Develop modeling tools that integrate the entire chain oflife-cycle events for a product or process, including environmental, safety, health, and other regula-tory requirements. (M)

– Integrated Life-Cycle Modeling – Develop integrated, plug-and-play tool sets and standard data-base structures that support modeling and simulation of all life-cycle factors for generic producttypes (e.g., mechanical, electrical, chemical). Include the capability for accurate modeling of all de-sign factors relevant to product support, including reliability, availability, maintainability, and sup-portability, to optimize product designs for performance, cost-effectiveness, and customer value.(M-L)

– Integration of Legacy Data – Provide the capability to integrate archived information from legacyapplications and databases into the current life-cycle model framework, and demonstrate the capa-bility for a selected product family having long-term usage and support requirements. (M)

– Integral Product Monitoring – Develop packaging and transportation systems and onboard sensorsthat monitor products from point of origin and record and report environmental conditions and han-dling history (thermal cycles, g shocks, UV and salt air exposure, etc.) to enrich the product’s life-cycle knowledge base. (M)

– Modeling Tools for Systems-Based Life-Cycle Planning – Develop and pilot modeling capabilitiessupporting requirements definition, problem-solving, tradeoff analysis, and prediction of decisionimpacts anywhere in the product life cycle (including future technology insertions) in the context ofthe environment in which the product will operate. Include tools enabling product models to take indata gathered concerning operational use and predict product condition at end of life, to support de-sign decisions about refurbishment, recycle, and disposal. (L)

– Model Linkages to MRO Management Systems – For a selected set of products, extend and inte-grate current design, manufacturing, PDM/PLM, and maintenance/repair operations (MRO) man-agement systems to support forecasting to plan for expected repair operations, prioritization of re-sources, conduct of work, resupply/reorder of spares and consumables, and similar MRO functions.(M)

– Product-Driven Support Schedules – Develop modeling tools to analyze a specific product andquickly and accurately generate the projected need for repair assets and spare parts, and optimizemaintenance schedules based on the product design and its deployment schedule. (S-M)

– Technology Impact Forecasting – Develop the means to link knowledge and projections about fu-ture technology progressions (e.g., faster processors, new materials) to optimize a product design forits intended useful life, including technology refresh or phase-out. (M)

• Goal 2: Life-Cycle Model Feedback to Design & Planning – Provide the ability to acquire and usecaptured information from users and maintenance/repair and disposition operations to enrich the fi-delity and depth of product life-cycle models, and to feed back and enhance the process and productdesign function. (M-L)

– Life-Cycle Model Connectivity to Operational Data – Develop means for capturing, verifying,and delivering needed data (including cumulative history such as performance over time and repair



trends and spares demands) for different types and families of products back to the enterprise andproduct models. Include the capability to continuously update life-cycle system models and system-of-systems models to enhance their fidelity and value. Include the capability to mine data for prod-ucts with service problems, searching development and manufacturing data for contributing condi-tions, and provide alerts if associations are found (e.g., concerning defects or potential liability is-sues). (L)

– Life-Cycle Performance Feedback Tools – Develop tools and methods to automatically capturelife-cycle performance data (e.g., actual reliability and repair turnaround times) from the enterprise’sproduct support systems and update the product knowledge base. (S-M)

– Model Database Interfaces to Life-Cycle Feedback – Establish formal interfaces with specificmanufacturer and customer databases enabling product models to link to actual life-cycle informa-tion such as spares and consumables drawdowns, frequency of maintenance and repair actions, fieldmodifications, and user feedback on performance and problems. (M)

– Real-Time Access to Maintenance Data – Develop the capability to capture and use real-timefeedback from maintenance activities in predictive maintenance/support models to improve planningand management of maintenance/repair operations, including supply logistics. (M)

• Goal 3: Model-Based Training – Provide the capability to use product and process models as the ba-sis for all training activities across the product life-cycle. Provide model-based training tools that en-able development of training content concurrent with the product or process, support training for dif-ferent kinds of products and processes, are available for training prior to release and use of the prod-uct/process, and are readily adaptable for all types and levels of user. (S-M)

– Model-Based Training Requirements Definition – Define levels and types of training needs (in-cluding both formal training and real-time job support for operation, maintenance, and product sup-port) for different classes of products, in cooperation with training community stakeholders (includ-ing universities). (S)

– Model-Based Embedded Training Concepts – Develop model-based embedded training conceptsand approaches for different classes of products and processes in collaboration with industry andgovernment user communities, academia, and training technology providers. (S-M)

– Embedded Training Pilots – Develop and demonstrate model-based embedded training technolo-gies and applications for selected products, for use by support/maintenance staff and end users. (M)

• Goal 4: Industry-Wide Sharing of Product Support Data – Work with the prime manufacturer andvendor communities to develop strategies and methods for sharing (with appropriate protection of sen-sitive information) of detailed product information needed to enable model-based life-cycle support en-vironments. (M-L)

– New Business Models – Develop new business standards that facilitate controlled sharing of pro-prietary life-cycle data and information. (S-M)

– Shared Life-Cycle Modeling Tools – Develop tools, standards, and methodologies enabling web-based access to appropriate data, models, and modeling tools to all participants in the value chain.(S-M)

– Shared Product Support Data Pilots – Select and conduct a series of small-scale government orcommercial pilot projects to demonstrate and validate tools and techniques for controlled sharing ofdata needed for model-based life-cycle management, and document the resulting benefits. (M-L)



2.4 ROADMAP FOR PRODUCT REALIZATION & SUPPORT

The following pages lay out a nominal project plan for technology development to achieve the NGMTIgoals for Product Realization & Support. The schedule is based on a January 2006 start, and the spansallocated for the defined activities are based on a convention where each activity is targeted for comple-tion in a Short (0 to 3 years), Medium (3 to 5 years), or Long (5 to 10 years) timeframe.

This project plan is intended as a reference point of departure for detailed planning purposes. Refinementof the schedule is dependent upon allocation of funding, assignment of responsible organizations, anddevelopment of detailed statements of work and project plans to accomplish the individual tasks. Furtherdetail on specific Product Realization projects proposed for near-term implementation is provided in theNGMTI white papers for Flexible Representation of Complex Models, Intelligent Models, Model-BasedLife Cycle Management, Product-Driven Product & Process Development, Real-Time Factory Opera-tions, Shared Model Libraries, and other topics. These documents are available in the NGMTI Commu-nities of Practice at http://www.ngmti.us.


Resource Management 3-1

3.0 RESOURCE MANAGEMENT

3.1 FUNCTIONAL MODEL FOR RESOURCE MANAGEMENT

Resource management (RM) is the process of integrating the activities of the enterprise to ensure that theright people and the right processes have the right resources at the right time to fulfill their functions. Itencompasses all activities associated with the business of the enterprise, including control and oversightof production and support operations, supply chains, and sales and distribution mechanisms; and humanand financial resources, knowledge and technology resources, and other assets. For assessment and plan-ning purposes, these functions can be divided into eight separate yet interrelated elements as shown inFigure 3.1-1 and described below.

Figure 3.1-1. Functional Model for Resource Management

FinancialManagement

Includes all activities associated with controlling and applying the financial re-sources of an enterprise, including accounting and financial reporting, budgeting,expenditure tracking, collecting accounts receivable, disbursing funds, and man-aging financial risks.

OperationsManagement

Includes all planning, directing, monitoring and controlling activities associatedwith the support of manufacturing operations, including scheduling, equipmentand facilities maintenance, material handling, quality systems support, and costmanagement and control.

Supply ChainManagement

Includes all activities associated with managing and controlling the network offacilities and organizations – including subcontractors, suppliers, vendors, andpartners – that provide services, materials, components, subsystems or other con-stituents of the enterprise’s products, transforms these materials into components,and distributes products and services to enterprise customers.

Marketing, Sales,& Distribution

Includes all activities associated with sales, delivery, and billing including marketresearch, product positioning and advertising; pre- and post-sale support, inquiryand quotation processing, and order processing; delivery to point of sale or pointof use; and billing.

WorkforceManagement

Includes all activities associated with managing the human capital assets of theenterprise including recruiting and retention; training and development; compen-sation and benefits; motivation; and complying with laws and regulations re-garding health, safety, employment practices, and related subjects.



Capital Asset &InventoryManagement

Includes all activities associated with tracking, controlling, and directing thephysical resources of the enterprise, including assets entrusted to it by third par-ties (e.g., partners and the government), product stocks, and materials and com-ponents maintained to meet production requirements.

Knowledge/InformationManagement

Includes all activities associated with identifying, developing, managing, con-trolling, and applying the intellectual and data assets available to the enterprise.

TechnologyManagement

Includes all activities associated with surveillance, assessment, selection, imple-mentation, and management of the technologies used by the enterprise in itsproducts, processes, and facilities. This includes coordinating the required orga-nizational changes (e.g., delivery of training and establishment of new policiesand procedures) as part of a new technology implementation.

3.2 CURRENT STATE OF RESOURCE MANAGEMENT

Competitive pressures in every sector of U.S. industry are continuing to drive companies, large and small,to manage their enterprises more efficiently and improve their ability to respond quickly to opportunitiesand challenges. Manufacturing capabilities have improved to the point where quality is rarely a genuinecompetitive discriminator, so success is increasingly tied to cost, service, and innovation – the ability tobe first to market with new and improved products.

Industry’s focus for more than a decade has been on automating and integrating enterprise processes,managing information more efficiently, reducing costs through operations streamlining, waste elimina-tion, and managing financial performance for maximum short-term results. Modeling has been a key toolin helping companies redesign their operations for greater efficiency, using techniques such as valuestream analysis and business process reengineering to eliminate non-value-added functions and focustheir resources on those processes with the greatest impact on competitiveness, performance, and profit-ability. Although modeling tools are widely used, the current generation of tools – both commercial andinternally developed – are function-specific (e.g., financial, product distribution, process design, andworkflow) and do not readily support integration across different enterprise processes.

It is increasingly important for manufacturers to integrate their design, manufacturing, and life-cycleproduct support activities with processes and systems across the rest of the enterprise, knocking downlongstanding “silos of integration” to deliver appropriate knowledge to decision-makers to maximize en-terprise performance and customer/stakeholder satisfaction. Model-based tools such as product datamanagement (PDM) applications are providing new and valuable integration functionality. However,integration of legacy systems and applications to support the new tools and technologies remains a keybarrier.

Multi-enterprise integration – the ability to integrate processes across multiple companies in a supplychain – is another critical need that is not supported by the current generation of proprietary resourcemanagement applications. Most of the tools are not affordable for small manufacturers, or simply do notoffer economic returns sufficient to justify the required investments in software, training, and support.

Enterprise integration has been a major focus of industry for decades, focusing largely on process auto-mation. Computer integrated manufacturing (CIM), manufacturing execution systems (MES), materialresource planning (MRP), enterprise resource planning (ERP), product life-cycle management (PLM),supply chain management (SCM), customer relationship management (CRM), and enterprise manufac-turing intelligence (EMI) approaches have all sought to solve pieces of the puzzle. While these solutionshave in many cases delivered great value, in many others they have failed – at great expense – to meetexpectations. The result is an enterprise systems landscape that is chaotic, with tools that don’t talk to



each other, processes that are isolated from their upstream and downstream counterparts, and vital datathat is trapped in legacy systems.

The current state is migrating from tools and functions that operate in relative isolation to a shared envi-ronment where systems and functions talk to each other; and where new capabilities are added quickly torespond to short-fuse requirements. It is moving from an environment where placing an order initiateshuman activities to get ready to produce product, to an environment where order placement automaticallylaunches processes in coordinated execution to deliver the goods. It is moving from an environmentwhere data is collected ad-hoc to one where data is collected and shared in real time to tune products andprocesses for performance, efficiency, and profitability.

Modeling and simulation are increasingly used in all aspects of manufacturing, planning, and financialmanagement. Most enterprise management software tools provide modeling functions to help users makeinformed decisions. Models can range from simple static depictions used to understand and evaluate con-cepts (e.g., organizational structures and business process flows), to formulas that calculate a result basedon a set of inputs (e.g., material balances), to sophisticated simulations that emulate a complex manufac-turing process with scientific accuracy.

Engineers use computer-aided design and engineering (CAD/CAE) applications and analytical codes tocreate, evaluate and refine product and process designs, lay out factory workflows and facility designs,and calculate throughputs and material requirements. Financial staffs use cost modeling tools to developestimates, predict cash flows, and quantify risk and return on investment. Marketing and sales staffs usemodeling tools to understand and predict customer behaviors and sales potential. Managers use modelingtools to develop business strategies and align organizational elements to execute those strategies. How-ever, all of these capabilities are highly dependent on the user’s subject matter expertise, their skill withthe application, and their ability to obtain accurate data to feed the model.

Resource modeling techniques are delivering great impact. In defining training requirements for theDoD’s Joint Strike Fighter (JSF) program, for example, an Integrated Training Center discrete eventsimulation model is used to calculate the number of instructors, simulators, classrooms, support equip-ment, and other resources required to train a given number of pilots and maintainers. This enables theservices to understand exactly what resources are needed to meet the training requirements for the threedifferent JSF types, and quickly update the training program plan to reflect changes in aircraft quantityand type mix. Like many models today, however, the JSF ITC model is entirely dependent upon the ac-curacy of its underlying data, which is in-put manually based on the ad-hoc knowl-edge of the JSF training team.

In some areas, integrated business model-ing is a reality today. Multi-functionalmodeling applications such as Proforma’sProVision and Interfacing Technologies’Enterprise Process Center provide unifiedmodeling environments that enable manag-ers to model strategies, rules, organiza-tional design, business systems and inter-actions, workflows, use cases, and more.General-purpose modeling tools such asWizdom Systems’ ProcessWorks andImagine That’s Extend (Figure 3.2-1) pro-vide a powerful capability to build dy-namic models perform what-if analyses. 1 http://www.imaginethatinc.com/prods_overview.html.

Figure 3.2-1. General-purpose modeling tools such as Extenduse generic building blocks that enable users to quickly build

complex simulations of business processes and systems.1



However, these systems all require considerable investments in resources and training that small compa-nies cannot easily afford, nor do smaller firms have the flexibility to experiment with new tools unlessthey are paid for under a contract. These issues are a major barrier to enabling smaller firms to supportthe multi-enterprise collaboration initiatives of their various prime manufacturer customers.

Table 3.2-1 notes some key attributes of the current state of practice for each of the functional elements ofresource management. Further discussion is provided in the following sections.



Table 3.2-1.Current State of Resource Management


FinancialManagement

• Reactive financial planning usingspreadsheets and “back of the envelope”

• Long lags in getting actuals to under-stand financial performance

• Little or no use of modeling tools to ana-lyze issues and understand the implica-tion of options

• Well-defined business management proc-esses based on accepted accounting prac-tices

• Routing use of spreadsheets; limited use offinancial modeling tools

• Low fidelity of estimating for large, complexprojects

• Financial management processes inte-grated across all business functionsand organizational units

• Widespread use of specialized tools forcost modeling and analysis

• Heavy reliance on accuracy of manualestimating and forecasting

OperationsManagement

• Systems are automated, but planningand management processes remaindriven by paper

• Success/failure entirely dependent onhandful of key managers and lead staff

• Limited use of modeling and simulationtools to plan and manage operations

• Good capability to plan for and maintainsteady-state operations using discreteevent simulation tools, facility CAD systems

• Widespread use of third-party consultingengineering to optimize throughput, quality,reliability, etc.

• Highly automated processes well inte-grated with operations monitoring andmanagement, especially in process in-dustries and high-volume discreteproduct manufacturing (e.g., automo-tive and semiconductors)

Supply ChainManagement

• Little or no integration of processes ortools; strong reliance on paper contractsand personal contacts

• Supplier relationships an intractablesource of cost, schedule, quality prob-lems

• Complex subcontract management envi-ronments using multiple, often incompatibletools for prime/sub interfaces

• Certified supplier programs to simplifysourcing decisions and benefit from pastgood working relationships

• Increased reliance on subs, with riskspushed down the supply chain

• Point integration of suppliers into long-term relationships using common suitesof tools and shared collaboration envi-ronments

• Supplier selection and managementprocesses use a large base of auto-mated tools; SC management costsconsume large percentage of overhead

Marketing, Sales,& Distribution

• Market modeling routine in consumerproducts sector, non-existent in others

• Sales targets set by fiat, not determinedby good models

• Distribution modeling capabilities readilyavailable from commercial tools; used asneeded

• Widespread use of modeling in consumerproduct sectors to plan/implement new in-troductions and develop demand

• Heavy reliance on company transport fleetand third-party distribution networks (e.g.,Fedex, UPS, USPS)

• Marketing and distribution processeshighly efficient, make extensive use ofmodeling and IT tools

• Econometrics and marketing mix mod-eling used to optimize sales and profit-ability

WorkforceManagement

• Automation implemented to ensure com-pliance to applicable regs; little or no useof modeling and simulation

• Management processes for staffing tendto be reactive, focused on short-term is-sues

• Wide use of spreadsheets, compensationmodels, and similar tools for manpower andHR planning

• Some use of commercial analytical tools tounderstand current resources, needs, andforecast future requirements

• Increasing use of outsourcing to reducemanpower costs

• Wide use of modeling to analyze andreengineer business processes to “leanout” the workforce

• Workforce analytics included in majorERM systems (e.g., PeopleSoft, SAP)

• Active efforts to capture workforce ex-pertise and experience in knowledgepreservation systems




Capital Asset &InventoryManagement

• Little or no modeling in capital assetmanagement processes – buy it/build itand use it until it wears out

• Inventory management an intractableissue; systems in place, but not well cou-pled to the factors that influence demand

• Strong focus on maximizing utilization ofexisting capital assets beyond their com-petitive life

• Processes supported by commercial toolsor homegrown systems

• Aggressive implementation of just-in-time(JIT) inventory management, but factors in-fluencing demand variables are not wellcontrolled – significant effort devoted tomitigating impacts of dips and spikes

• Capital decision processes and inven-tory management well supported by fi-nancial modeling and analysis tools

• Good capabilities for life-cycle model-ing of capital facility and equipment as-sets

• Improving ability to forecast demandand benefit from JIT inventory man-agement while avoiding shortfalls

Knowledge/InformationManagement

• I.T. a “necessary evil”; little or no use ofmodeling tools or any automation beyondelectronic file storage and exchange

• Paper and personnel remain the criticalassets for company knowledge

• Poor interoperability of the systemsholding different types of enterprise data

• Automated tools used to generate andmanage information in vast majority oftechnical and business processes

• Company/corporate networks with elec-tronic document storage in native and PDFformats

• Increasing use of internal web-based infor-mation management (intranets)

• PLM and ERP/ERM systems used asrepository and source for all productand process information, with costlymanual integration of legacy systems

• Increasing emphasis on tools for cap-ture, reuse, and preservation of data,information, and knowledge

TechnologyManagement

• Technology decision processes aremanual, ad-hoc, and reactive

• High aversion to risk – conscious positionas a “late follower” in implementing newtechnologies

• Structured strategic technology planningprocesses; forecasts coupled into businessplans with financial analysis

• Routine use of modeling and simulation toexplore/understand new opportunities andsolve technical problems

• High aversion to risk – technology has toproven and implemented successfully bysomebody else

• Technology management integratedinto business processes – strategicplanning, market positioning

• R&D investment decisions supportedby technology forecasting and eco-nomic modeling

• Increasing interest in automated toolsto guide technology investments

• Moderate to high risks routinely ac-cepted, but well monitored and man-aged for mitigation



3.2.1 CURRENT STATE OF FINANCIAL MANAGEMENT

Finance functions in today’s manufacturing enterprises rely heavily on spreadsheet-based models andmodeling applications because of their ease of use in preparing complex estimates. Although such toolslack the power and scalability of the enterprise planning tools available in current ERP/ERM packages,low-end tools such as Excel and more sophisticated packages such as Quantrix Modeler and Outlook-Soft’s Everest offer useful functionality at a fraction of the cost of an ERP implementation.

Commercial estimating applications provide both large and small manufacturers the ability to model costsfor discrete processes such as manufacturing. Feature-based estimating functions in tools such as MTI’sCostimator OEM (Figure 3.2.1-1) allow designers to quickly compare the cost of various part features todetermine the most economical designs.2 Applications such as BSD’s CostLink/AE provide a similar ca-pability for architectural and engineering estimating by using parametric models as templates to guide thecost calculation process.3 Many of these applications support integration with ERP/ERM systems, pro-viding the foundation for integrated model-based financial management.

Most financial models today do not incorporate non-financial drivers and thus do not support higher-levelbusiness decision processes with the needed accuracy; i.e., they do not enable accurate financial predic-tions beyond extrapolation of trends from known data.

Spreadsheet-based models are typically developed only to the level of detail sufficient to capture catego-ries of cost, and rarely provide the deeper insight companies need to identify areas where they can cutcosts. For example, manufacturers do not generally know if a particular shift is always over-staffed orthat a particular machine could have been replaced for less than the cost of its repairs over the past 6months. Activity-based cost modeling techniques have gone a long way towards helping manufacturersmodel labor costs and have been invaluable in reducing costs in the service sector. Business processmodeling techniques and applications, as previously discussed, are well-developed and widely used foranalyzing operations to eliminate non-value-added activities and better understand the factors that influ-ence the costs of processes.

The available base of specialized cost modeling tools continues to grow. COCOMO is well-establishedas the standard for estimating the cost of software development, and software companies closely trackproductivity metrics that enable them to model costs for software systems based on new and modified

2 http://www.costimator.com/products/oem.html.3 http://www.bsdsoftlink.com/costlinkae/ae_frame.htm.

Costimator CostLink/AE

Figure 3.2.1-1. The current generation of commercial estimating applications give managers agreatly improved capability to model production costs.



lines of code. Coupled with improved standardsfor software development (e.g., the Carnegie-Mellon Software Engineering Institute’s Capa-bility Maturity Model), these tools have con-tributed significantly to reduce the time, costand risk of software development for militaryproducts.

Organizations such as the Electronic SystemsCost Modeling Laboratory (ESCML) at theUniversity of Maryland provide on-line modelsfor understanding the cost impacts of technol-ogy obsolescence, test rework, and other finan-cial drivers in electronic systems design andmanufacturing. ESCML’s MOCA (Mitigationof Obsolescence Cost Analysis) is a design toolfor determining part obsolescence impact onlife-cycle sustainment costs for the electronicsystems based on future production projections,maintenance requirements and part obsoles-cence forecasts. Using on a detailed cost analy-sis model, MOCA determines the optimum de-sign refresh plan over the life of the system(Figure 3.2.1-2). Outputs from this analysis areused as inputs to the PRICE H/L commercialsoftware tools for predicting system life-cyclecosts.4

Despite the wealth of financial modeling tools available, hard barriers remain. Most such tools are notresponsive to change because they are not directly connected to the sources of their underlying data.Changing model structure andformulas is a manual process,making it problematic to ensure themodels are accurate and current.The upcoming generation of finan-cial modeling tools is tacklingthese issues, however. CostVisionis developing a modular softwareapplication (Figure 3.2.1-3) to planand manage financial, time andcapacity impacts over the productlife-cycle, from concept throughdesign, sourcing, manufacturing,and support. The total cost modelcan trade off changes in the port-folio, products, investments, manu-facturing processes, materials,tooling, and facilities. For risk as-sessment and optimization in an“engineering sandbox,” the model

4 http://www.enme.umd.edu/ESCML.

Figure 3.2.1-2. MOCA models the cost implications of allpossible combinations of design refresh points in an

electronic system's life cycle.

Figure 3.2.1-3. Next-generation tools being developed by CostVisionand others are providing the capability to develop complete life-cycle

cost models that are tied to underlying data from PDM and PLM tools.



components can be integrated or isolated, with different levels of granularity. The user interface providesaccess to data and predictive analytic workflows based on the user profile, which allows different func-tional roles (e.g., engineering, manufacturing, supplier) to collaborate within their level of access on costmodels. The web-based system integrates to CAD, PLM, manufacturing software, and ERP applicationsto maintain consistent and accurate cost inputs.5

Current financial models (and modeling tools) also are not well integrated with the rest of the enterprise.This makes forecasting targets (e.g., for orders, sales, and profits) difficult. Targets today are still arebased more on politics and competitive positioning than on reality. Much estimating, particularly in thedefense industry, continues to be driven by top-down processes where “bogeys” are assigned bysubsystem or department, and engineers and functional staff then have to back into their assigned targets.As a result, estimators tend to low-ball or inflate figures in order to account for risks and navigate theinternal negotiation processes to ensure their scope of work is adequately funded.

Poor ability to model the cost impact of technical decisions continues to be a significant problem in thedefense and aerospace sectors. Major programs such as NASA’s space station, the Air Force’s F-22Raptor, and the Army’s RAH-66 Comanche helicopter experienced extreme cost escalations due to in-adequate estimating, failure to fully account for cost risk, and inability to project the financial impacts ofchanges in requirements. Restructuring of the Comanche program in 2002 (the sixth time the programwas restructured since its inception in 1983) doubled the aircraft’s development budget to over $6 billion,and estimated unit cost grew from $24 million to more than $32 million.6 With the development testingprogram uncovering numerous technical issues requiring further outlays, the Army elected to terminateComanche in February 2004, writing off 20 years and more than $7 billion of investment.

3.2.2 CURRENT STATE OF OPERATIONS MANAGEMENT

While the definition of “operations” varies widely across different industry sectors – and even with thewalls of a single company – this manufacturing enterprise function has benefited greatly from applicationof modeling and simulation technologies. Discrete event simulation (DES) is particularly useful in thisarea since it enables users to model interdependent activities, especially in complex operations involvingmultiple interrelated processes. Major semiconductor producers routinely apply DES tools to optimizetheir production lines, which are characterized by high volume, high precision, high capital costs for spe-cialized process equipment, and significant product changeovers every 12 to 24 months. In the case ofIntel, use of operations modeling – including sensitivity studies, ergonomic simulations, factory layout/flow, throughput constraint modeling, and chaos effects – saves millions of dollars in direct and avoidedcosts each year. 7

Modeling and simulation tools such as Delmia’s Quest (Figure 3.2.2-1) and UGS’ E-Factory enable facil-ity designers to create 3-D virtual factory models including tooling and material flows to support designand optimization of operational facility layouts and factory floor process flows. AspenTech has estab-lished a leading position in the petroleum and chemicals sectors, providing unified operations and supplychain management capabilities and modeling and simulation functionality with its aspenONE productsuite.8

Operations modeling is also supported by multipurpose application environments such as TechnoSoft’sTIE (Tool Integration Environment), which uses adaptive modeling language (AML) to link a wide arrayof process modeling and cost modeling functions (Figure 3.2.2-2).

5 http://www.costvision.com.6 http://www.globalsecurity.org/military/systems/aircraft/rah-66.htm.7 Courtland M. Hilton, “Manufacturing Operations System Design and Analysis,” Intel Technology Journal, 4th Quarter 1998.

http://www.intel.com/technology/itj/q41998/articles/art_3.htm.8 http://www.aspentech.com/mfg-sc/index.cfm.



Much operations modeling is per-formed by third-party specialty firmsusing a combination of proprietary andcommercial modeling tools, and suc-cess stories are numerous with bothapproaches. ExxonMobil used Pro-Model to evaluate new equipment in-stallation and process change for itsFilms Division, enabling a capacityincrease of 40% by identifying andreengineering handling constraints.Laser manufacturer Cymer used thetool to optimize new layout design,evaluate process changes, find andexploit constraints, schedule person-nel, control work-in-process, and in-vestigate the impact of new tech-niques, helping ramp up production400% in one year and increasing reve-nues by more than $180 million.9

Despite the excellent range of capa-bilities available from commercial ap-plications, a number of barriers re-main. Creation and updating of op-erations models with useful fidelityrequires a significant investment oftime and funds, and extensive applica-tion training as well as subject-matterexpertise. The applications are costly,and much of the high-value function-ality is available only through acquisi-tion of extra modules. The high costof acquiring, implementing, andmaintaining such applications makesthem unaffordable for many smallmanufacturers.

Areas such as operations maintenancerequire yet a different brand of appli-cation, although tools such as Relex’OpSim provide a comprehensive capability for modeling of maintenance and repair operations.

Also, although different application packages have different strengths and features, investment in onepackage generally precludes investment in another one even for large firms, due to lack of interoperabil-ity. Perhaps the most significant barrier is that current operations modeling applications do not operate inreal time; i.e., they lack to capability to accept and respond to live data feeds from the shop/plant floor tosupport routine decision-making. Such integration can be done, but requires a dedicated point-to-pointdevelopment effort for the specific operation.

9 http://www.promodel.com/solutions/manufacturing.

Figure 3.2.2-1. Quest and similar modeling tools enable facilitydesigners to create 3-D virtual factory models that are tied to

constituent process models.

Figure 3.2.2-2. Multi-purpose operations modeling applicationssuch as TIE provide the ability to link activity-based costs to each

operational element.



3.2.3 CURRENT STATE OF SUPPLY CHAIN MANAGEMENT

Managing suppliers has always been key to business success, and the importance of this function hasgrown tremendously over the last decade. Mergers and consolidations have driven the rise of globalmanufacturing enterprises and “systems integrators” who coordinate the efforts of many suppliers to de-sign, manufacture, and support increasingly complex products. The growing high-technology content ofmany products also drives this trend, since it is impractical for companies to maintain internal capabilitiesfor production of specialized items that can be readily procured – with greater capability and at lower cost– from specialty suppliers.

The concept of the supply chain is evolving as well, from a paradigm where vendors deliver products tospec to one where suppliers team with larger manufacturers to design, produce, and support productsacross their life cycle. The pressure to “lean out” their operations has companies looking to outsourcemany functions, with the expectation that these functions can be managed with sufficient rigor to meetcost, schedule, and quality requirements.

Modeling and simulation play a key role in the current evolution of supply chain management. Businessmodeling tools help companies better understand their core competencies and re-engineer their processesto the point where suppliers and vendors can be integrated into those processes efficiently, delivering suf-ficient value to offset the cost of managing outsourced functions management. CAD, PDM, and PLMenvironments are enabling members of supply chains to collaborative effectively in design, manufactur-ing, and product support.

Modeling of supply chains is a well-developed capability. General-purpose business modeling tools suchas GoldSim10 provide good supply chain modeling functionality (Figure 3.2.3-1), and users have a verywide range of software applications from which to choose. The Managing Automation web site main-tained by Thomas Publishing (producers ofthe Thomas Register) currently lists morethan 495 software applications related tosupply chain management, of which 446claim “analytic” capability.

The largest present challenge in this area isthe ability to integrate real-time data, fol-lowed closely by the ability to account foruncertainty factors. Modeling of supplychain elements is limited, and there are nostandards for what constitutes a “model ofa supplier.”

Supply chain management is intimatelytied to enterprise integration, and manybarriers remain in the way of the enterpriseintegration vision. Limited CAD systemsinteroperability forces suppliers to supportmultiple systems, or convert primes’ engi-neering data into formats their tools canuse. Many sub-tier suppliers have opted to focus their business on supporting a single prime with whomthey can cost-effectively implement compatible processes and systems.

The growth of large-scale ERP systems offered by SAP, PeopleSoft, and SSA Global (Baan) is givinglarge manufacturers much better visibility and control of their suppliers. However, small suppliers are

10 http://www.goldsim.com/Solutions/ExAutoSupply.asp?Referrer1=casestudies.

Figure 3.2.3-1. Supply chain modeling is a mature science,and current applications provide excellent capabilities for

supply chain design and analysis.



bearing the brunt of the cost of buying into those environments, by either purchasing the required tools orhaving to translate data and information into formats that their prime’s particular ERP systems can accept.

Another major concern in this area is protection of proprietary information. A large manufacturer is typi-cally interconnected with multiple suppliers who compete with each other. Each supplier typically sup-ports many manufacturers who likewise compete with each other. For these relationships to work, theprimes must have access to resource and capacity information. Likewise, suppliers must have visibilityinto the primes’ requirements; however, knowing all of the details on either side may compromise thecompetitive position of the prime, the supplier, or both. Tools are needed to enable partners to access ap-propriate detail while protecting sensitive data in dynamic collaboration environments.

Compatibility is another key barrier. Despite the growth of web-based interfaces, the modeling andsimulation applications community must provide better, easier-to-use tools that all members of the supplychain can afford. Standards must be developed to define how different applications can interact in a dis-tributed environment – with appropriate security – to support integration of supply chain operationsacross the entire product life cycle.

3.2.4 CURRENT STATE OF MARKETING, SALES, AND DISTRIBUTION

The marketing functions of today’s consumer products manufacturers make extensive use of modelingand simulation tools because of the size, complexity, and chaotic nature of their customer base and mar-kets. Modern information technologies give companies an increasingly large and detailed base of infor-mation about their customers, providing the capability to refine their marketing models with ever-greaterprecision and closely track factors that impact the marketplace. Web-based surveys, usability testing,ethnographic research, and virtual reality tools are increasingly used to customize product offerings, setpricing, and profile of customers for targeted marketing and advertising efforts.

Econometric modeling is used by every large consumer goods manufacturer in the U.S. and Europe toidentify the marketing variables that maximize return on sales. One of the reasons this analysis is diffi-cult, is that brand managers focus advertising outlays to build brand equity – which often manifests as adelayed response – while financial managers define payback in terms of weekly, monthly, and quarterlysales.

Leading-edge companies now use marketing-mix modeling to allocate marketing dollars for maximumreturn. At Procter & Gamble, the insights gleaned from use of these modeling tools affected over $400million of the firm’s marketing budget in 2003 – almost a tenth of the company’s $4.3 billion global out-lay.11 Although marketing-mix modeling has existed since the 1980s, improved analytics have brought itto the foreground among progressive business practitioners. Randolph Stone, president of Aegis Group’sMarketing Management Analytics, foresees adoption of this approach by firms in the automotive, tele-communications, retail, entertainment, and pharmaceutical industries in the near future.

Sales

The digital environment is now a rapidly expanding element of the company-customer sales interface.The Internet now gives companies “storefronts” that are open 24/7, with the ability to service hundreds ofcustomers simultaneously anywhere in the world. Geographic models, data mining, and analytics are in-creasingly standard tools for lead generation, and sales force resource analysis is well established as a bestpractice for optimizing and training the sales force.

Wider use of sales-response modeling has led to more intense competition in many sectors, forcing com-panies to rethink their current sales models. In the pharmaceutical industry the technique has been usedsince the early 1990s to predict the number of prescriptions written per physician as a function of thenumber of sales contacts. This allows sales managers to determine how many contacts are needed to pro-duce results for the targeted physician segment. This has been a major driver of expansion in this indus- 11 “The 50% that works: can econometrics help us lose some of the guesswork?” Adweek, 45 (19):25, May 10, 2004.



try, where sales forces have expanded by 75% in the past 6 years. Return on sales for the top 14 pharma-ceutical companies actually declined 24% between 1998 and 2001,12 which indicates that the technique iswell past the saturation point.

Distribution

Higher customer expectations, coupled with lean operations strategies, are placing tremendous pressureon the distribution systems of both large and small manufacturers. Leading companies that use sophisti-cated modeling tools to distribute large quantities of small items include well-known shipping servicesFederal Express, UPS, and DHL. Leading enterprise management systems providers such as SAP andASPEN include robust distribution planning and analysis capabilities as part of their product lines. TheDepartment of Defense, through its Defense Logistics Agency, uses sophisticated models to move peopleand supplies around the world; however, DoD faces formidable challenges because it must keep pace withrapidly changing and unpredictable requirements in managing the world’s largest and most complex in-ventory.

The distribution function for most large and mid-sized manufacturers operates using well-developedmodels that incorporate geographic information, logistics, and delivery schedules into highly effectivedistribution systems. Ketera, MindFlow, Oracle, and others provide a myriad of solutions for spendanalysis, sourcing analytics, contract monitoring and tracking, cost performance analysis. These toolsalso support modeling of complex monetary parameters that include duties, taxes, tariffs and othercharges associated with complex international distribution systems. The growing complexity of today’sconsumer marketplace has spawned a large market of its own for third-party distribution management.

As with other areas of enterprise resource management, the barriers to wider use of model-based tech-nologies in distribution management include the cost and complexity of available tools, and the difficultyof modeling uncertainties. The ability to detect, analyze, and respond to subtle qualitative factors thatinfluence demand – and hence impact the distribution system – remains more art than science.

3.2.5 CURRENT STATE OF WORKFORCE MANAGEMENT

Modeling in the broad sense is routine in the workforce management area, with enterprises maintaininghuman resource (HR) models in the form of structured job descriptions and organizational models thatenable them to relate manpower and skill requirements to enterprise talent needs. Competencies andskills are the basic building blocks of HR management, and do not lend themselves to the kind of mathe-matical description needed to create models that enable accurate prediction. Salary structures and jobcategories are well modeled, but the tight definitions of job categories in the models limit flexibility. Inaddition, job performance-specific models are lacking and human resource decisions are often impededby poor job specification. Factors such as attrition and turnover can be modeled based on historical data,but the effect of potential actions to influence those factors cannot be predicted with statistical accuracytoday.

Many workforce management decisions are based on business reality instead of modeled scenarios. Morethan one large company has met the challenge of reducing overhead rates by simply jettisoning the de-sired percentage of overhead staff – leaving the survivors to figure out how to get the same amount ofwork done with fewer bodies.

Simulation techniques are used in re-engineering of business process workflows to eliminate non-value-added activity, sustain or increase productivity with smaller workforces, and align skill and manpowerneeds more closely to business objectives. PeopleSoft, Baan, and other major ERP vendors provideworkforce analytics as a part of their HR management packages, but their modeling and simulation func-tionality is not well defined in available literature.

12 ”Hard sell: as expanding the sales force becomes a less attractive option, pharmaceutical companies are reevaluating their sales strategies.” In

Med Ad News, 23(3), No. 1, March 2004.



Another problem today is that business metrics do not reflect job requirements well. Staff utilization israrely efficient, and employee education is often not well aligned with actual job requirements. Employeetraining is migrating to training for specific functions, but mandatory compliance training is consumingmany companies’ training budgets at the expense of training for useful skills.

Global outsourcing of many core enterprise functions (particularly customer service and product supportfunctions) and shifting workforce demographics have completely overturned the traditional employer/employee expectation of an implied lifetime relationship. The current trend toward flexible job assign-ments and short-term employment is not being handled gracefully by many organizations. Ability tomodel these issues is significantly lagging today’s realities, with most companies making outsourcingdecisions purely on the basis of near-term cost and profit factors. Inability to model the long-term im-pacts of these strategies has many companies gambling on their future ability to compete.

3.2.6 CURRENT STATE OF CAPITAL ASSET & INVENTORY MANAGEMENT

Capital asset and inventory management is a key managerial concern as manufacturers face increasingcompetition in the global marketplace. Competitive survival demands reducing costs and closely match-ing capacity to demand. As a result, capital assets and inventory are becoming increasingly importanttargets for cost-reduction efforts.

Capital Asset Management

Capital asset (equipment, facilities, etc.) tracking models are mature, and tools such as the BalancedScorecard Method (Figure 3.2.6-1) are widely used to evaluate return on assets. U.S. manufacturers havebillions of dollars invested in capital assets ranging from skilled personnel and fixed plants to equipmentsuch as machine tools and cooling towers. Even small improvements in managing and maintaining assetscan have a major impact on a company’s financial position.

Acquiring, maintaining, and disposing of assets is serious business. A fractional improvement in manag-ing capital assets can be worth millions of dollars annually to a large manufacturer). However, the diffi-culty of accessing detailed information on these assets makes decisions regarding postponing purchases oreliminating current assetsrisky. The importance ofeffective capital asset man-agement models that enableexecutives to make suchdecisions accurately andconfidently is clear.13

Accurate assessment of theremaining operational life-time of a capital asset re-quires a broad range ofmodel expertise (i.e., vibra-tion characteristics, oilanalysis, motor current dy-namic characteristics, ther-mographic behavior, andprocess conditions). Tradi-tional capital managementsystems supply raw data toin-house experts who ana-

13 “Climate is Right for Plant Asset Management and Condition Monitoring Solutions to Grow Substantially,” David Clayton, Senior Analyst for

ARC Advisory Group (www.arcweb.com/Newsmag/ent/pam061903.asp)

Figure 3.2.6-1. The Balanced Scorecard is a widely accepted management toolfor allocating capital resources to support critical drivers of business success.



lyze data and make recommendations regarding each asset’s health. However, experienced and knowl-edgeable employees are leaving manufacturing plants in ever greater numbers. As a result, the era of in-house maintenance experts is coming to an end, and there is a growing need for open, fully integrated so-lutions capable of examining asset performance and comparing it to performance models and drawingaccurate conclusions from the disparate data. This need is fueling growth of a new breed of integrated,model-based PAM and CM solutions.

Inventory Management

The high cost of carrying product and material inventory between the point of resource need and the pointof product sale has driven most manufacturers to adopt just-in-time practices that minimize in-processinventory. Advanced, model-based inventory management (AIM) systems are mature and widely used toimprove warehouse operation and efficiency. Internal routing models permit the definition of step-by-step paths to follow for the movement of goods and all the properties assigned to each step: label printing,confirmation options, status, reference, and lot/serial changes.

Inventory management is closely tied to distribution management and entity-relationship distributionmodels typically include inventory management functions (Figure 3.2.6-2).14 Inventory modeling is awell-developed discipline, and the only significant barrier in this area is the difficulty of linking live datato the model to enable real-time problem-solving.

Figure 3.2.6-2. Inventory modeling is a well-developed discipline closed tied to distribution modeling.

14 Michelle A. Poolet, “Product Distribution Metamodel,” July 2002.

http://www.windowsitpro.com/SQLServer/Article/ArticleID/24912/24912.html.



3.2.7 CURRENT STATE OF KNOWLEDGE/INFORMATION MANAGEMENT

There is tremendous emphasis on knowledge management in the ERM domain today, but use of modelsto manage knowledge assets – beyond relatively simple functions such as reuse of CAD data – is in itsinfancy. In most companies, there are few organized efforts to document knowledge in reusable forms.Although progress is being made with distributed workgroup tools and company intranets, only the largercompanies can afford the cost of developing and maintaining focused systems for knowledge manage-ment. While smaller manufacturers typically have a much smaller and more focused base of knowledgethey wish to preserve, these assets are almost universally captured in the same source – their most experi-enced and skilled employees.

Today, huge amounts of valuable information are captured in ways that make it difficult to reuse (i.e.,embedded in spreadsheets, text documents, and application files having proprietary formats). However,the kinds of knowledge that are most valuable to the enterprise reside in the heads of key personnel at alllevels of the organization. Formal documentation of best practices has helped companies preserve andshare institutional wisdom, but much of such information is of limited usefulness because it is highlycontext-specific.

ERP and PLM systems, which are in wide use today even by smaller businesses, are increasingly be-coming the repositories for the data and information used to support day-to-day operations and manage-ment planning. However, these tools are designed primarily as database-based application systems, anddo not provide the deeper functionality associatedwith emerging knowledge management concepts.

3D CAD is enabling significant improvements in thequality of training while reducing the cost of devel-oping and maintaining training materials (Figure3.2.7-1). Product models generated by designers arenow being ported directly into training media, elimi-nating much of the cost and time of creating docu-mentation. Assembly models and simulations de-veloped to optimize product manufacture are beingused directly for training of maintenance and repairstaff. CAD designs can also be downloaded tostereolithography systems to produce physical mod-els, thus reducing costs associated with creatingtraining aids while providing exact form/fit replicas.This is particularly valuable in maintenance trainingfor complex, expensive equipment.

Advances in interactive simulation, being led byDoD initiatives such as High Level Architecture(HLA), are laying the foundation for distributed in-teractive training that relies heavily on modeling andsimulation. These technologies will enable teams of geographically dispersed individuals to train inshared synthetic environments, ultimately combining both simulators and live assets “in the loop”.

Numerous modeling and simulation technology advances are needed to support such capabilities, rangingfrom more robust and comprehensive standards for product models (physics as well as geometry), fasterand more powerful processing capability, enhanced visualization, and interaction of synthetic entities.

Figure 3.2.7-1. Sharing of model-based technicaldata greatly reduces the cost and time of developingdocumentation. It also ensures that all users have

the exact same data and version information.



3.2.8 CURRENT STATE OF TECHNOLOGY MANAGEMENT

While most companies use formal processes to allocate R&D funding for technology development andmaturation, corporate decision-making in this area is highly subjective. Current business modeling capa-bilities aid in aligning corporate priorities with core competencies, but investment decisions are predomi-nantly driven by the perceptions of the organization’s senior technology managers. Significant effort isdevoted to quantifying market potential in order to calculate ROI and risk in different scenarios, but asidefrom spreadsheets and rule-of-thumb models such as Moore’s Law15, few tools exist to model the tech-nology investment process to support specific business decisions.

Modeling and simulation are used extensively to explore concepts for new products and product im-provements, particularly in the defense sector. Monte Carlo force-on-force simulations are used routinelyto gauge the potential value of new weapons and sensors, and these results in turn support marketing tomilitary customers to influence DoD R&D funding. Simulation-Based Acquisition principles are now acornerstone of current DoD strategies to ensure that new weapon systems are not over-engineered andthat they stay on track to meet requirements at each stage of the development process. Standard toolssuch as FLIR92 and NVTHERM are commonly used to model the performance of infrared sensor sys-tems, and MATLAB is used to gen-erate similar kinds of performanceanalyses for radar systems. Manyagencies also apply “should cost”modeling techniques to quantifycost risk in major development pro-grams, helping neutralize the com-petitive advantage of lowballquotes.

The Technology Readiness Level(TRL) model is rapidly gaining fa-vor in managing technology devel-opment in the aerospace sector. TheTRL process, simply put, assignsTRL levels on a scale of 1 to 7 ac-cording to the evaluated maturity ofsubject technology, where TRL 1 =basic research and TRL 9 = in pro-duction and “flight proven.” Indus-try proponents are moving towardadopting a similar methodology formanufacturing (MTRL), whichaligns closely to the basic TRLmodel (Figure 3.2.8-1).

The past decade has also seen a shiftin technology management strate-gies for high-tech manufacturers.Prime contractors are increasinglyrepositioning themselves as “systemintegrators,” reducing their ownR&D budgets and relying on their supply chain members to deliver innovations that they can incorporate

15 The observation made by Gordon Moore, co-founder of Intel, that the number of transistors per square inch on integrated circuits doubles ap-

proximately every 18 months.

Figure 3.2.8-1. TRL and MTRL models provide a structured methodof assessing technology readiness to guide R&D investments.



into their product lines. This has shifted the burden of development risk to smaller companies, whoseability to get problematic programs back on track is limited by their smaller pool of resources.

Companies such as General Motors have applied system dynamics principles to model the effectivenessof research partnerships, helping better understand the impacts of factors such as cooperation, conflict,and trust (Figure 3.2.8-2).16 This kind of model will only increase in value as more companies move todistributed technology development strategies.

Figure 3.2.8-2. System dynamics modeling techniques can help companies better managetheir technology development partnerships.

16 Modeling Relationship Dynamics In GM’s Research-Institution Partnerships, Gülcin H. Sengir et. al., IAMOT 2004, 15 March 2004.

http://www.iamot.org.



3.3 FUTURE STATE VISION & GOALS FOR RESOURCE MANAGEMENT

Future manufacturing enterprises will continuously optimize internal and external resources to maxi-mize value to all stakeholders. Fully integrating model-based design, manufacturing, and productsupport processes with all business functions will make appropriate knowledge available to decision-makers and enable them to tune enterprise performance for total customer and stakeholder satisfac-tion.

In the future, the core elements of the enterprise (including its business rules and strategies as well as itsprocesses and systems) will be modeled so accurately and thoroughly that routine allocation of resourceswill be handled autonomously by enterprise resource management (ERM) systems. These systems willhave total connectivity to all enterprise processes and assets – including product/process capabilities,manpower and skills, facilities and equipment, raw material and product inventories, supply chain capa-bilities, and working capital and budgets (Figure 3.3-1).

This seamless connectivity will extend to every tier of the enterprise’s supply chains. An open businesssystems architecture based on well-defined standards for modeling and managing different types of re-sources will enable different companies to quickly “plug together” to exploit new opportunities. Whileallocation of resources will always be at the discretion of the enterprise’s managers, the ability to accesscurrent resource information anywhere in the supply chain – with appropriate security – will eliminatemuch of the inefficiency inherent to managing complex supply chain relationships.

Science-based models of the inputs, outputs, demand factors, and dependencies of every enterprise proc-ess, coupled with continuous access to all sources of information that affect these processes, will provideclear definition of what resources need to be where and when, and when they will be available again forreallocation. These models will control the systems that execute the enterprise’s technical and businessprocesses. Managers at all levels will interact with the system to develop plans, monitor performance,analyze issues, evaluate opportunities, and efficiently direct resources to point of need.

Figure 3.3-1. Future ERM systems will provide total connectivity of all enterprise processes to all enterpriseresources, with powerful modeling and simulation capabilities that enable fast, accurate decisions.



The greatest benefits of model-based resource management will come from radically improved ability toprepare for new requirements, and to respond to problems, throughout the supply chain. Future productand process models will provide precise definitions of the resources they require for their execution – in-cluding raw materials, parts, and components; manufacturing labor and skills; facility space, equipment,tooling, and fixtures; handling and transport; and product support, including training and documentation.These requirements will be “uptaken” by the ERM system and fed to functional planning systems for im-plementation. Managers will use desktop modeling and simulation tools, connected to the enterprise’sknowledge bases, to evaluate options for meeting the requirements with those resources in ways that offerthe best balance of performance, speed, cost, risk, and profitability.

The same tools will enable managers to rapidly determine the best response when requirements change asa result of design changes or performance or schedule problems anywhere in the supply chain. Intelligentadvisors will rapidly recalculate the impacts of an actual or planned change in resources on all other de-pendent resources, and provide recommendations for corrective action to get the product, process, project,program, or operation back on track.

CROSS-CUTTING GOALS & REQUIREMENTS FOR RESOURCE MANAGEMENT

• Goal 1: Model-Based Resource Management – Provide model-based tools and techniques to manageall resources across all components of the manufacturing enterprise. (M)17

– Model-Based Enterprise Architecture – Develop an open, model-based business systems archi-tecture that enables the necessary interconnections and resource-related information flows betweenand among different enterprise processes. Include the capability to support different sizes and typesof manufacturing enterprises, including small suppliers as well as OEMs and complex supply chains.(M)

– Generic Resource Models – Develop a generic set of models and modeling standards for commonresource types that can be customized to meet the specific needs of any manufacturing enterprise.Include materials, manpower, skills, process equipment, unit processes, facilities, capital/cash, andother common forms of resource. (M)

– Resource Data Linking – Develop methods, tools, and techniques for linking model-based resourcemanagement applications to current resource status information from different processes, functions,sites, and organizational entities. (S)

– Resource Change Management – Develop a computer-based advisory tool, compatible with cur-rent MRP/ERP/ERM and operations management software applications, to alert resource owners andusers when a requirement changes, so as to enable quick negotiation and implementation of theproper response. Include the capability to automatically communicate changes in resource require-ments and availability to all affected organizations, systems, and applications. (M)

• Goal 2: Multi-Enterprise ERP/ERM Integration – Provide mechanisms and methods for rapidlyinterconnecting the systems of different enterprise partners to integrate ERP/ERM functionality to thelowest tier of the supply chain. (M)

– ERP/ERM Interface Frameworks – Develop interface frameworks and standards for quickly andseamlessly integrating different resource management systems across different companies. Includethe capability for ERP/ERM systems to automatically negotiate full or limited interfaces dependingon the capabilities of the systems being interfaced. (M)

17 The S-M-L designations identify a nominal timeframe for delivery of the specified capability, where S (Short) = 0 to 3 years, M (Medium) = 3

to 5 years), and L (Long) = 5 to 10 years.



– Linkages to External Resource Sources – Develop tools for linking ERM systems to external re-source information sources to enable continuous update of resource information to support planningand decision processes. (S)

– Distributed Resource Status Tracking – Develop model-based tools for continuously tracking andforecasting resource status throughout the supply chain, enabling real-time updating of activityschedules based on internal and external resource constraints. (M)

3.3.1 VISION & GOALS FOR FINANCIAL MANAGEMENT

Future decision-makers will be able to quickly predict, with confidence, the financial implications ofall decisions. The systems and models that drive the enterprise’s business processes will capture finan-cial data and relationships with precise accuracy to provide a continuous, clear view of performance.Financial models and simulations will take into account both financial and non-financial parameters,predict well, and enable managers to continuously fine-tune financial strategies for business success.

In the future manufacturing enterprise, model-based business management systems empowered by fullyinterconnected business processes will provide real-time visibility into all aspects of financial perform-ance. These systems will enable managers to rapidly analyze financial issues and consistently make thebest decisions based on all relevant factors and options. Real-time cost reporting systems will continu-ously feed the business models to reveal cost problems, and desktop analytical tools will enable fastevaluation of options for workarounds, recovery, and re-planning.

Product and process models will be fully populated with accurate cost data via interfaces to the enter-prise’s financial systems. Cost information for labor, materials, parts, commodities, and support elementswill be automatically captured along with appropriate rates and factors and linked to the associated prod-uct and process models, updating themselves automatically whenever the underlying data change. Be-cause cost information will always be captured at the lowest level of the design or activity, basic cost in-formation for an element will travel with it transparently when the same element is applied to a differentproduct or process.

Routine changes in cost basis (such as fluctuations in material or commodity costs) will be handled auto-matically within defined limits. In the case of design changes, the PDM system will automatically extractthe change information from the product/process model and pass it to the appropriate function (Engi-neering, Purchasing, Subcontracts, etc.) for re-pricing.

With product and process models able to link to actual cost history captured in the enterprise’s knowledgebase, preparation of estimates will require minutes rather than hours – freeing functional personnel to fo-cus on their “real jobs” and ensuring that estimates are accurate and complete. This will remove much ofthe time, complexity, and risk in bidding for large and complex contracts, particularly in the defense andcivil engineering sectors.

Model-based costing will also enable companies to provide required cost information to partners and sup-ply chain members without revealing sensitive financial data such as rates and factors. This will facilitategreater openness in teaming on large, multi-company programs where competitors today have great diffi-culty in working together.

Although many managers will continue to rely on simple spreadsheet-based models to aid day-to-day fi-nancial decisions, most manufacturing firms will have converted to more powerful and capable enterprisemanagement tools. Both financial and non-financial parameters will be included in decision processes,and the models will capture the reality (with varying fidelity based on the ability to express factorsmathematically) of all parameters impacting financial decisions. They will predict well and, by compar-ing actual to predicted performance, help companies determine where they can or should cut costs. Theability to monitor maintenance and repair trends, for example, will enable production managers to quicklydetermine if an item of equipment should be replaced. The ability to understand the precise contributionof the equipment to cost and profit will enable the manager to quickly select the solution that best meets



near-term requirements at the lowest cost – or the one that fulfills the immediate need and provides addi-tional capability to meet future needs.

Financial models will be well integrated into all of the enterprise’s functions and, as a result, realistic costand profit targets will be easy to set and track. Calculating the net present value (NPV) of expected futurecash flows will continue to be a widely used method for evaluating financial options, but most organiza-tions also will evaluate the financial implications of non-financial options using pricing models, MonteCarlo simulations, and other modeling tools.

Integration of different companies’ accounting systems will remain a challenge to financial modelingacross supply chains. Even should the U.S. pass legislation to unify accounting practices, the widely dif-ferent standards in the global business environment will continue to complicate financial planning andmanagement in international business dealings with customers, partners, and suppliers Creation of coun-try-specific financial knowledge bases will be key to advancing modeling capabilities in this area.

Goals & Requirements for Financial Management

• Goal 1: Enterprise-Wide Product & Process Cost Models – Provide cost modeling systems andtechniques that enable integration of all required data, within and external to the enterprise, to supportmodeling and analysis of cost, profitability, and other financial attributes of a product or process de-sign. (L)

– Model-Based Cost Standards – Establish financial information management standards that supportcreation of comprehensive cost models for products and processes. Include provision for capture ofdirect and indirect costs; integration of life-cycle factors such as maintenance and repair, spares,training, and recycling/disposal; and tailoring to meet the unique requirements of a particular indus-try sector. (M)

– Intelligent Cost Models – Develop cost modeling techniques that automatically distribute the ef-fects of a change in one cost parameter across all affected cost models, and automatically performdynamic updates against enterprise data sources to ensure currency of cost data. Include the capa-bility to alert all affected business functions when costs change beyond defined thresholds. (M-L)

– Automated BOE Documentation – Provide the capability for cost models to automatically docu-ment the basis of cost at the material, part, subsystem, and system levels. Include the ability toautomatically update the bill of material and capture engineering estimates as designs are created,retrieve and apply cost history for similar items, pull in subcontractor/vendor quotes, apply overheadrates and other factors, and query manual inputs that conflict with captured data. (M)

– Multi-Level Cost Modeling – Develop tools to model costs at different levels and from differentperspectives (e.g., activity-based versus product-based) and automatically present the user-requestedview. Include the capability to “click down” to the lowest level of the model. (M-L)

• Goal 2: Enterprise Financial Simulation Environment – Provide the capability for cost modelingapplications to obtain and evaluate current financial status information and requirements, and to accu-rately predict effects of contemplated actions or events on capital levels, funds flow, profitability,ROS/ROI, rates and factors, and other financial factors. (L)

– Accounting Integration Model – Develop a comprehensive, generic accounting data model towhich an individual enterprise’s cost structure can be automatically mapped, enabling rapid correla-tion of cost elements and associated data among all members in a supply chain. (M)

– Distributed Financial Engineering Tool Suite – Develop financial engineering and analysis toolsto enable integrated modeling of all finance functions (estimating, accounting, asset management,cash flow management, etc.) throughout the enterprise. (M-L)



– Extended Enterprise Financial Data Interchange – Develop methods and tools to integrate andcontinuously update financial data across the extended enterprise to provide a unified view of finan-cial health, status, and issues. (M)

3.3.2 VISION & GOALS FOR OPERATIONS MANAGEMENT

Future operations functions will be directed by an integrated enterprise model that provides full visi-bility, accurate prediction, and real-time control of scheduling, resource allocation, performancemonitoring, maintenance, and day-to-day problem-solving.

In the future, operations teams will meet their business objectives using model-based planning and controlsystems that provide real-time, in-depth (appropriately filtered) access to all factory/plant/activity status,performance, capability, and utilization information. The operations model will monitor real-time feed-back from ubiquitous sensors, continuously comparing actual to predicted and required performance toensure that all activities conform to their requirements and are continuously tuned for best results.

Supervisors and managers at different levels (unit process, production line, shop floor, plant/factory, etc.)will be able to quickly detect and assess performance issues and direct the right resources to correctproblems, respond to challenges, and optimize the performance of their operations (Figure 3.3.2-1). Themodel-based operations management system will also enable them to predict, with high degrees of confi-dence, the impacts of planned or potential changes. This will enable managers to quickly evaluate differ-ent options for improving performance, such as rearranging shifts and workflows, adding or changing outequipment and tools, and adjusting work-in-process inventory levels. They will be able to “plug in” dif-ferent resource options into the operations simulation and run multiple simultaneous scenarios to deter-mine the best cost/performance solution. The system will automatically generate implementation plansthat itemize and schedule the tasks to be done to accomplish the change, including procurement, installa-tion, checkout, worker training, and revision of workflows and maintenance plans.

Routine fluctuations in process performance will be automatically corrected by the operations modelbased on defined process control rules. Although humans will always be in the loop for safety-criticaland other sensitive processes, model-based operations systems will enable fast, accurate assessment ofcorrective action options while providing automated oversight to prevent, or minimize the impact of,process upsets. The operations model will implement an orderly shutdown when a process upset resultsfrom an accident, failure, natural disaster, or other cause, and automatically implement emergency re-sponse plans. The model will also define options for degraded-mode operations.

Model-based operations management capabilities will initially be implemented to control activities at theshop floor level, including both highly automated unit operations (e.g., batch chemical processing) and

Figure 3.3.2-1. Model-based operations management will enable precise control of all factory systems,interfacing with equipment-level automation to continuously tune performance.



those combining automated and manual processes (e.g., product assembly). This capability will then beextended to more complex operations including multi-site manufacturing across supply chains. Interde-pendent operations functions that today are managed as separate processes, such as product distributionand product support, will be fully integrated into the enterprise operations model. This will ensure thatevery part of the enterprise that is affected by a change or a problem is able to respond quickly and cor-rectly to the event.

Since what constitutes “operations” varies widely in different sectors and from company to company,model-based technologies, applications, and systems for operations management will be flexible as wellas modular. Model-based planning and con-trol systems for process systems and equip-ment will have robust interfaces that enableself-integration with upstream (lower-level)and downstream (higher-level) processes.This will support the emergence of flexible,adaptive operations systems able to reconfig-ure themselves to meet changing requirementssuch as variation in production rates andswitchover to new products or new models(Figure 3.3.2-2).

Maintenance activities will continue to be oneof the more costly components of facility op-erations, but model-based monitoring andmaintenance will greatly reduce these costswhile improving uptime and ensuring qualityperformance of complex operations. Facilityoperations models will integrate componentlife-cycle models for individual units ofequipment, with many of these models pro-vided by equipment vendors and the remain-der developed in-house using standard modeling tools and templates. This will enable creation of amodel-based maintenance program covering all facility requirements, including inspection, routine serv-icing, and parts replacement at regular intervals based on process throughput and demand factors. Thefacility operations model will continuously track performance versus plan, factoring in unscheduledmaintenance and repair calls and supporting analysis to determine if maintenance intervals should bechanged for any item of equipment.

In the broader context of operations, next-generation modeling and simulation tools will enable designersto rapidly explore options for configuring and integrating product and process elements so that the ele-ments most likely to fail, or requiring the most maintenance attention, can be quickly and easily (andsafely) serviced on site using a minimum of special tools. Designers will also use simulations to deter-mine the life-cycle support impacts of product and process design changes, linking to the supply chainmanagement system to rapidly get cost/schedule/technical impact assessments from suppliers and ven-dors.

The operations model will also provide the basis for all work instructions related to operations support.Maintainers and service technicians will be able to call up process and equipment models on their desktopor heads-up display, quickly “click down” to the area of interest, and bring up an interactive simulationand instructions for servicing the affected equipment. Analytical tools will enable service staff to trouble-shoot complex problems and evaluate the feasibility of different solution approaches. Support staff any-where in the world will also be able to collaborate directly with on-site staff, using the model to point outproblems and rapidly work through solution options. This will enable the operations support team to

Figure 3.3.2-2. Plug-and-play process and equipmentmodels will enable manufacturers to quickly build high-fidelity operations models to support real-time control as

well as planning for the future.



quickly plan, evaluate, select, and implement corrective action plans including a mix of options – recall,repair in place, updating of usage or maintenance/repair instructions, etc.

Goals & Requirements for Operations Management

• Goal 1: Operations Element Modeling – Provide the capability to create accurate models and simu-lations of manufacturing operations for an entire facility, and to integrate and modify constituent mod-els to mirror the real-world facility and its assets and processes. (M)

– Operations Modeling Framework – Establish standard conventions for creating high-fidelity mod-els and simulations of manufacturing operations, including equipment, tools, fixtures, unit processes,physical facility attributes (structure, utilities, etc.), material flows and transport systems, workflows, monitoring and control functions, safety systems, and other attributes of interest. (M)

– Generic Integratable Process Models – Develop generic models for different kinds of processesand facilities associated with manufacturing operations in different business sectors. Include inter-face definitions and hooks that enable integration of equipment models, unit process models, and fa-cility models into higher-level operations models with accurate linking of inputs and outputs betweenand among each element of the system. (M)

• Goal 2: Real-Time Factory Modeling – Provide the capability to develop supply-chain-wide factorymodels that provide real-time representations, run large portions of factory operations, and are able tooptimize operations under multiple constraints. (L)

– Emergent Workflow Modeling – Develop the capability to automatically re-optimize workflowmodels in response to any changing factors impacting operational requirements or constraints. (M-L)

– Performance Barrier Modeling – Develop modeling and simulation applications to identify andquantify limitations to operational performance. Include the capability to examine equipment issues(capability, throughput, capacity), staffing and skills issues, regulatory issues (e.g., safety and envi-ronmental requirements), and material availability issues (raw materials, supplied components, han-dling capability, etc.); and the ability to interface with the enterprise design function to examine andimplement product and process design changes to improve operational performance. (M-L)

– Extended Factory Modeling – Develop methods, tools, and techniques for modeling extended fac-tory operations across multiple sites and linking to current resource status information (i.e., utiliza-tion and capacity from all sites. (L)

• Goal 3: Model-Based Operations Control – Provide the capability to integrate proprietary controlmodels for equipment and unit processes to enable model-based control at the shop floor and factorylevels. (L)

– Process Control Linkage – Develop methods for linking individual equipment and process per-formance monitoring and control functions to facility operations models to support model-basedcontrol of operations performance. Include the capability to monitor external factors that affect op-erational performance, such as supplier production schedules. (M)

– Performance Reporting Modules – Develop generic reporting modules that can be plugged intooperations models to deliver defined performance status information sets for different types of proc-esses and equipment. Include the capability to respond to specific queries and calculate the impactsof simulated changes in operating parameters. (M)

– Equipment & Material Status – Develop equipment/material status systems that interface withother enterprise planning and management systems to continuously update operations models with



equipment and material resource availability and utilization information. Include the ability to pre-dict the impact of running equipment at 100% capacity/utilization for sustained periods. (M-L)

– Model-Based Performance Management – Develop model-based tools and methods to monitorand evaluate the performance of factory operations including resource staging and application, mate-rial and work-in-process flows, and off-line activities. Include the ability to simulate problems andchanges in selected functions to support troubleshooting, tradeoffs and optimization, and planning tomeet new requirements. (L)

• Goal 4: Integrated Maintenance Modeling – Provide the capability to apply real-time feedback fromthe production floor to plan and manage all operations maintenance activities using an integratedmodel-based operations management system. (L)

– Standard Maintenance Models – Develop standards for creating and managing model-basedmaintenance information for manufacturing equipment and operational processes, with the capabilityto integrate vendor-provided maintenance models into the enterprise’s operations management sys-tem. (S)

– Model-Based Maintenance Frameworks – Develop frameworks for model-based operations man-agement systems able to integrate predictive and reactive maintenance models for equipment andunit processes. Provide the capability to create maintenance models for operational elements thatlack vendor-provided models, and to automatically capture actual performance history to refine themodels over time. (M)

– Model Linkages to Maintenance Management Systems – Develop standards for linking productand process models to maintenance/repair management systems to support forecasting, prioritization,and conduct of maintenance work, resupply/reorder, and similar functions. Include the capability formaintainers to remotely access technical data, assembly models, and instructional media as well asOEM support services. (M-L)

– Autonomous Model-Based Maintenance – Develop predictive modeling and diagnostic technolo-gies to support the ability of operations management systems to autonomously plan and implementall required preventive and reactive maintenance/repair actions. (L)

3.3.3 VISION & GOALS FOR SUPPLY CHAIN MANAGEMENT

Widespread use of model-based processes at every level of the supply chain, coupled with model-basedtechnical and business management tools, will enable manufacturers of all sizes to efficiently managethe intricacies of designing, producing, supplying, and supporting products in a highly dynamic andcompetitive global marketplace.

Model-based supply chain management is arguably the most important capability that manufacturers willneed to survive and thrive in the future business environment. Development and proliferation of model-based technical and business processes will drive the evolution of highly agile supply chains that usemodel-based techniques to quickly recognize and respond to opportunities and problems. Model-basedconnectivity will dissolve many of today’s walls between prime manufacturers and their suppliers.Smaller manufacturers will serve as virtual “specialty departments” simultaneously for multiple primes,distinguished from in-house departments only by their company nameplates and reporting chains.

Automated market surveillance systems will monitor demand and potential, continuously canvassing on-line sources of information on market forces and trends (both economic and competitive) to accuratelyforecast demand profiles for current and future product. This will give product managers and facilitymanagers at every level of the supply chain clear visibility of upcoming needs as well as provide earlywarning of disruptive events that impact business requirements.



Model-based product and process definition will enable seamless integration of supply chain operations.Prime manufacturers’ business planning systems will interface to comprehensive capability modelsmaintained by potential suppliers. These models will be virtual mirrors of a supplier’s products and pro-duction capability, complete with high-definition models of equipment and processes and including bothperformance and capacity information. This will allow designers and procurement teams to quicklyevaluate the ability of a supplier to support prime requirements, either singly or in combination with othersuppliers.

The system will also enable engineers, planners, and managers at all levels of the supply chain to collabo-rate in virtual environments where designs and approaches are optimized for the best balance of perform-ance, reliability, cost, schedule, and other factors. All product design data and supporting informationwill reside in secure repositories readily accessible – via the owners’ PDM system interfaces – by author-ized users anywhere in the world. This will eliminate the time, cost, and complexity of maintaining andreconciling multiple versions of the same data at different stages of the supply chain, and ensure thatevery member’s engineering, planning, and management tools are always operating off the same infor-mation.

The ability of future model-based CAD, PDM, and planning and reporting systems to transparently ex-change information between different brands of application will eliminate the cost and time of transfer-ring or recreating design definitions shared among different members of the supply chain. For suppliers,this will eliminate the need to operate and support multiple CAD/PDM and analytical tools. For primesand suppliers, this will eliminate a major source of design errors and reduce the time and cost of movingnew products and processes from design to production.

Operation of suppliers and primes in an integrated PDM environment is essential to realizing the vision oftrue science-based manufacturing. Suppliers will no longer simply provide materials, parts, or subassem-blies for the prime to incorporate into their products; they will also provide the complete design defini-tion, complete with properties, characteristics, and supporting analytical data. This digital knowledge willbe seamlessly integrated into the prime’s product and process models, providing a complete and totaldefinition of the product or process and its underlying science. This will enable engineers to create mod-els and simulations that accurately reflect – not merely approximate – the true properties of the design toits lowest level.

Goals & Requirements for Supply Chain Management

• Goal 1: Extended Enterprise Interoperability – Provide standards and methods enabling seamlessinterconnection of model-based processes among supply chain members. (M-L)

– Common Supply Chain Language – Define and develop a standardized language and method ofsharing model-based data, methods, and procedures across and between each member of a supplychain. (M)

– Shared, Secure Models – Develop information management methods enabling all members of thesupply chain to input to, access, and manipulate shared models in accordance with appropriate per-missions, with assured data security. Include the capability to provide a continuous audit trail of allactions and to automatically communicate changes to affected partners and personnel. (M)

– Extended Business Infrastructure Management – Develop modeling tools and techniques foridentifying, monitoring, and responding to internal and external forces acting on the supply chain.Include the capability to predict the different impacts of an event on each member of the supplychain. (M-L)

– Multi-Enterprise Estimating & Planning – Develop models and associated tools that supportmulti-enterprise estimating and planning for joint projects, with appropriate protection of the sensi-tive data of each team member. (M)



• Goal 2: Model-Based Teaming – Provide a modeling framework and tools for rapidly creating newsupply chain teams to exploit business opportunities. (M)

– Prequalification Model Framework – Develop a standard, web-based supplier qualification modeltemplate appropriate for any manufacturing industry type. Include data elements for process capa-bilities, capacity, current utilization and commitments, certifications, past performance, financial as-sets, and cost history. (S)

– Industry-Specific Extensions – Extend the standard supplier qualification model to support theunique requirements of specific industries, including definition of ranges of performance require-ments for factors such as tolerances, purity, turnaround time, quantity, regulatory/standards compli-ance, and similar criteria. (S)

– Extended Enterprise Modeling – Develop methods for modeling extended enterprises to pursueand execute defined business opportunities, including evaluation of team member contributions,value added, and capabilities to support supplier selection and teaming/partnering decisions. (M)

• Goal 3: Model-Based Extended Enterprise Management – Provide frameworks and standards thatenable integration of model-based technical and business systems to the lowest level of the supplychain. (L)

– Enterprise Multi-Model Integration – Develop a methodology to integrate different companies’enterprise models within the framework of an extended enterprise architecture, providing connec-tivity of interdependent operations including requirements management, product and process design,configuration management, manufacturing planning, cost estimating, scheduling, and performancemanagement. (M)

– Extended Factory Modeling – Develop methods, tools, and techniques for creating accurate modelsand simulations of the extended enterprise’s factory and for linking to current resource status infor-mation from different companies and sites. Include the capability to automatically query status, lo-cate extra capacity, identify and analyze constraints, and forecast requirements throughout the ex-tended enterprise. (L)

– Integrated Enterprise Logistics & Life-Cycle Support Modeling – Develop tools to model logis-tics requirements across the supply chain and invoke required actions to ensure that materials,equipment, and human resources are delivered to point of need to facilitate product tracking, supply,support, maintenance, repair, and return for reprocessing and recycle/reuse. Include analytical capa-bilities for problem problem-solving, tradeoff analysis, and predicting the impacts of decisions (in-cluding future technology insertions) at different points in the product life cycle. (L)

– Inverse/Reverse Manufacturing Modeling Tools – Develop modeling and simulation tools to aidin reverse engineering of products or components no longer supported by the original supplier (or forwhich the original supplier no longer exists), to support product life extension programs and helpmanage end-of-life concerns such as reprocessing, reuse, recycling, and disposal. (M)

3.3.4 VISION & GOALS FOR MARKETING, SALES, & DISTRIBUTION

Model-based tools integrated across all elements of the enterprise will be used to plan, manage, andexecute marketing, sales, and distribution functions with scientific precision, enhancing customer re-sponsiveness, mitigating risk, and maximizing profitability and return on investment.

Marketing & Sales

Modeling and simulation are key to the future enterprise’s ability to conceive and explore product oppor-tunities, generate demand, and fulfill customer needs. Model-based tools will enable product line manag-



ers and designers to forecast the likely impacts of different business options, and facilitating a proactiveresponse to events and trends in the marketplace. Future market modeling tools will extend current capa-bilities using technologies such as ontology-based knowledge mining to provide deeper insights intoavailable data, and provide different users with the specific data views they need through a single desktopor handheld interface. These tools will marry econometrics modeling with the growing body of contex-tual research on branding and its relationship to marketing effectiveness.

Marketing analysis systems will apply and refine purchasing behavior models segmented by demographicand econometric factors, providing an ever-deepening understanding of the enterprise’s customer base.Immersive modeling and simulation capabilities with intuitive, natural-language interfaces will increas-ingly enable companies to bring customers into the loop to better understand their needs, wants, and pref-erences, exploring and refining product concepts in the virtual realm before committing resources to pro-duction. These systems will be applied to enhance existing products and explore ideas for new and futureproducts, providing the capability to trade off performance and features versus costs and other factors tobest meet customer needs and wants with a single product or with variants that target different niches.

Future marketing management systems will monitor news feeds and corporate intelligence sources forlocal, regional, and global events and trends that impact the enterprise, its customers, its suppliers, or itsproducts. These systems will distill the resulting data into meaningful knowledge to guide research,product development, manufacturing, marketing, sales, and support. They will provide early warning ofemerging opportunities and competitive threats, and enable the enterprise to devise and evaluate the ef-fectiveness of different response options. Ubiquitous electronic connectivity to points of sale, distributionnodes, and news sources, exploited with intelligent data mining tools, will enable companies to accuratelymodel demand for products already on the shelf or in the production pipeline, calculate saturation levels,and identify windows of opportunity for customer incentives that maximize payback.

The sales environment of the future will rely strongly on science-based modeling coupled to real-timesales performance data to identify windows of opportunity, define sales force requirements (skills as wellas manpower), set and manage sales targets, continuously tune incentives to meet those targets withmaximum profit margins, and ensure ability to meet demand. Sales managers at local, regional, and en-terprise levels will use modeling and analysis tools to track daily, weekly, monthly, and yearly perform-ance, analyze trends, and explore the potential of different strategies to boost performance.

The marketing function will interface withthe engineering and manufacturing functionsthroughout the product development cycle,extracting the information it needs to supportinteractions with customers. The ability tosimulate the functionality and features ofdifferent design options will be a powerfultool for positioning products with discrimi-nating features that resonate with the cus-tomer.

Modeling and simulation capabilities at thepoint of sale will be powerful tools for en-gaging customers, allowing them to exploreproduct features and options and providing anon-confrontational milieu for triggering thepurchase decision (Figure 3.3.4-1). Custom-ers for many kinds of products will use the

18 Haptic Workbench photo ©2003 Scientific Computing and Imaging Institute at the University of Utah (www.sci.utah.edu). Haptic interface

device photos ©2004 Force Dimension, Lausanne, Switzerland. (www.forcedimension.com)

Figure 3.3.4-1. Immersive interface technologies will enablecustomers as well as engineers to manipulate models with

their hands instead of a keyboard. 18



model-based sales interface to interact directly with the factory, customizing exactly the product theywant based on what can be produced rather than being limited to only what is in inventory or the produc-tion pipeline.

Distribution

Future model-based distribution management systems will deliver products to point of sale and point ofuse through the most efficient means, forecasting and responding proactively to every fluctuation in geo-graphic demand. The ability to accurately model the ebb and flow of demand based on seasonal patterns,advertising and marketing outlays, and other factors that influence purchasing behaviors will enablemanufacturers to apply lean strategies, closely matching production levels to drawdown rates. This willenable enterprises to drastically reduce inventory carrying costs while preserving the ability to quicklysurge to meet upswings in demand. The cost of these tools will be driven down to the point that evensmall manufacturers will find the capability investments clearly advantageous to their bottom line.

Advanced modeling and simulation capabilities will enable companies of all sizes to analyze differentdistribution approaches for new or current products, optimizing for location, stock levels, choice of carri-ers, etc. and providing the ability to quickly respond to changes in the distribution environment – such asfluctuations in carrier capacity, changing fuel prices, and congestion of local and regional transportationroutes. These systems will also model complex financial factors including duties, taxes, tariffs and othercharges to optimize the design of international distribution strategies.

Increasingly widespread use of radio-frequency ID tags and similar sensors, coupled to cellular and satel-lite communications networks, will provide managers with real-time visibility of assets anywhere in thedistribution network, moving beyond present common-carrier tracking to enable immediate, precise loca-tion of any shipment anywhere in the delivery channel. Distribution management systems will collectthis information continuously, make it available to customer support systems, and compare actual to pre-dicted results to flag performance issues, explore solutions, and update the enterprise’s distribution proc-ess model.

Goals & Requirements for Marketing, Sales, & Distribution

• Goal 1: Market Assessment & Planning Toolset – Provide modeling tools that support rapid crea-tion and exploitation of market opportunities based on enterprise capabilities and a clear understand-ing of customer needs and wants, competitive factors, and other market forces. (L)

– Customer Requirements Analysis – Develop modeling and simulation tools that provide a com-plete capability for exploring and capturing customer needs and wants – both expressed and inferred– based on past experience, direct interactions, and market trends. Include the capability to trade offthe needs of multiple customers with diverse and conflicting requirements. (M)

– Opportunity Analysis – Develop model-based methods to evaluate business opportunities based onenterprise core competencies and trends in customer buying patterns, technology evolution, andmarketplace conditions. (M)

– Niche Market Customization – Develop capabilities for modeling and rapidly adapting product de-velopment, positioning, and production strategies to align with local, regional, and global eco-nomic/cultural trends and customer values. Include the capability to model the effectiveness ofproducts tailored for local and regional niche markets. (M)

– Market Entry Modeling – Develop generic, tailorable models to support evaluation of opportunitiesto enter new product markets. Include capabilities to analyze sales and ROI potential as a functionof capital investment level and timing; to model the impacts of new ventures on existing products,facilities, and enterprise resources; and to evaluate the costs and benefits of targeting specific niches(e.g., high-end vs. low-end) in the new market. (M)



– Market Tracking Methods – Develop market modeling systems that autonomously monitorchanges in marketplace trends and support analysis to refine product development, production, mar-keting, and sales strategies. (L)

– Qualitative Market Forecasting Tools – Develop modeling and simulation tools that use fuzzylogic, neural nets, and similar techniques to forecast market requirements based on non-quantitativefactors. (L)

– Market-Based Restructuring – Integrate marketing analysis tools with enterprise resource planningtools to support evaluation of business opportunities that require significant changes in enterprisecore competencies and technical/business capabilities. (M)

• Goal 2: Model-Based Sales Management – Provide fully integrated modeling capabilities for plan-ning and managing sales operations. (M)

– Sales Force Modeling – Develop applications for modeling sales force requirements, includingdemographics, skills, training, and staffing levels by region and location, to support introduction ofnew products and services and better manage sales staffing levels to respond to surges and lags incustomer demand. (S)

– Responsive Sales Performance Modeling – Develop applications that enable regional and localsales managers to quickly analyze options (e.g., promotions, buyer and sales force incentives, cus-tomer outreach) for boosting sales to meet or exceed targets. Include the capability to incorporatecompetitor attributes. (M)

– Build-to-Order Product Fulfillment – Develop point-of-sale modeling tools that allow a customerto interactively design a unique product from a range of options based on enterprise capability andtransmit the order directly to manufacturing for fulfillment. (M)

– Integrated Product/Service Modeling – Develop point-of-sale modeling applications that enablerapid creation of customized product/service offerings that facilitate selling customers not just a one-time product, but a lifelong service. (M)

• Goal 3: Real-Time, Responsive Distribution Management – Provide the capability to calculate op-timal product allocation to points of sale/use and staging nodes based on current need, rapidly deter-mine the most efficient means of distribution for new shipments, and interface with product trackingsystems to direct assets to points of need anywhere in the distribution network. (M-L)

– Design for Distribution – Develop modeling capabilities to optimize product designs for efficientdistribution via different transport modes, including long-range bulk shipment as well as local deliv-ery to point of sale. Include the capability to model approaches for final assembly at point ofsale/use and to model methods for optimization of performance and cost-effectiveness in protecting,storing, and transporting the product from origin to destination. (M)

– Integrated Distribution Modeling – Develop modeling and simulation applications that enableplanning and management of distribution requirements based on predicted and actual demand anddistribution network capabilities. Include the capability to automatically determine best deliverymethods and routes based on time, cost, and capacity factors; to analyze stock drawdown patterns toredirect product to demand points; and to identify opportunities to reengineer distribution channels toenhance performance and profitability. (M)

– Model-Based Product Tracking – Develop product tracking systems that enable continuous or on-demand location of products in the distribution network, with the capability to locate any asset toprecise GPS coordinates and automatically direct or redirect assets while updating any resultingchanges in the product distribution model. (S)



– Pull-Based Distribution – Develop modeling capabilities that enable the distribution system toautomatically respond to changes in demand by initiating shipments from inventory and reportingdrawdowns to the factory production planning system. (L)

– Special Materials Management – Develop applications to support modeling and planning for dis-tribution and tracking of radiological materials, hazardous chemicals, and other high-value/high-sensitivity products requiring special handling for safety, environmental, or security reasons. Includethe ability for the distribution model to interface with regulatory requirements databases, automati-cally detect any changes that affect the enterprise’s distribution strategies and mechanisms, and sup-port analysis to develop necessary changes (M)

3.3.5 VISION & GOALS FOR WORKFORCE MANAGEMENT

Job requirements modeling, human capability modeling, and model-based training will enable humanresource functions to proactively plan for near-term as well as long-term needs, ensuring readiness ofthe enterprise workforce to respond to changing requirements.

Future manufacturing enterprises will apply modeling and simulation technologies to maintain a clearunderstanding of current and future skill and manpower requirements and couple these needs directly tobusiness planning and operations systems to ensure readiness of the human resource pipeline to meetchanging needs. Job performance and human capability modeling will enable human resource functionsto clearly quantify the value of investments in education, skills, performance, and compensation for everyjob position; tune job structures to respond to changing business realities; and define career paths thatbetter prepare individuals to benefit the company as well as realize personal growth. The ability to modelthe impacts of changes in compensation and benefitsagainst industry norms will greatly improve control ofretention rates, preserving the ability to compete cost-effectively in the enterprise’s market sectors. Thesecapabilities will also enable the enterprise to clearlyunderstand and plan for the changes required to moveinto new areas of opportunity.

Workforce modeling tools will enable managers toquickly define requirements for increased and de-creased staffing, and trade off options for new hiresversus subcontract labor versus reallocation of existingstaff to realize the most cost-effective solutions thatmeet the talent need. These tools will also enablemanagers to understand and plan for new requirementsand priorities of the business environment in areassuch as safety and environmental compliance, ethics,diversity, and technical/business certifications.

Manpower planning tools tied to product, process, andresource modeling tools will enable program managersto accurately forecast the staffing levels, skill mix,ramp-up, and learning curve for new production pro-grams, and automatically feed these requirements tothe human resource system for allocation. These toolswill also tie into the financial systems of the enterpriseto support automated cost estimating, eliminatingmuch of the time and uncertainty associated with cur-rent labor costing practices.

The Changing Workforce As we move deeper into the Information Age, manyobservers believe that its impact on the manufac-turing workforce will be as great as that of the In-dustrial Revolution.

Workers now must be increasingly computer liter-ate, and need to understand and master a muchwider array of technologies than in the past. Theterm “knowledge worker” applies to more and morejobs as workers need to better understand informa-tion systems, decision support tools, knowledgebases, digital modeling and simulation, and com-puter-controlled equipment as part of their basicfunctions.

At the same time, workers at all levels are increas-ingly being called on to function as members of col-laborative teams, and thus need skills in interper-sonal relations and other non-technical topics thatenable them to function effectively in the manufac-turing cultures of the 21st century. Manufacturingline workers are no longer “task repeaters” simplymaking product, but serve as “process facilitators”who apply experience and knowledge to meet en-terprise goals.

As the 1950s paradigm of lifetime employment con-tinues to dissolve as companies downsize theirworkforces to compete, the ability to capture knowl-edge and experience in reusable forms is critical tolong-term survival. Integration of knowledge man-agement functions into model-based processes isthus key to future success.



Model-based technologies will enable workers to train more cost-effectively and receive training with fargreater fidelity than currently possible. Training functions embedded into enterprise process systems willenable users to train on demand to refresh current skills and acquire new ones. In many cases these sys-tems will monitor worker performance, enabling real-time identification of skill gaps for remediation.

Training functions will be fully integrated with the enterprise’s knowledge management systems, con-tinuously monitoring the underlying knowledge bases for changes that affect training content. Trainingsystems will link directly to product and process definitions maintained by engineering and operationsfunctions, enabling real-time update of configuration data used in training media. This will drasticallyreduce the cost of maintaining training content and eliminate much of the lag time between completion ofproduct development and completion of training development. This capability will also enable productand process designers to better understand the impacts of design choices on training functions.

Model-based training and qualification functions19 will be built into operations management systems.Every node in the operations model will provide links to related training content, and immersive simula-tion capabilities will enable personnel to train in 3-D virtual environments to acquire proficiency in indi-vidual processes and items of equipment. Most complex items of manufacturing equipment will comefrom the vendor with built-in and/or uploadable training functionality, supporting a mix of virtual andhands-on instruction as well constructive (instructor in the loop) training. Interfaces between the opera-tions management system and the human resources system will enable fully automated administration oftraining requirements, ensuring that personnel remain current with required certifications.

Goals & Requirements for Workforce Management

• Goal 1: Workforce Asset Modeling – Provide modeling tools that enable managers to assess thebenefits of human resource development investments and help enterprises develop and maintain theproper talent and manpower resource mix to support changing business requirements. (M)

– Human Attribute Representation – Develop standards for representing personnel information,skills, education, training, certifications and other human resource traits in models and simulations.Include the capability to link to enterprise knowledge bases to provide continuous insight into humanresource capabilities. (M)

– Unified Skills Standards – Develop and promulgate a standard definition, characterization, andmetrics that support modeling of all types and levels of skills in all manufacturing sectors. (S)

– Manpower Planning Models – Develop modeling tools that enable managers to accurately definemanpower requirements to support new programs, products, or business initiatives. Include the ca-pability to map requirements against available internal and external resources, identify gaps, and de-termine the optimum method to fill those gaps; and the capability to link to financial systems to sup-port cost estimating processes. (M)

– Skills Requirements Definition – Develop analytical tools to help determine the type and mix ofworker skills needed to establish new product lines or operations; new engineering, manufacturing,and support processes; or new business relationships. Include the capability to perform comparativeanalysis with similar past requirements. (M)

– Human Resource Investment Modeling – Develop tools to model the impacts on enterprise per-formance and capabilities of investments in human resources, including hiring, training, education,certification, and incentive programs (i.e., compensation, benefits, and reward mechanisms). Includethe capability to analyze labor outsourcing options and quantify the near-term and long-term impactsof outsourcing in terms of cost and competitive positioning. (M)

19 Goals and requirements for model-based training are addressed in Sections 3.3.5 and 3.3.7.



– Attrition & Retention Modeling – Develop tools to model the impacts of factors such as aging ofthe workforce, compensation/benefits positioning, and corporate culture on the ability of the enter-prise to recruit and retain the right mix of skills and experience to meet current and future businessneeds. (M)

• Goal 2: Model-Based Workforce Training – Provide modeling and simulation tools that supporttraining for different classes of products and processes, adapt for all levels of user, and support busi-ness environments characterized by continuous change. (L)

– Model-Based Training Requirements Definition – Define types and levels of training needs fordifferent classes of products (military and consumer, electronic and mechanical, etc.) and differenttypes of engineering and manufacturing processes. Include requirements for both formal and ad-hoc/on-demand training. (S)

– Model-Based Embedded Training – Develop embedded training concepts and supporting model-ing and simulation tools in collaboration with industry/government users, academia, and trainingtechnology vendors. Include the capability for users to receive training on demand and for processsystems to monitor individual performance issues and identify skill gaps for remediation in-processor off-line. (M-L)

– Real-Time Linkage to Product/Process Models – Develop methods and protocols to link productand process data and representations contained in training media directly to the configuration-controlled product and process baselines. (M)

– Universal Training Modules – Develop a suite of standard, computer-based training modules,spanning the complete spectrum of manufacturing functions, that can be plugged together to providejob-specific training and instruction to any worker in any industry. Include the capability for em-bedded advisory systems to “call up” and integrate sets of training modules and automatically tailorthe content and depth of instruction in response to user feedback. (L)

3.3.6 VISION & GOALS FOR CAPITAL ASSET & INVENTORY MANAGEMENT

Modeling and simulation tools will enable future enterprises to manage their capital assets and inven-tories with extreme precision and efficiency, ensuring that whatever is needed will be where it’sneeded, on time. This will drastically reduce the need for maintaining excess capacity and inventory tomeet changing business requirements.

The processes and operations of future enterprises will be powered by model-based business systems thatprovide continuous, precise visibility of capital asset and inventory requirements for manufacturers of allsizes. Intelligent resource management models linked to ERP/ERM systems will monitor the enterprise’smarketing, sales, and distribution systems and external information sources to accurately predict near-term and long-term variations in product demand by region and locality, automatically recalculating re-quirements and redirecting inventory at the enterprise level and at operating sites. The system will enableproduct managers and operations managers to understand, with a high degree of confidence, what re-quirements are coming the next day, week, month, and year. It will enable them to quickly evaluate thepros, cons, and deeper implications of all options for responding to those requirements. More importantly,the system will enable them to re-plan quickly as requirements change and as new opportunities andchallenges arise.

Ubiquitous use of bar coding, radio-frequency tags, and other tracking technologies will enable everyproduct to be tracked from origin to point of sale, providing the real-time information that model-basedinventory management systems need to monitor demand, adjust manufacturing throughput, and directdistribution networks to ensure the right makes, models, and styles of product reach customers on time inevery market.



The system will monitor all indicators that directly and indirectly influence demand – local and regionaleconomic trends, weather, new product introductions by competitors, and the like – and enable enterprisemanagers to quickly identify issues, evaluate options, and determine the best response.

Model-based planning systems will also enable companies to more efficiently manage their capital assets.Life-cycle models supplied with every item of capital equipment, or developed in-house for existingequipment, will enable facility planners to understand the capability and capacity limits of their factoryassets, and make the best decisions when trading off options to meet changing requirements. The modelswill help managers determine the right kind of equipment to acquire, and project when the item will needto be upgraded or replaced to keep pace with production needs. Real-time monitoring of equipment per-formance will feed data to factory health models, enabling continuous visibility of equipment life andkeeping managers apprised of when assets will need to be replaced.

These capabilities will reduce outlays of capital (and associated financing costs) by providing more op-tions to solve capability and capacity needs through leasing and subcontracting, as well as by enablingmanagers to optimize capital facilitization strategies for the highest return on investment.

Model-based facility planning advisors will also interface with the enterprise’s information systems tomonitor the competitive and regulatory environment, providing clear visibility of issues that impact capi-tal equipment and facility decisions.

Goals & Requirements for Capital Asset & Inventory Management

• Goal 1: Dynamic Asset Modeling – Provide asset management systems that integrate high-fidelitymodels to manage capital equipment and facilities across their useful life and support evaluation ofappropriate responses for addition, modification, replacement, and retirement of equipment and facili-ties. (L)

– Asset Definition Modeling Standards – Develop standards for creation of object and life-cyclemodels for different types and classes of capital equipment and facilities, which support integrationinto business and facility planning models. Provide the capability to include key factors such as ca-pacity (throughput, sizing, tolerances, etc.), life expectancy and life-limiting factors, and growth ca-pability to support higher performance levels, expanded functionality, or changeover to support newrequirements. (S)

– Automated Asset Monitoring & Condition Prediction – Develop techniques for predicting the lifeexpectancy of capital assets based on feedback from sensing systems that monitor wear, frequency ofmaintenance/repair, and changes in performance over time. Include the capability to simulate the ef-fects of stressing conditions (e.g., extended operation at limits of capacity) to support contingencyplanning. (M-L)

– Asset Alternative Modeling – Develop methods to integrate asset information and knowledgeacross the enterprise, including its suppliers and partners, to enable rapid evaluation of solution op-tions for fulfilling a capital asset requirement. Include the capability to define the margins of capa-bility and financial impacts for each option for a given time span, including factors such as availabil-ity of capital funds, return on capital, funds flow, and ability to meet surges in demand. (L)

– Regulatory Impact Modeling – Develop techniques for modeling the impact of changes in regula-tory requirements on capital equipment and facilities. Include the capability to automatically moni-tor information sources for issues relative to process and facility emissions (air and water discharges,noise), safety standards, and similar factors that potentially dictate modification, replacement, orshutdown of capital assets. (L)



• Goal 2: Intelligent Inventory Modeling – Provide modeling tools that monitor sources of inventoryrequirements change and aid users in defining and implementing optimal responses to change. (L)

– Adaptive Inventory Modeling Applications – Develop generic inventory modeling applicationsthat can be readily adapted to specific industry sectors and different supply chain roles (i.e., OEM,major subcontractor, supplier). Include the ability to integrate products having widely varying in-ventory characteristics, easily add new products to the model, and interface with distribution plan-ning and management systems. (M)

– Inventory Modeling Information Interface – Develop interface solutions enabling inventory man-agement systems to acquire and continuously update all information that impacts inventory require-ments, including direct factors such as orders, sales, and market trends, and indirect factors such aseconomic forecasts, weather, and governmental actions (e.g., changes in regulations). (M)

– Capacity Management System Interface – Develop methods for interfacing inventory modelingtools with factory management systems to enable calculation of factory impacts resulting fromshifting production demands. Include the capability to interface with supply management systems toensure just-in-time provision of raw materials, components, labor, and other assets required to fulfillproduct demand. (L)

– Automated Demand Prediction – Develop modeling tools able to evaluate variables that impactproduct demand over time, and accurately forecast inventory requirements for all makes, models,and styles of product. Include the capability to extrapolate demand trends for new product introduc-tions based on initial orders and sales, and the ability to model the demand impacts of disruptiveevents such as strikes, natural disasters, political upheaval, or introduction of competing products.(L)

3.3.7 VISION & GOALS FOR KNOWLEDGE/INFORMATION MANAGEMENT

Model-based systems will revolutionize manufacturing enterprise productivity and effectiveness bydrawing on totally interconnected information resources to ensure the right knowledge is available asneeded to the right people and processes.

Model-based corporate information systems linked to live data sources and automatically filtering infor-mation feeds for significance will be the ultimate “power tools” for the future manufacturing enterprise.Companies will operate from an ever-growing knowledge base that contains or automatically searches outall information needed by any of its functions, from concept definition to engineering, production, andlife-cycle support (Figure 3.3.7-1). All of these functions will rely on science-based modeling and simu-lation tools not only to develop optimum solutions to their requirements, but also to continuously tunetheir processes for optimal performance based on all information and knowledge available to the enter-prise.

The value of model-based processes will depend in part on the ability to develop models and simulationsthat are equipped with the accurate, complete information essential to obtain valid results, and on theability of models and simulations to recognize when supplied information is inadequate to calculate a re-sult with the necessary degree of confidence. The model-based systems of the future will possess suffi-cient intelligence to understand what inputs they require to fulfill their functions, and will interact withthe enterprise’s information resources to acquire the data they need to perform their functions, with hu-mans in the loop to interpret results and provide the necessary checks and balances.

Current barriers posed by capture of data in proprietary formats will dissolve as market demands forceapplication vendors to support open standards, and as the vendors expand their market share by support-ing application integration across different domains (e.g., CAD with PDM, PDM with ERM/ERP, and



ERM/ERP with financial systems). The current migration of corporate information services toward cli-ent/server architectures where “thin client” systems host all applications and data on servers (as opposed

to desktops) will make all enterprise knowledge and data readily available to the individuals, the proc-esses, and the supporting modeling and simulation systems needing it. This will also drastically reduceredundant data entry andthe occurrence of multi-ple conflicting versionsof the same information.

The systems that powerthe enterprise’s model-based processes willdirectly access externalknowledge repositoriesshared across all of in-dustry or within an in-dustry sector or a supplychain relationship (Fig-ure 3.3.7-2). Domain-specific repositories willevolve over time intomulti-domain reposito-ries with many types ofinformation available to all users and directly accessible by model-based tools. Standards organizationswill ensure that shared tools, models, and information resources meet well-defined criteria for validation,verification, and interoperability.

Figure 3.3.7-1. Model-based integration provides an elegant solution to information managementchallenges, going beyond current middleware and message broker technology to enable true

plug-and-play interfacing of enterprise processes and systems.

Figure 3.3.7-2. Future manufacturing enterprises will rely on a transparentlysharable reservoir of data and knowledge to integrate processes, operations,

facilities, and partnering relationships.



Goals & Requirements for Knowledge/Information Management

• Goal 1: Model-Based Knowledge Integration – Provide methods and tools enabling capture, stor-age, and usage of technical and business information resources by model-based systems. (M)

– Multi-Functional Knowledge Representation – Develop methods for storing information and datain formats that transparently support different modeling and simulation applications for engineering,planning, manufacturing, product support and other enterprise functions. (M)

– Common Concept Representation – Develop standard representation schemes for common prod-uct, process, and support concepts used within and across multiple industry sectors, to enable trans-parent sharing and exchange of model-based knowledge. (M)

– Multimedia Integration – Develop techniques and standards for integrating information of diversetypes (text, graphical, sound, tactile, etc.) in formats that enable seamless access by modeling andsimulation applications. (M)

• Goal 2: Standard Modeling & Simulation Architectures – Define standard formats and informa-tion requirements enabling creation, interoperability, and quality assurance of common types of mod-els and simulations. (M)

– Common M&S Architecture for Product Models – Develop standards that define the basic infor-mation content, data quality requirements, and external hooks and interfaces that support creation,application, and maintenance of models and simulations for major classes of manufactured products.Categories to be addressed include mechanical, electronic, electromechanical, feedstocks, chemicals,agricultural, and textiles. (M)

– Common M&S Architecture for Technical Process Models – Develop standards that define thebasic information content, data quality requirements, and external hooks and interfaces that supportcreation, application, and maintenance of models and simulations for engineering, manufacturing,and other technical processes. Categories to be addressed include material properties, unit processes(machining, forming, etc.), chemical processes, assembly processes, quality assurance (inspectionand test) manufacturing flow, and sensing and control. (M)

– Common M&S Architecture for Business Process Models – Develop standards that define the ba-sic information content, data quality requirements, and external hooks and interfaces that supportcreation, application, and maintenance of models and simulations for major business processes.Functions to be addressed include enterprise resource planning, finance, supply chain management,strategic planning, capital asset management, inventory management, distribution, customer relation-ship management, and product support. (M)

• Goal 3: Model-Based Knowledge/Skills Management – Provide modeling and simulation tools andtechniques to enable a growing store of corporate knowledge plus an integrated training and certifica-tion environment that transforms manpower assets into capable, qualified skill/knowledge workers. (L)

– Model-Based Training – Develop concepts and approaches enabling creation of training systemsand media that automatically capture as-designed, as-built, and “as used” product and process dataand update the models used for training workers and users. Include the capability to feed backworker/user experience to refine the training models and document opportunities for product/processimprovement. (M)

– Tacit Knowledge Capture – Develop methods to capture enterprise knowledge and lessons learnedin forms that allow it to be accessed by, and applied to enrich, model-based technical and businessprocesses. (M-L)



– Embedded Simulators – Develop capabilities and standards for embedding training and user sup-port simulations into enterprise management and control systems (and into products that are of suffi-cient complexity to warrant embedded functionality). Include the ability to support factory opera-tions, customer service, engineering, and other major functions of the manufacturing enterprise. (L)

• Goal 4: Model-Based Enterprise Knowledge Repositories – Establish manufacturing knowledgerepositories that support transparent data acquisition by modeling and simulation applications. (M-L)

– Model Baseline – Survey, compile, and establish an internet-accessible database of validated prod-uct and process models and related information available from industry, government, and academicsources. Acquire copies of these models to populate the repository and establish linkages to enableupdating as needed. (S)

– Model Validation Methods – Define standard methods for testing models and simulations to verifytheir accuracy and document, within the model or simulation, their limitations and bounds of uncer-tainty. (M)

– Uniform Data Formats – Define standard data formats for storing information in ways that enabletransparent access by modeling and simulation systems. (S)

– Protection of Sensitive Knowledge – Provide means of ensuring proprietary or sensitive data con-tained in or accessed by product and process models is accessible only to applications and users en-titled to that data. (M)

– Knowledge Scouts – Develop mechanisms to monitor external databases, news feeds, and otherreal-time information sources, recognize when knowledge is potentially relevant to the enterprise,and integrate it into the repository for use by modeling and simulation applications. Include the ca-pability to reconcile conflicting data (both autonomously and with humans in the loop) and providean audit trail of all updates. (M-L)

• Goal 5: Information Delivery to Point of Use – Provide the capability to create, manage, and deliverrequired information to the point of need for use by model-based systems and human users. (L)

– Authoring of Planning Information – Develop a model-based integrated information frameworkand authoring tools that enable authoring of information from enterprise planning processes (design,scheduling, etc.) with the appropriate view and presentation style needed by different enterprisefunctions over the life-cycle of the product, process or business function. (M)

– Publishing & Distribution of Planning Information – Develop means of managing informationfrom planning functions in a highly functional, vendor-neutral format (e.g., XML) that is compatiblewith ERP/ERM and manufacturing execution systems. Include the capability to publish the infor-mation in different formats such as discrete pages, executable animations, etc. that are required fordifferent tasks and to facilitate real-time delivery of extremely large files. (M)

– Point of Use Information Delivery Devices – Develop technologies to enable hands-free, wirelessinformation delivery with intuitive navigation and human interface, with emphasis on human factorssuch as safety and ergonomics. (S-M)

– Task-Appropriate Information Access – Develop information schemas and control mechanismsthat provide easily navigable, real-time access to all needed information from enterprise models andother sources, with appropriate security. (M)

– Integration of Legacy & External Information – Develop techniques for integrating and main-taining enterprise legacy information suitably along with new/current information. (M-L)



3.3.8 VISION & GOALS FOR TECHNOLOGY MANAGEMENT

Future planning and decision processes for technology management will apply model-based tools toprovide a clear vision of technology direction, maintain continuous visibility of emerging technologieswith significant – and potentially disruptive – impact, and enable managers at all levels to guide theirtechnology investments for maximum cost-effectiveness, competitive benefit, and return on investment.

In the future, robust technology forecasting and planning tools will support systematic management oftechnology decisions and activities in all manufacturing sectors. Technology planning models linked tothe enterprise knowledge base and external surveillance systems will enable managers to quickly identifyand analyze science and technology advances that relate to their enterprise’s product lines, core compe-tencies, operations, and strategic plans. The surveillance systems will “troll” the Internet and accessibledatabases for new information on patent awards, R&D and product announcements, changes in customerrequirements (e.g., pending regulatory changes) and other sources of technology news. Results will bemapped in real time against the enterprise’s defined technology interests, scored for degree of interest andapplicability, and routed to responsible managers and staff members.

The technology management system will enable users to analyze the potential impacts of new technologyadvances in many ways. It will model technology maturation timelines, enabling prediction of when thetechnology will be ready for initial implementation, and when it will supplant current technology as acompetitive preference and as the industry standard. It will evaluate what elements and operations of theenterprise will be impacted, enabling direction and timing of capital investments to implement the newtechnology at the right time based on competitive and cost/benefit factors. It will also enable managers todecide when to reduce or terminate a technology investment due to changes in ROI predictions; emer-gence of a better technology; or a change in business strategy (e.g., a decision to exit a particular productsector) that eliminates the technology need.

The system will enable far more effective day-to-day management of technology investments by moni-toring performance indicators for ongoing R&D programs and by updating metrics for technology readi-ness, risk, and return whenever underlying information changes.

Goals & Requirements for Technology Resource Management

• Goal 1: Model-Based Technology Surveillance – Provide autonomous surveillance capabilities thatenable enterprises to capture relevant information about technology developments having potentialbeneficial or adverse impact to current and future products, processes, facilities, and operations as de-fined in enterprise business models. (M)

– Autonomous Technology Data Acquisition – Develop technologies enabling automated search ofweb-accessible databases for information on technologies of interest and correlation of retrieved in-formation against specific areas of interest to, or having potential impact on, the enterprise. (S)

– Intelligent Semantic Search – Develop automated analysis technologies to perform “deep search”of internet-accessible databases and information sources based on semantic content, enabling users toobtain search results that correlate directly to technology topics of interest, with zero redundancy andno false alarms (i.e., no non-relevant matches). (M)

– Automated Technology Needs Definition – Develop technologies enabling rapid creation of enter-prise-specific technology needs definition from multiple sources including documented strategicplans and product/process/facility/business models maintained in the enterprise’s knowledge base.(M)



• Goal 2: Technology Management Modeling – Provide integrated modeling tools to support analysisand prediction of technology maturation timelines and impacts. (M)

– Technology Timing Analysis – Develop modeling tools enabling forecasting of maturation time-lines for specific technologies based on historical trends, market conditions, driving factors (e.g.,regulatory compliance milestones and customer-directed capability targets), and known risks. In-clude the capability to support automated technology readiness level (TRL) and technology ad-vancement risk assessments through surveillance of internet-accessible information sources. (M)

– Technology Impact Assessment – Develop tools and techniques supporting accurate evaluation ofthe impacts of a new or emerging technology, including effects on competitive position, product andprocess life spans, and capital facility investments. (M)

– Technology Investment Decision Support – Develop capabilities for automated analysis of com-peting options for technology selections (including factors such as capital requirements and ROI,timing, and competitive impact) and documentation of results in a form that readily supports invest-ment decisions. Include the capability to support make/buy tradeoffs in acquisition of technical ca-pabilities and to provide real-time update when changes in underlying data or factors impact thebusiness case for a particular technology investment. (M)

– Technology Insertion Modeling – Develop modeling capabilities enabling identification of the op-timal insertion point for application of new technologies in enterprise products and processes. In-clude the capability to evaluate the risks and returns of insertion at different points in the prod-uct/process life cycle with respect to impact on competitive position, sales and profits, and return oninvestment. (M)



3.4 ROADMAP FOR ENTERPRISE RESOURCE MANAGEMENT

The following pages lay out a nominal project plan for technology development to achieve the NGMTIgoals for Resource Management. The schedule is based on a January 2006 start, and the spans allocatedfor the defined activities are based on a convention where each activity is targeted for completion in aShort (0 to 3 years), Medium (3 to 5 years), or Long (5 to 10 years) timeframe.

This project plan is intended as a reference point of departure for detailed planning purposes. Refinementof the schedule is dependent upon allocation of funding, assignment of responsible organizations, anddevelopment of detailed statements of work and project plans to accomplish the individual tasks. Furtherdetail on specific Resource Management projects proposed for near-term implementation is provided inthe NGMTI white papers for Information Delivery to Point of Use, Enterprise-Wide Cost Modeling,Model-Based Resource Management, and Multi-Enterprise Collaboration. These documents are avail-able in the NGMTI Communities of Practice at http://www.ngmti.us.


Strategic Management 4-1

4.0 STRATEGIC MANAGEMENT

This document explores Strategic Management functions in the manufacturing enterprise and lays thefoundation for creation of a model-based environment that supports those functions to achieve an efficientand responsive business entity. Strategic management encompasses those operations of the enterprise thatare associated with positioning the company for success, both near- and long-term. It overlays and guidesthe core functions of designing, producing, and supporting products, and managing enterprise resources.

Establishing a clear view and a plan for success in the manufacturing sector is a difficult challenge be-cause of the increasing complexity and quantity of data inherent to the modern business environment.Although tracking of performance metrics and health indicators is routine today, it is difficult to monitorall the external forces and factors that can cause disruptive impacts. And, while corporate intelligencefunctions can provide early warning of problems, executives have few tools available to help analyze im-pending or fast-breaking problems, accurately predict possible outcomes, and make and enact the bestpossible decisions.

It is also a major chal-lenge to make neededdata accessible in a use-ful form. For executives,what is needed is a con-tinuous, intelligent win-nowing of the waterfallof real-world data intofocused, summarized,factual information thatis filtered into custom-ized views (Figure 4-1)and coupled with model-based tools for analysis,prediction, and problem-solving.

In the NGMTI vision ofthe model-based enter-prise, all aspects of en-terprise operations arerepresented in a rich andever-changing mastermodel that is continu-ously fed with informa-tion from internal andexternal sources. Thismodel reflects the enter-prise and its processesand interactions with the world, both in physical operations (e.g., development, manufacturing, and sup-port) and in setting and maintaining long-term corporate direction (i.e., strategic operations). Diverseviews are formulated through filtering and analysis of pertinent information to meet the needs of all func-tional users, supporting the different needs of the Chief Executive Officer (CEO), Chief Financial Officer(CFO), Chief Operations Officer (COO), Chief Technology Officer (CTO), and other executives.

Figure 4-1. Effective strategic management demands continuous, intelligentgathering and refinement of the mass of real-world data into focused,

summarized, factual information filtered into customized views.



4.1 FUNCTIONAL MODEL FOR STRATEGIC MANAGEMENT

Strategic management (Figure 4.1-1) includes all activities required to enable a company’s leadershipteam to guide the enterprise based on current, complete, accurate information, and best decisions. For-mulating the optimal strategic directions and making the right decisions is critical, as the decision out-comes at this level carry longer-term and greater cost, risk, profit, business health, and survival ramifica-tions for the enterprise. Strategic management is discriminated from enterprise resource management(discussed in Section 2) in that strategic management is not specifically concerned with the enterprise atan operational level, or within a short time window, although the two functions are certainly interrelated.

Figure 4.1-1. Functional Model for Strategic Management of the Model-Based Enterprise

There are five distinct elements within Strategic Management:

1. TECHNOLOGY PORTFOLIO MANAGEMENT includes all activities related to technologicalcapabilities and intellectual property of the enterprise. These activities include acquisitions, merg-ers, and strategic collaborations needed to acquire the technology and to direct funds needed to de-velop new technology for future competitive advantage.

2. FINANCIAL & CAPITAL ASSETS MANAGEMENT includes all activities related to protect-ing the enterprise’s investments by optimizing capacity. This includes allocating capital to imple-ment required improvements, sustain levels of capability, and ensure future financial viability; de-termining the optimal distribution of capital; and maintaining appropriate levels of capital, equity,and debt.

3. KNOWLEDGE MANAGEMENT & APPLICATIONS includes all activities involved in thecreation, maintenance, and use of the internal and external information assets of the enterprise. Thisincludes identifying information needs; determining how to acquire, manage, control, and distributeinformation; and managing the various knowledge resources available to the enterprise.

4. STRATEGIC PLANNING & EXECUTION includes all activities associated with developingand implementing the enterprise’s strategic objectives, aligning core competencies with marketneeds, and ensuring the enterprise is properly positioned and equipped to address future opportuni-ties and challenges.

5. STRATEGIC OPERATIONS MANAGEMENT includes all activities related to developing andimplementing the tactics needed to achieve the strategic objectives of the business. This includesbenchmarking against competitors; implementing corporate consistency across all enterprise opera-tions; evaluating the productivity, capacity, and efficiency of production facilities; all activities re-lated to establishing the business’s market presence; and positioning the business’s product lines.



4.2 CURRENT STATE ASSESSMENT FOR STRATEGIC MANAGEMENT

A corporation cannot realize the full potential and value of its assets without understanding how individ-ual programs, projects, operations, and resources fit into the holistic view of the present and future busi-ness environment. Today, executives and managers work together to assure that the company performs toits highest standards, is ready to exploit upcoming opportunities, and is able to respond quickly and cor-rectly to new challenges. Although information technology is giving executives access to a wealth ofdata, and practices such as monitoring of key performance indicators are improving the ability to monitorbusiness health, tools that deliver the knowledge needed to maximize enterprise potential remain sorelylacking.

Several themes are common across all executive management functions. Currently, parameterization ofvariables and data collection required for building business models is performed manually or semi-automatically. This consumes significant organizational resources and typically delivers static modelshaving limited utility and depth, and lacking real-time connectivity to the underlying sources of data.However, modeling technology is improving and software tools for automated text and data mining arematuring. These emerging toolsets will provide a new capability to assure that information is available tomake the best long-term decisions. This in turn provides a more solid foundation to ensure that the beststrategies are in place for enterprise success.

STRATEGIC MANAGEMENT APPROACHES

In today’s world, most manufacturing corporations are managed from the top down and operated from thebottom up (Figure 4.2-1). Executives set direction and drive that direction down through the organiza-tion. The quality of direction varies with every manager in the organizational chain, making consistencyone of the biggest problems in achieving a strong and responsive corporate culture. The corporate man-agement strategies that affect the entire organization are seldom visible below the top levels, and are typi-cally communicated in generalities to line employees. In normal circumstances, the factory simply oper-ates and product is produced based on weekly or monthly schedules. Except in very small companies,senior management has little insight into the detailed workings of the enterprise as a whole until or unlessa serious problem arises.

From the top down, strategic direction is driven by the enterprise’s mission and vision. These define whythe company exists and what it wishes to accomplish. Upon this foundation, the company architecture ofbusiness units and organizational elements is fit together. The core values and mission generally remain

Figure 4.2-1. The two approaches to enterprise management each have unique strengths – and weaknesses.



stable over time, while the organizational vision and structure evolve to respond to perceived changes inthe needs and wants of the enterprise’s stakeholders (customers, stockholders, etc.) as well as shifts in thecompetitive landscape.

The top-down approach has deficiencies. The process of strategic management is frequently implementedusing corporate off-site retreats, strategic planning meetings, and emergency sessions to correct seriousproblems. These processes typically permeate down through the entire organization, with each organiza-tional element responding reactively to commands filtered down from the levels above. In this environ-ment, major changes are disruptive events that are typically kept concealed from the rank-and-file of theorganization. This has bred corporate environments where changes in strategic direction are heavily “spindoctored” and are usually greeted with a high degree of skepticism by the affected employees.

The bottom-up approach usually is successful in getting the product out the door. Creative workers canfind a way to make the product, while mistake-proofing techniques have made engineered solutions andstructured performance commonplace. However, there is still much room for improvement. What hap-pens when problems are encountered on the shop floor? In most cases the foreman instructs the workersto fix the problem. Often the incentives are all focused on getting product out at all cost, and not focusedon anticipating and eliminating problems. There are many stories of small problems at the factory floorlevel that were “fixed” to avoid missed schedules, resulting in costly failures. The Department of Energyuses an occurrence reporting system to avoid exactly these kinds of problems in manufacturing for thenuclear stockpile. In their facilities, the contractors are obligated to report all deviations from normalpractice. Standard measures are in place to categorize occurrences, and specific corrective actions aremandated by the categorization. There are parallels in industry.

The following sections provide an overview of the current state of practice and technology issues for eachelement of the Strategic Management functional model. Table 4.2-1 (see following page) provides asummary-level view into a number of specific areas of concern to executives.



Table 4.2-1. Current State Assessment for Strategic Management


TechnologyPortfolioManagement

• Technologies not identified as corecompetencies; company just“makes things”

• Acquisition and hoarding of tech-nology to protect existing productlines and revenues

• Limited focus on patents that im-prove core competencies

• No concept of technology portfoliomanagement

• Technologies managed primarily to improvefinancial returns

• More companies managing technology assets inglobal market context, struggling to address in-ternational intellectual property (IP) issues

• Strategic partnerships sought to complementcore capabilities (distribution, manufacture,service, etc.) and add to resource base withoutcapital investment

• Technology needs and investments fully inte-grated into strategic plans; long-term tech re-fresh strategies integral to product visions(e.g., defense sector)

• Increasing reliance on new technology pulledfrom lower tiers of supply chain

• R&D very clearly focused on keeping productsat leading edge of market segments

• Formal risk management integrated into tech-nology planning and management processes

• Pricing strategies focus on ensuring R&D costrecovery within opportunity windows (e.g.,pharmaceuticals, aerospace)

Financial &Capital AssetsManagement

• GAAP-based accounting systembased on analog reporting; not agood fit to our digital world; slow,cumbersome, very manual

• Over-reliance on improving thebottom line for shareholder valueat expense of future growth

• Belief that short-term managementof capital is good management;e.g., making decisions on short-term amortization of capital equip-ment vs. long-term investments

• Companies hamstrung by short-term view ofcorporate success – performance tied to 10-dayforecasts, 10K and quarterly reports to SEC,weekly stock price

• Financial management tools lack single-pointdata entry afforded by integrated systems

• Real options evaluating using financial com-puter-aided design (CAD) tools such as Black-Scholes model or Monte Carlo simulations

• Electronic data interchange is widespread, yetcomplex and inflexible

• Return on X (investment, sales, capital, etc.)analysis drives decisions; net present valueanalysis dictates mergers, major projects

• Business unit models primarily standalone

• Financial goals and objectives tied to long-term corporate strategic direction

• International standardization of business ap-plications using XML and as Extensible Busi-ness Reporting Language (XBRL)

• Real options evaluating using financial CADtools such as Black-Scholes model or MonteCarlo simulations; cross-cutting financialmodel used to optimize use of capital equip-ment capacity and capital investment

• Strategy mapping process used to define andcommunicate causal links among differentcomponents of company strategy

• FIFO and averaging inventory tools give morerealistic assessment; multivariable testingused by BASF, DuPont, and others

KnowledgeManagement &Applications

• Paper-based systems to captureand exchange information

• Ad-hoc information exchange be-tween companies

• No effort to capture human as-pects of information management,especially capturing expertise andexperience

• Lack of integration of communica-tions systems

• Digital storage and transfer of information iscommonplace

• Information systems not well integrated• Electronic data exchange between companies is

still cumbersome; standards (e.g., EDI, FIX fi-nancial info exchange protocol, XML) do notcover all scope needed

• Many companies require suppliers to acquireand use specified software systems

• Integrated information systems with extensiveuse of standardized digital tools

• Internal Information Technology (IT) organiza-tions dictate all hardware and software stan-dards (e.g., "managed desktops”)

• Communications integrated into supply chainsand distribution networks

• Global communication capabilities and enter-prise-wide networks used to strategic advan-tage




StrategicPlanning &Execution

• Market-reactionary pursuit of im-mediate orders or current productfads

• Limited product focus• Limited understanding of how the

organization adds value with itsproduct offerings

• Opportunity analysis using quantitative andqualitative research methods to explore trends,customer needs, competitive landscape

• Market modeling fragmented, based on incom-plete data; statistical analysis packages andspreadsheets used for regression and clusteranalysis

• Common value propositions are price leader-ship, product performance edge, complete cus-tomer solutions, and system lock-in

• Future forecast model relies heavily on identi-fication of technology that is strategic for cor-poration to acquire or develop (e.g., GM withfuel cells)

• Continuous trend analysis used to frequentlyrefresh and guide planning

• Model-based brand management, with modelfor each brand (P&G, auto manufacturers)

• Bootstrapping of derivative products to under-cut competitors and capture new revenuestreams (e.g., Microsoft)

StrategicOperationsManagement

• Duplication of activities andequipment across different busi-ness units and sites

• Inefficient, reactionary use of ca-pacity

• Poor forecasting means companycannot deliver products to custom-ers when they want it (due to lackof surge capability)

• Ineffective training results hindersproductivity improvement and im-plementation of change

• Management by crisis, not analysisand prevention

• Lacking or inconsistent perform-ance measurement; key indicatorsnot understood, resulting in arbi-trary objectives

• Practices benchmarked against best in industryto guide improvement initiatives

• Lean, Six Sigma, statistical process control(SPC), and similar techniques used to improveand ensure performance

• Increased outsourcing/”offshoring” to reducelabor costs and capital expense

• ERP/MRP/ERM systems used to manage op-erations

• Balanced scorecard, key characteristic (KC),and key performance indicator (KPI) techniquesused to set and manage performance againsttargets

• Lean principles enhanced by future forecast-ing used to manage capacity and utilizeequipment and workforce

• Pull-based manufacturing based on demand,rather than “push”

• Staff are cross-trained in up-to-date operationsmethods

• Activity-based costing (ABC) techniques usedto understand cost at detail level

• Leaders take information gathered through allthe processes and analyze it for strategic op-portunities and productivity improvements

• “Executive cockpits” provide continuous visi-bility of many performance indicators(throughput, availability, etc.)

• Better companies always refreshing theirtechnology to improve performance, profitabil-ity, and competitive advantage



4.2.1 CURRENT STATE ASSESSMENT FOR TECHNOLOGY PORTFOLIO MANAGEMENT

WHO'S LOOKING AND WHAT DO THEY SEE? Technology portfolio management is a shared responsibility. Ifan issue concerns assurance of technological excellence and methods of acquiring it, the Chief ExecutiveOfficer and the Chief Technology Officer are likely the hub of the decision process. This function requiresan honest view of the company’s technical capabilities, continuous awareness of the competitive land-scape, and the ability to quickly identify and respond to emerging opportunities and threats.

The technology portfolio of an enterprise includes its technical know-how and the skills of its staff; itsphysical assets such as specialized equipment and facilities; and the more important intangible assets ofintellectual property. Technology portfolio management involves those actions that an organization takesto ensure that its technology base is in place and ready to perform when opportunities and challengesarise, both anticipated and emergent. It includes the acquisition, development, and exploitation of techni-cal capabilities (process as well as product-related), and also includes the divestiture or discarding oftechnical capabilities that no longer align with the direction of the enterprise.

The technology portfolio is the engine of the corporate enterprise, and a company that is not managing itstechnology assets in a focused and efficient way, is driving blind. Good strategic management processesempower a company to systematically apply the assets of the technology portfolio to create, sustain, andgrow revenue streams while balancing risk and cash flow. However, today there are few tools (asidefrom financial models) available to help companies model their technology assets and make optimal deci-sions with respect to those assets.

Recent trends have resulted in major shifts in strategies for technology portfolio management, and havegreatly heightened this aspect of strategic management. Globalization pressures and stockholder demandsfor quarterly performance have forced companies to take a hard look at their technological investmentsand strategies. Many companies now manage their core competencies and competitive positions in dif-ferent market segments by simply buying and selling each other’s business units – buying where the po-tential for increased sales and profits appears high; then, selling if the promised gains fail to materialize.

The competitive landscape has changed in other ways as well. The corporate roles of the past where acompetitor was a competitor and a supplier was a supplier are now blurred. Many companies today func-tion simultaneously as competitors in one area, partners in another, and in reversed prime and supplierroles in yet other areas.

Some trends are ominous. Many formerly innovative companies have all but eliminated basic R&D, re-positioning themselves as “systems integrators” who rely on suppliers and partners to bring forth newtechnologies that they can exploit. Corporation after corporation has moved from manufacturing productto assembling and distributing product. Original equipment manufacturers have pushed technologicalresponsibility down the supply chain and have become far more selective in where they focus their R&D.The world’s top 100 corporations spent $236 billion on R&D in 20031; however, the percentage of thatinvestment focused on applied product development is very high, leaving a huge underinvestment in fun-damental science and critical areas such as process technology.

Core Competencies

When does an enterprise choose to maintain capability in house, and when does it choose to rely on part-ners and suppliers? In most cases the decision is a balance between profit, risk, and speed to market.Where it is clear that a certain function can be satisfactorily fulfilled with no reduction in product deliveryor performance, and no loss of intellectual capital, the decision is purely an economic one. Where threatfactors are present, the decision becomes more complex.

1 Harry Goldstein and Ronil Hira, IEEE Spectrum R&D 100, IEEE Spectrum, November 2004.



Circuit board manufacture is a good example of the trend to outsource. Not many years ago, most elec-tronics manufacturers maintained an in-house capability for making circuit boards. However, as envi-ronmental rules became more stringent and suppliers became more reliable, company after companyclosed their internal shops. Today, most circuit boards are commodity items bought from the lowest-costsupplier. High-end products are made through partnerships and qualified suppliers. Only a very smallgroup of original equipment manufacturers (OEMs) maintain any kind of circuit board production capa-bility. The same stories can be told for every industry and sector, from automotive and aerospace, toclothing and consumer products. In the consumer products industry, Procter & Gamble, long a pillar ofin-house technological excellence, recently announced their commitment to go from 95% internal tech-nology to 60% within 5 years. In the automotive sector the obsession with exploiting existing processcapability and capacity has driven a reduction of investment in core competencies to the extent that someindustry leaders worry about the loss of U.S. technical competitiveness.

How do models assist in sorting through the complexity of technology management and investment deci-sions? In leading companies, cost/benefit modeling and return on investment (ROI) analyses are main-stream tools for making technology investment and entrance/exit decisions. However, in many cases thedecisions come down to humans looking at the data (which is not complete or up-to-date), evaluating therisk (which may not be well-characterized), and making a determination about corporate priorities. Thesedecisions are not simple. For example, in pursuing foreign sales, U.S. companies frequently have to ad-dress offset requirements2 that include technology transfer to in-country partners. Companies in this po-sition have to balance the merits of winning a significant piece of work against the specter of creating apotential future competitor.

Outsourcing is another area where decisions have long-term strategic implications. A decision to out-source a technical capability may be made to quickly increase product capacity, offload workforce issues,or reduce capital exposure at the cost of losing a previously vital corporate asset. The commitment to“lean” organizations and processes has led to the loss of critical capabilities in many U.S. companies.Too lean could place the future of an organization in jeopardy. In other cases, OEMs have found theirproduct not making it to market because of the failure of key suppliers. The Sony PlayStation 2 is a clas-sic example, as an entire Christmas of sales was essentially lost due to a failure of the supply chain toprovide critical electronic components. Companies who outsource both production and quality controlhave paid dearly for those decisions.

U.S. companies need enhanced methods for core competency management and more sophisticated modelsto assure that all pertinent factors are considered during the decision process. The qualitative human de-cision needs to be augmented by quantitative knowledge provided from within the company and throughexternal data/information mining. Modeling systems, integrated with knowledge-based risk assessment,can support proactive core competency management.

Technology Selection & Maturation

Management of technology selection and maturation is an area of much progress, although governmentand industry continue to invest billions in R&D without any kind of large-scale coordination based onaccurate knowledge of the entire R&D landscape. A senior manager of R&D for a major corporation re-cently said, “We do a great job of managing our technology investment. We have a Director of R&Dwho reports to the VP of Operations. We have four program managers working for her, and they makesure that the money is well spent.” While it may be true that they are doing a great job, is this approachenough? Is it empowered individuals or a rigorous and unbiased strategic system that assures R&D dol-lars are spent wisely?

2 Offset is a common practice in multi-national contracts. For example, an engine manufacturer might be required to subcontract

a significant portion of production to in-country sources and import certain technological capabilities as a basic requirement ofa compliant bid.



Roadmapping is an increasingly popular and powerful tool for guiding technology evolution. Using anyof a wide variety of formats and development methodologies, technology roadmaps answer the questions:

• Where are we now?• Where do we need to be?• How do we get there?

Technology roadmaps alone are not sufficient to assure wise investment. Systems are needed to managethe technology maturation process. One system becoming commonplace across the manufacturing land-scape is the technology readiness level (TRL) convention. This method assigns a technology readinesslevel (TRL) to an emerging technology on the basis of discrete points in the maturation process.. Proc-esses such as stage gate3 enable the management of the decision points for progressing through the levels.Manufacturing technology readiness levels (MTRLs) are becoming more relevant in managing manufac-turing technologies to the point of ap-plication and productive use. Figure4.2.1-1 outlines and compares the TRLand MTRL criteria. Another elementof the TRL/MTRL process is advance-ment risk level (ARL), which is used inassessing risk and supporting invest-ment decisions. The ARL metrics are:

• ARL 1 – Very low degree of diffi-culty anticipated in achieving R&Dobjectives; only a single, short-termtechnology project is required to as-sure high probability of successfullyachieving the next MTRL on sched-ule.

• ARL 2 – Moderate degree of diffi-culty anticipated in achieving R&Dobjectives; a single, focused technol-ogy project with a viable alternateapproach for development is requiredto ensure high probability of success-fully achieving the next MTRL onschedule.

• ARL 3 – High degree of difficultyanticipated in achieving R&D objec-tives; two concurrent alternativetechnology approaches are requiredto assure high probability of success-fully achieving the next MTRL onschedule.

• ARL 4 – Very high degree of difficulty anticipated in achieving R&D objectives; requiring multipletechnology development approaches to achieve the next MTRL within project schedule requirements.Technologies with this designation are most likely assessed at TRL 3 maturity or lower and would bedifficult to scale up.

3 http://www.prod-dev.com/stage-gate.shtml.

Figure 4.2.1-1. Technology readiness assessment is an effectivetool for understanding technology maturity and determining the

investment required to move to production readiness.



• ARL 5 – The degree of difficulty anticipated in achieving R&D objectives for this technology is sohigh that a fundamental demonstration of concept feasibility is required. Technology likely assessed atTRL 1 or 2 and is thus considered not ready for applied technology development until basic principlesare developed. Alternate approaches should be developed in parallel.

While the current state does embrace the readiness assessment methodology, there are deficiencies thatthe model-based strategic management process will address. In the present state, TRLs, MTRLs, andARLs are assigned based on human opinion since supporting models and decision tools are not available.Further, the determination for advancement and continued funding is very much dependent on the writtenand oral communications skills of the researcher or program manager. The systems have promise, buttools are needed to make them more effective. There is a clear need for strategic planning models toguide the technology maturation process.

Technology Acquisition, Protection, & Exploitation

The circuit board example used earlier provides a convenient starting point for discussion of technologyacquisition. Suppose an electronics company has excess capital they wish to invest in growing their busi-ness base. They have several choices: 1) diversify into related products; 2) expand existing design andmanufacturing capabilities to increase capacity; and 3) acquire technologies from suppliers, partners, orcompetitors to bring more work in-house. These important decisions have very different implicationsrelating to cost, profit, market share, workforce skills, capital position, and future competitiveness.

Models play a role in these kinds of decisions, but not a strong enough one. In today’s best practice, hu-man analysts review cost and profit projections based on economic and market conditions and forwardtheir findings to the decision makers. The final result typically comes down to an individual decision thatis based on intuition as much as hard numbers. In the current state, the company leadership rarely has allthe information they need to make the best decisions, and much of the information they do have is incom-plete or imprecise. There is a pressing need for robust models that link to rich sources of accurate data toallow executives to accurately project the outcome of different options, evaluate the impact of possiblefuture business conditions in each scenario, and determine, with high confidence, the best course of ac-tion.

Intellectual property (IP) is the knowledge capital that an organization can utilize to transform an existingproduct or process to a new or better product or process. IP is the fuel that powers the corporate engine.Today, the only real source of IP is the human thought process. IP is often created when companies seekto improve on a product or to find a different and better solution altogether. A second major contributorto IP is pure research. A third way IP is generated is by accident. This happens when the search for asolution to one problem, or a random observation, unexpectedly results in a new idea or discovery. Theconcept for Velcro, for example, resulted from a look at a cocklebur under a microscope. The concept forPost-It notes came from a flawed batch of adhesive.

Models are valuable tools in IP generation. Combinatorial chemistry – the ability to model and evaluatemany matrices of materials with graded doping – has led to a revolution in pharmaceuticals and otherprocess industries. Modeling ideas and designs to quickly identify failure modes, and success modes, isaccelerating the innovation process. However, there is still much to do. We need models that can quicklyturn random thoughts into workable concepts, and concepts into systems.

Protection of IP is an increasingly contentious issue as U.S. manufacturers become global companies andincreasingly rely on offshore markets and sources to grow their sales and profits. Many countries, andmost foreign competitors, have little or no regard for U.S. patent protections, and even U.S. companiesactively engage in “reverse engineering” to analyze competitors’ products and incorporate valuable fea-tures into their own designs. Although these issues are not solvable with technology, modeling capabili-ties can aid by helping companies weigh options for exposing their technologies to copying or reverseengineering by other countries. These capabilities would not impede pirating, but they could help com-



panies understand the potential time windows within which a market opportunity could be exploited be-fore it is undercut by local sources.

Modeling and simulation systems are finding their way into IP management applications. Dow ChemicalCompany found, as many organizations do, that they were inundated with patents and IP that was not be-ing well utilized. They increased annual licensing income from $25 million to more than $125 million injust 5 years using an Intellectual Asset Management Model. This model helped reduce patent administra-tion costs and identify worthless patents for abandonment. Examples like this highlight the need formodeling systems that support innovation and the exploitation and protection of decision processes.

4.2.2 CURRENT STATE ASSESSMENT FOR FINANCIAL & CAPITAL ASSETS MANAGEMENT

WHO’S LOOKING AND WHAT DO THEY SEE? Capital and Financial Management is a team sport. The ChiefExecutive Officer and his direct reports drive the decision process for mergers, acquisitions, and majorcapital investments. The Chief Financial Officer represents the most prominent viewpoint, being respon-sible for financial planning and management and having a strong role in managing capital assets. Thecompelling need for all executives is accurate, current, and complete data, coupled with financial modelsthat accurately forecast outcomes and risks.

This very broad element of the functional model for strategic management includes the management ofenterprise-level finances and capital assets. Capital asset management concerns questions such as: Do Ibuild a new building, or renovate the present one? Do I continue to invest in Product X when my com-petitor is working on something similar? Is it better to continue to compete with Competitor A or pursue amerger to dominate the market, even though it may be “us” that is acquired? While supply chain man-agement may not be on the radar screen of the capital management function, sourcing decisions (e.g., dowe build the new product here at our main facility or distribute production around the world) certainlyimpact capital utilization. Improved systems are needed to provide accurate dynamic models that enablethe comprehensive management of the finances of the total enterprise.

4.2.2.1 Financial Management

The financial management function is critical to the success ofall companies, large and small. Modern information systemsand financial modeling tools provide a wealth of data to helpunderstand the health of the business and guide financial deci-sions. However, fully understanding all factors that impactfiscal success, and getting access to all the data needed tosupport good decisions, is still a major challenge for everycompany.

Even in a small business, when considering the time spacingof events (leads and lags), the tax effects, constraints, andother complexities, the modeling process is complex andhighly sensitive to error. The modeling tools that assist increating financial projections may include mathematical cal-culations, simple rules, or complex rule systems that applycomplexity theory, neural inference, and other advanced con-cepts. Many tools exist in a wide range of cost, sophistica-tion, and functionality, although spreadsheets remain the stan-dard tool for financial modeling in every business sector.

Major deficiencies of the current state in this area are the lackof standards for financial information exchange and the lackof interoperability of financial management systems. An im-

On-Line Financial DataSaves Money

When Mike Jordan took over as CEO ofFrito-Lay, it was suffering from somethinganalogous to a "spinal cord injury" -- itsphysical body was severed from its digitalbrain. Starting with decentralization of salesand marketing platforms, Jordan developeda "rip and replace" plan of IT renewal basedon handheld technology for salespeople inthe field and shifting to on-line operations.Salespeople were able to manage price,inventory, and customer data in real-time incommunication with the supply chain.

The changes cost $140 million. Result: thecompany saved 30,000 to 50,000 hours ofpaperwork per week. Better control of salesdata saved the company more than $40million per year. Frito-Lay was able to re-duce the number of distribution centers,reduce stale inventory by 50%, and in-crease domestic revenues from $3 billion to$4.2 billion in 3 years. Quite an ROI!

From "Getting IT Right", Harvard Business Re-view, by Charlie S. Feld and Donna B. Stoddard



portant development is the emergence of the Extensible Business Reporting Language (XBRL) – a com-mon standard to improve the speed and accuracy of corporate reporting. XBRL is a web-based program-ming language that tags financial information and provides contextual information. Standard coding offinancial data ensures that it can be used across software programs, platforms, and countries; allows fordirect comparison of numbers obtained from disparate sources; and makes it easier to benchmark finan-cials with other businesses in a company’s sector. Also, financial information can be exchanged betweendifferent corporate divisions or sent to regulators or investors without manual re-entry or conversion usingmiddleware. This enables organizations to manage international differences in currencies and accountingstandards. Use of a common financial language also simplifies account reconciliation in partnership ar-rangements or after a merger or acquisition.4

Some experts believe that XBRL will be widely used by the end of 2005.5 The Federal Deposit InsuranceCorporation (FDIC) will require banks to submit their 2005 call reports in XBRL; banks are offering in-centives to clients who are XBRL-compliant; and the U.S Congress is considering whether the SECshould adopt XBRL. Development of XBRL-compliant modeling and other software tools will enablethe adoption of XBRL on a large scale.

4.2.2.2 Capital Management

Capital asset management is a stressing challenge in today’s business environment. In much of traditionalmanufacturing, the industrial complex now in place was built for a large-production, limited-change mar-ketplace (i.e., large buildings with lots of equipment and space). Downsizing, outsourcing, and leanmanufacturing have been among the many influences that have led to the present situation of mismatchedcapacity. Agility is limited by the constraints of sunken investments, so change to incorporate new tech-nologies and processes is impractical in many cases. In the automotive industry, for example, utilizationof existing processes and existing assets is a top priority. The R&D budget for this sector is heavily in-vested in product-based technologies to the exclusion of processes. The result is a focus on “continuousimprovement” that offers little chance of dramatic, transformational change. This locks the basis forcompetition into one of minor changes for each model year and limited new product capabilities untilsomeone breaks the mold and forces competitors to play catch-up.

The electronics industry offers a different view. With high-profit products and a business strongly fo-cused on exploiting intellectual property, the cash flow is robust and product model lifetimes are short.New fabrication factories (fabs) are quickly built with the latest equipment for emerging product lines.As new products emerge, the fabs are closed, then either retooled or made available for other applications.This model of highly fluid change is not limited to electronics. One mid-sized mechanical componentmanufacturer is known to have a policy that a machine tool will never reach its second birthday in theirshop. The logic is that the latest model equipment is valuable for resale, maintenance is low and reliabil-ity is high, and the shop is always tooled for world-class operation.

This discussion relates to the decision processes for capital investment. In most cases, the decisions aremade based on competitive positions (such as the automotive and electronics industry models) and avail-able assets. Modeling tools are used extensively to support capital investment decisions. For example,one Navy program utilized a very large and highly specialized eight-axis mill. The milling operationswere timed to the availability of tooling sets for various setups, and the contract with the Navy specifiedonly two sets of tooling. The contractor used process flow models to demonstrate that it was impossibleto deliver the product on schedule and within cost without an additional tooling set. The investment of amere $250K paid for itself many times, and enabled the delivery of critical components for a multibillion-dollar program.

4 CFO.com (2004). “Special Report: Need-to-Know Tech,” http://www.cfo.com/guides/guide.cfm/3036068?f=search.5 Ibid.



Capital investment decisions and the models that support them consider many factors. Typically, in anycorporate structure someone is going to spend all the capital funds in the budget. This promotes reason-ing like, “This department has done well this year, so let’s renovate their offices,” driving many decisions.The choice to acquire an asset, lease it, or outsource the capability should be extensively and accuratelymodeled. In most cases, the measure of the model’s effectiveness swings on its ability to predict futureresults. In most cases, if we have the data and have an accurate vision of the future, accurate prediction isassured. The strategic management challenge is thus to develop financial modeling systems with greatlyimproved prognostic accuracy.

4.2.3 CURRENT STATE ASSESSMENT FOR KNOWLEDGE MANAGEMENT & APPLICATIONS

WHO’S LOOKING AND WHAT DO THEY SEE? Managing corporate knowledge may or may not be a strategicimperative in a given organization. Chief Technology Officers and Chief Information Officers might findknowledge in their responsibility area – often without a clear view of what that responsibility means.Some corporations have established the Chief Knowledge Officer as a senior management position.Whatever the structure, in today’s business environment the ability to manage the knowledge assets ofthe enterprise is increasingly vital to not just business success, but continued survival.

Knowledge is an inseparable component of a model-based enterprise – knowledge about customers andproduct technologies, production data trends, process expertise, market intelligence, and more. The cur-rent state of knowledge management is characterized by isolated examples of success and some disillu-sionment about the topic area. The opportunity is strong, as knowledge applications offer an increasinglyvaluable toolset for managing the complexity of corporate data and capabilities. Some companies haveutilized the tools to institutionalize best practices, preserve valuable expertise, optimize decision making,and develop future capabilities through focused knowledge capture and application. Disillusionment dueto the intangible nature of the topic and the “fuzzy” implementation of many companies exists. Manycompanies have created lessons-learned systems and established internal web portals to communicate bestpractices and improve access to corporate knowledge. Results of such initiatives have been mixed. Onelarge aerospace company recently shared with IMTI that they have 870,000 lessons-learned documents ina repository, but no clue what to do with them. Another large aerospace firm launched a corporation-wide web presence redesign in 2003, with the result that tens of thousands of employees lost links to vitalinternal information sources. One user commented that formerly rich detail available on company pro-grams is apparently gone, requiring employees to make dozens of phone calls to track down informationthat was formerly available with a few clicks of a mouse. On the positive side, Ford Motor Company hasinstitutionalized a best practice system that has documented savings of over $1.4 billion since 1997.6

Knowledge management is a relatively new field. It has arisen as a new discipline in its own right be-cause leading companies have realized the role of knowledge in creating sustainable competitive advan-tage. Unlike many other organizational functions, knowledge management is cross-disciplinary. Everydepartment, facility, and employee is both a user and a contributor. Successful companies understand thatmanaging knowledge is a collective activity. The need for reusable nonphysical resources, as manifestedin object-oriented programming models, is rapidly growing. Finally, knowledge management can be use-ful in managing organizational changes and improving agility. It is viewed by some as a valuable protec-tion against the vulnerability caused by the business process reengineering movement’s emphasis on rapidreorganization to meet changing customer requirements.

It is no surprise that business managers and technologists have different viewpoints with respect toknowledge management. Business practitioners typically have a top-down perspective and are primarilyinterested in leveraging intellectual assets to achieve corporate strategic objectives. They often viewknowledge management as a people issue, not a technology issue. The problem, writes Phil Murray,Editor-In-Chief of KM Briefs and KM Metazine, is that tacit knowledge, which is embedded in personal

6 From information exchanged in e-mails with Ford Motor Company employees and provided here with permission.



experience and often intangible, is “rarely recorded and shared in business organizations…even thoughtacit knowledge may be the real key to getting things done.” 7 Technologists, in contrast, typically have abottom-up perspective and are interested in technologies for sharing and reusing knowledge. They areconcerned with building models using the organization’s explicit, codified knowledge. They often holdthe view that the benefits of knowledge management will inevitably emerge, albeit in unpredictable ways,as long as people use the right tools in the right way.

Most business practitioners and technologists agree that data and information do not by themselves solveproblems or create value. Knowledge management is concerned with the transformation of data and in-formation into knowledge: the actionable insights and lessons learned from experience, which are trans-formed into wisdom – the ability to apply knowledge to solve problems and exploit opportunities.

While both perspectives have strategic significance, the focus for the model-based enterprise is on thebusiness viewpoint. Leading companies are beginning to use competency modeling to create models thatrepresent the skill sets, values, and behaviors of their top-performing employees. Auditing of intellectualassets may help companies leverage existing explicit knowledge and skills, but competency modelinggoes further. It identifies the intellectual and emotional characteristics that are most strongly associatedwith individual performance, helping ensure a well prepared, flexible, and responsive workforce. Com-petency modeling also allows more accurate modeling of potential enterprise responses to future opportu-nities.

Knowledge management for strategic positioning is being accelerated by the emergence of ontology-based knowledge discovery and management systems using modeled structures and increasingly powerfulalgorithms. Corporations have long desired to understand the R&D landscape as a prerequisite to theirinvestments: to understand what the future holds and to look into the future and see opportunities. Theseand more capabilities are being provided through the development of text and data mining systems thatare based on a semantic understanding of the topic area as defined in specialized ontologies. Applyingthis ontological understanding, automated knowledge management systems and other tools use model-based frameworks to process massive amounts of information into useful knowledge. At Pfizer, Execu-tive Vice President Karen Katen leverages intellectual assets using a process she calls “information min-ing.” Pfizer’s systems and marketing research departments gather and analyze data from an array of in-formation feeds and repositories. Product positioning is based on knowledge culled from databases ofPfizer's clinical trials information. Competitive intelligence is gathered using Pfizer's sales force, newsfeeds, online search services, and web-based and traditional resources focused on specific diseases.8

Knowledge management is a rich topic that extends far beyond the bounds of strategic management asaddressed here. Knowledge Applications for Design and Manufacturing is a separate NGMTI thrust areabeing launched in the 2005-2006 timeframe. For more information check the NGMTI web site atwww.ngmti.us.

4.2.4 CURRENT STATE ASSESSMENT FOR STRATEGIC PLANNING & EXECUTION

WHO’S LOOKING AND WHAT DO THEY SEE? Everyone on the corporate management team should be en-gaged in the strategic planning process. In the best-managed companies, strong leaders drive a corpo-rate vision that permeates all operations and processes. The key needs in this area are for greatly im-proved capabilities to model the future business environment, the linking of these models to rich sourcesof data, and the integration of these capabilities into every aspect of enterprise operations.

There is little argument that strategic planning is critical to corporate success; however, there are no surerules for successful strategic planning, and it cannot be truly effective without continuous effort. One keypromise of the model-based enterprise is to institutionalize strategic planning into the business processes

7 Ibid.8 Alice Dragoon, “Rx for Success,” CIO magazine, July 1995, p. 52.



of the enterprise by providing a toolset that enables seniormanagement to define the best directions and continuallymake the right decisions to support those directions.

The challenges of strategic planning are well documented.In The Rise and Fall of Strategic Planning, Henry Mintz-berg concludes that, “The whole nature of strategy making– dynamic, irregular, discontinuous, calling for groping,interactive processes with an emphasis on learning andsynthesis – compels managers to favor intuition. This isprobably why all those analytical techniques of planningfelt so wrong...Ultimately, the term ‘strategic planning’ hasproved to be an oxymoron.”9

Progress is being made, however. Almost every manufac-turing company has a strategic plan, and the strategic plan-ning process has evolved to be far more than documentinga few strategic objectives. Leading enterprise managementapplications such as OutlookSoft’s Everest (Figure 4.2.4-1)provide “predictive analytics” tools to bridge the gap be-tween activity monitoring, strategic planning, and tacticalexecution.10 Similar functionality is available in the prod-uct lines of SAP, AspenTech, and other vendors.

Technology roadmapping has evolved to add greater depthto the strategic planning process. Tools such as the bal-anced scorecard, developed in the early 1990s by RobertKaplan and David Norton,11 now provide structured waysto understand the various perspectives important to theenterprise and define what should be measured in order toensure success. While the balancedscorecard (Figure 4.2.4-2) is not astrategic planning tool per se, it is aleading example of how models areused to supplement strategic plan-ning processes in guiding businessperformance.

In virtually all corporations, thestrategic management process ismanaged from the top and permeatesthe organization through establish-ment of goals and objectives foreach business unit and operationalelement. The Chief Executive Offi-cer, supported by a Vice President orDirector of Strategic Planning, en-sures that the strategic plan, the met-rics, and the action process are in

9 Henry Mintzberg, The Rise and Fall of Strategic Planning, Free Press: 1994.10 http://www.outlooksoft.com/product/predictive_analytics.htm .11 http://www.balancedscorecard.org/basics/bsc1.html .

Figure 4.2.4-1. Applications such asOutlookSoft’s Everest provide predictive

analytics tools to integrate strategic planningand tactical execution.

Figure 4.2.4-2. The Balanced Scorecard is a widely acceptedmanagement tool for creating a clear view of a corporation’s strategic

direction and translating that direction into action.



place and the plan is executed. The top-level measures of success are broken down through each organi-zational unit, and performance assessment is directly tied to that process.

In the current state, strategic planning is mostly the purview of the executives. It may be dynamic if im-plemented appropriately, but it is not a real-time process except when “activated” to respond to a crisis.Models are used, but are not integrated into the mainstream process or automated toolsets. An internetsearch will deliver many options for strategic planning templates and models. Scorecard systems anddashboards are plentiful, and their functionality is expanding. It is probably fair to state that these sys-tems mostly provide assistance in executing manual processes, but do not provide an integrated, model-based analysis system that lays out the best path for the enterprise.

4.2.5 CURRENT STATE ASSESSMENT FOR STRATEGIC OPERATIONS MANAGEMENT

WHO’S LOOKING AND WHAT DO THEY SEE? For the Vice President of Operations and the top levels of op-erations management, their view is not necessarily focused on daily operations performance, but onstrategies to keep those operations efficient, productive, safe, reliable, and profitable in meeting theircommitments. The key need is for better ability to model complex issues regarding capacity, expansionand contraction, changeovers, and response to both near- and long-term challenges.

Uncertainty and complexity cloud the vision of the enterprise for assured ability to produce profitableproducts and respond quickly and surely to changing requirements. How does an organization accuratelyalign to produce what the customer needs, in the way it needs to be made to be competitive at a profit?How can the organization select the best production and support partnerships with other companies, andunder what terms? This challenge is exacerbated by the intensity of competition, by the multiple rolesorganizations have with one another today, and by the necessity of maintaining lean operations that areresponsive to change.

Many issues must be considered to obtain a clear view of the needs for operations management from thestrategic perspective, as discussed below.

Integration of Design & Manufacturing

Modeling and simulation systems play a large role in the integration of design and manufacturing, butfurther development is needed. The cost of creating all the models needed to evaluate design options andprovide a total digital handover for manufacturing execution is not yet affordable, so progress will con-tinue to be evolutionary rather than revolutionary. This current state points clearly to the need for amodel-based world of product realization wherein many options are fully evaluated for producibility andtotal life-cycle performance.

Integration of Business Development & Production

The business developer and the production manager must understand and support each other’s perspec-tives. If the marketer sells a favored customer on the idea of a new product, the result of the deal mayleave the production manager with an unrealistic “opportunity” with no acceptable excuse for not meetingthe commitment. Enterprise production must be closely allied with marketing, sales, and business devel-opment to avoid such disconnects.

Opportunity analysis must be balanced between return and risk, and modeling systems can and alreadyare helping with this. In the 1970s and 80s there was a very popular software tool developed by BrighamYoung University called DCLASS. It was primitive by today’s standards, but some of its capabilitieswere ahead of their time. One particular DCLASS application, developed by Eaton Corporation, con-nected the marketer directly with the factory. A laptop database that was regularly upgraded with modelsof the factory capability gave the marketer instant access to information such as:

• Can we make it?



• When can it be delivered?

• How much will it cost?

Real-time systems today perform these functions much better than the early prototypes, but many compa-nies today do not have useful models that define their capabilities. Meaningful links between productionoperations and the marketing function frequently do not exist, and there are no good models that allowmanagers to thoroughly analyze the short- and long-term prospects and operational impacts of potentialbusiness relationships.

Efficiency vs. Capability

Lean manufacturing continues to have a remarkable impact on the production landscape. The currentstate is that most of the fat is gone, and companies are now cutting muscle in order to squeeze out in-creasingly smaller increments of productivity and profit. There are two strategies for getting lean. Onefocuses on making operations more efficient without impacting capability, through mistake-proofing,cellular operations, integrated workflow and workspace optimization, and kaizen (process improvement)techniques. The second strategy is to downsize for efficiency at the expense of capability, which canraise significant strategic management issues. In today’s environment, the short-term payback and quar-terly profit picture are driving companies increasingly to the second strategy, at the risk of losing the abil-ity to capitalize on fast-breaking opportunities.

Modeling and simulation play a strong role here. By modeling capability and throughput, companies cantest proposed changes in capacity and capability to understand likely effects on the ability to support bothpresent and future demands. The current generation of business modeling tools supports this function;but, aside from a number of good workflow modeling applications, the tools are not robust. The betterversions of these tools generally exist as component applications within large and costly ERP packages,which are not affordable for smaller manufacturers.

Effective, Skilled Employees

While automation is enabling companies to run manufacturing operations with fewer personnel, there willalways be a strong need for skilled, creative employees. In fact, the continuing trend to downsize workforces and make the remaining employees deliver more for less, raises the priority for high-caliber touchlabor, technical staff, and managers. Strategic management of staffing requires the same kinds of model-ing as the other enterprise functions previously discussed. What is the right level of permanent staff ver-sus subcontracted employees? If we have to go to two shifts to meet a production spike, do we haveenough lead personnel to maintain consistent productivity and quality? Should employees be developedin-house for the changing environment or should they be replaced with “off-the-shelf” trained people?What are the emerging skill sets needed for new products and technologies and how are those skills bestacquired?

Model-based capabilities are key to maximizing the potential and capability of a shrinking and increas-ingly stressed workforce. Model-based systems are essential to enabling consistent process performancewith limited human intervention, and model-based training offers the potential to radically shorten learn-ing curves and help employees quickly leverage existing skills to develop new ones.

Model-based human resource management systems are needed to enable more accurate forecasts ofstaffing requirements as the strategic plans for the enterprise unfold over time. These projected needs canhelp address a deeper issue in the staffing problem, which is the declining number of students who areinterested in entering the manufacturing field. By developing academic profile models to design futurecontent for undergraduate and K-12 academic curricula, teachers and manufacturing leaders can help re-verse current trends and revitalize the nation’s manufacturing workforce and technical leadership.

Some leading edge companies have employees cross-trained in multiple operational functions to guardagainst lost productivity in the event of employee absence or attrition. Modeling these requirements is



key to developing training programs that cost-effectively deliver a flexible workforce able to respond torapidly changing requirements.

Optimization of Inventories

The current state of inventory management is closely aligned with lean practices. In the tactical world,the simple answer is that the best approach to inventory management is to have none. This is the goal forraw material, work in process, tooling and equipment, product inventory, and every other aspect of thevalue chain. Wal-Mart, the standard setter for logistics and procurement on the retail side, is pioneeringstrategic relationships to push inventory responsibility onto the supplier, responding to the consumer“pull” with Wal-Mart taking a profit in each transition. In the manufacturing arena, companies such asVolkswagen have instituted electronic commerce systems that share more information with the suppliersand integrate them more closely to every aspect of production. PBR, an Australian caliper manufacturerthat supplies many of the U.S. automakers, does not own the machines, the tooling, or the product as itpasses through their factories. While many companies still have raw material bins and make parts inbatches, the current trends are toward pull systems with limited inventory.

Modeling is an important part of the strategic inventory management process. In order for PBR to havethe right tooling for its machines, the right forged parts for its processing, and trucks at the dock to deliverthe product, it must model its factory and production schedules. For General Motors to know that it canget a yellow Camaro to Kansas City on Thursday, it must also know that there are three yellow ones withthe right accessories in a storage lot in St. Louis, and that they are not blocked by 47 gray and cream col-ored Park Avenues. They also must know that there are trucks available to make the transfer. This is asimple example on the surface, but the models are run each night, and the trucks roll every morning.Without these models, automotive commerce would come to a halt.

Regulatory Compliance

Operations management includes the assurance of regulatory compliance in all processes that are planned,and ensuring that present processes comply with changing regulations. This is a challenge to all indus-tries, but it is particularly challenging in sectors where risk, liability, and human interface are closely tiedto regulatory issues. Pharmaceuticals, chemicals, medical devices, and the automotive industry areamong the many examples where strategic management processes require special attention to regulatoryrequirements and trends.

The present state is characterized by some major areas of concern and areas of great progress. On theconcern side, there continues to be significant disconnect between regulators and implementers. Despiteaggressive lobbying, the legislative bodies and the agencies that impose the regulations often have limitedappreciation for the ultimate impact of their actions on the affected companies. California emission lawsand congressional “buy American” legislation are two examples that demonstrate how government is per-ceived as being unhelpful towards industry in the U.S., adding costs that foreign competitors do not haveto bear.

Modeling is a valuable tool in planning for and managing regulatory compliance. Process models andproduct chemistry models are commonly used to obtain regulatory approvals and engineer out health,safety, and environmental risks for new processes and facilities, and solve problems in existing opera-tions. However, even the best models lack the deep scientific fidelity needed to eliminate requirementsfor extensive testing and qualification. There is a compelling need for individual industry sectors to workclosely with regulatory agencies to develop and validate models for widely used processes. This will re-duce the time and cost of ensuring compliance with safety, health, and environmental requirements, thusreducing the time and cost of implementing new manufacturing processes and reengineering existingones. Also needed is a means to integrate regulatory knowledge bases and make needed data available tomanufacturing enterprise planning and business systems. This will enable companies to more accuratelyunderstand and predict the impact of potential regulatory changes, and explore the implications of differ-



ent response options – such as moving a particular operation offshore, modernizing current operations forcompliance, exiting a line of business, or developing new technologies.

Global Operations

U.S. companies are increasingly a part of the global marketplace through pursuit of overseas customers,establishment of offshore operations, or linking with foreign suppliers and partners. The ability to modelthe local environment is key to designing and implementing operations strategies that align with culturaltraits, fulfill their intended functional and performance requirements, and maximize returns on invest-ment. Local considerations typically include different work schedules, labor costs and skills, and thecosts of assuring good relationships with local authorities and communities.

Models are already used in international operations. A semiconductor supplier in Korea provides modelsof operations with the quote and provides real-time visibility of operations, including testing data, duringproduction. Despite competitive issues, this is an arena where U.S. companies (including direct com-petitors) should work together to share the benefits of existing models and collaborate in developing andmaintaining new generations of models that strengthen global competitiveness.

Learning from Tactical Experience

Effective use of filtered information from factory floor and business operations could be a big opportunityfor operations management. In the current state, management directives come from the top, and opera-tions are managed at the factory and shop floor level. The craftsmen and operators make the product, theforeman runs the shop, the general foreman provides oversight, and so on up the chain. The Vice Presi-dent of Operations is often disconnected from the day-to-day operations. In other words, the tacticalworld and the strategic world seldom work to the same objectives, and misguided performance objectivesworsen the gap. One of the biggest problems resulting from this disconnect is perpetuation of a culturewhere problems are misrepresented, underreported, or even hidden from the next higher level of man-agement in order to meet production schedules and avoid costly stoppages. As an example, a tire manu-facturer had trouble with bubbles in the tires. The shop-floor fix was to puncture the bubbles with an awland be sure that there was good rubber contact at the point of the defect. After multiple failures and ex-tensive investigations, it was determined that this practice was leading to catastrophic tire failure – fail-ures that could have been easily avoided by halting production until the flaw in the process was uncov-ered and fixed.

There are good examples of managing operations information flow from the bottom to the top. In De-partment of Energy facilities, an occurrence reporting system drives responsibility for raising awarenessof any deviation or change throughout the organizational structure. Off-normal occurrences are immedi-ately categorized and reported according to established rules. The methods of response are pre-determined based on the level of the occurrence. Similar structures are used in the Department of De-fense, particularly for ordnance and nuclear power operations. These structures are not perfect, but theypoint toward an environment where operations are monitored and model-based tools are used to detectproblems and devise solutions.

An increasingly valuable tool is the use of management dashboards and cockpits, which monitor key per-formance indicators and other metrics of operational health. The cockpit concept, as its name implies,provides the same kind of situational awareness that an aircraft cockpit provides for its pilot and is an ex-tension of the corporate war room concept. One of the leading implementations, developed in 1989 byBelgian neurosurgeon Patrick M. Georges, is part of the current SAP product line and is in use today inmore than 50 American and European companies (Figure 4.2.5-1). Many companies have developedsimilar electronic war rooms of their own, such as the APECS (Aerospace Production Execution ControlSystem) environment created by Martin Marietta in the mid-1980s to manage production for theLANTIRN navigation and fire control system. Desktop management dashboards are standard tools todayin the IT and communications service management industry, where incentive-based payments are directly



tied to daily and weekly metricssuch as system availability,downtime, and average responsetime for resolving customerservice requests.

Figure 4.2.5-1. SAP’s management cockpit provides real-time visibilityinto performance against critical success factors. (photo courtesy of Bretel

and SAP AG)



4.3 FUTURE STATE VISION & GOALS FOR STRATEGIC MANAGEMENT

A master enterprise model that is unique to the enterprise will guide the strategic management team incontinuously positioning the company for success in the rapidly changing global marketplace. Mod-eling systems accessed through a “strategic management cockpit” will support scenario-based evalua-tion of options for all strategic management functions. Model-based intelligent advisors will guide theorganization to the best choices and best decisions for achieving corporate objectives and responding tochallenges and opportunities.

In the NGMTI vision for the model-based enterprise, top-down and bottom-up management processesacross all functional elements of the enterprise are integrated in a “unity model” as illustrated in Figure4.3-1. The unity model provides a framework whereby strategic planning and direction processes are in-tegrated into enterprise work processes at every level of the organization. Information flows easily andaccurately in all directions, from the bottom and the top, to exactly the right level needed to support proc-esses and operations at each level for everyfunction.

The unity model aligns the enterprise mis-sion and goals across and down to the low-est level of each area of the company. Itplaces focus and value where it belongs, onthe enterprise as a whole, not on any singleentity or group. With all corporate elementsin unity, the inherent disconnects betweenlevels and units of the organization areeliminated. Each corporate officer occupiesan equidistant management position. Thisfacilitates a unity of purpose, roles and re-sponsibilities throughout every organiza-tional element, regardless of their specificrole.

This unified process dissolves the notionthat strategic responsibility is only signifi-cant to senior executives. Instead, it be-comes the mission of every managementlevel, and flows all the way to the plantfloor. More importantly, it provides a framework for developing and implementing a powerful set ofmodel-based tools that every manufacturing company, regardless of size, sector, or organizational design,can apply to unify and coordinate its strategic management processes.

In the manufacturing companies of the future, enterprise processes will be integrated and guided by amaster enterprise model. This is not a monolithic organizational architecture or an information system,but rather a high-level process model that contains or links to all the constituent models that define andguide the company’s different business and technical processes. The master enterprise model contains (orprovides real-time access to) comprehensive, accurate, and timely information on the internal workings ofthe enterprise and its supply chain, plus the external information and events that may affect the enterprise.

As the strategic management team executes its analysis and planning processes, the master model deliversthe information needed to make the best decisions for the enterprise. It ensures that the implications ofeach decision are reflected in all affected business units, organizational elements, and processes, and pro-vides feedback from these elements in order to optimize strategies for best results. The master model thusenables accurate analysis of the current situation and probabilistic scenarios for the future, helping the

Figure 4.3-1. The unity model provides a frameworkfor a model-based environment to realize the future vision

for strategic enterprise management.



management team position the enterprise to maneuver and succeed in the rapidly changing global market-place.

An open systems framework, flexible information representation schemes, and highly automated moni-toring and surveillance functions will tightly couple strategic management processes to operational reali-ties. Digesting daily a huge and complex quantity of data about internal operations and external condi-tions and events, the master model will empower the operation of the desktop management cockpit thatprovides each strategic management function with a continuously updated overview of pertinent events,trends, and opportunities for improvement. A continuous scan of external information sources (news me-dia, patent applications, changes in regulations, R&D monitoring services, etc.) will feed into a continu-ously updated “threats and opportunities” analysis for the enterprise. Intelligent advisory systems willintegrate this information with internal information to identify op-portunity and challenge scenarios for consideration by the strategicmanagement team. The model-based strategic management envi-ronment will be a dynamic one that learns from both inside andoutside the organization on a 24/7 basis, integrating the top floorwith the shop floor (and everywhere in between) and supportingthe best decisions at every level of the enterprise.

In the model-based enterprise, the information required for allbusiness functions is available when it is needed, where it isneeded, and in the form in which it is most useful. The strategicmanagement cockpit will place anyone who needs the services incontact with the information they need and the tools to convert it toactionable knowledge. The cockpit is the point of interface be-tween the user and the master enterprise model, and it is supportedby analytical tools, intelligent advisors,12 and connectivity to allinternal and external knowledge bases available to the enterprise.The user can present a scenario to the cockpit, and quickly receivean analysis based on the best information available. For example,if a decision is being made between acquisition, merger, or part-nering to acquire a needed capability, accurate and completeevaluation of all alternatives will be readily available. The basisfor recommendations will be visible, enabling analysis of everydecision and supporting continuous learning for the cockpit’s in-telligent systems.

The strategic management cockpit will give every member of the team the information they need to setobjectives, monitor performance, and respond to opportunities and challenges. Occurrences will be ana-lyzed for their strategic value, and proactive changes in corporate direction will be recommended whenappropriate. The system architecture will be open, interoperable, and modular, enabling companies toquickly tailor generic modules to support the specific needs of the enterprise.

12 A knowledge-based or intelligent advisor is a software tool that provides analysis and advice to support decision processes.

THE STRATEGIC

MANAGEMENT COCKPIT

The model-based strategic manage-ment environment will be a dynamicstructure that learns from both insideand outside the organization on a 24/7basis, integrates the top floor with thebottom floor (and everywhere in be-tween), and supports the best deci-sions for every function at every levelof the enterprise.

The model-based strategic manage-ment cockpit will extend far beyondtoday’s electronic war rooms, givingevery member of the team the infor-mation and tools they need to planwell, analyze thoroughly, implementquickly, and operate efficiently. Thecockpit will be supported by the masterenterprise model, which will integrateknowledge and modeling tools to sup-port all business processes.

The system architecture will be openand modular, enabling any organiza-tion to tailor it to support their uniqueneeds and environment.



4.3.1 FUTURE STATE VISION FOR TECHNOLOGY PORTFOLIO MANAGEMENT

With the model-based understanding of current environment and potential scenarios for the future, thestrategic management team will evaluate its current portfolio of technologies, phase out those that donot contribute to continued growth, and select other value-added technologies for development within,or acquisition to, the enterprise. Protection of intellectual property will be a computer assisted, rapid,and secure operation.

4.3.1.1 Core Competencies

In the model-based enterprise, management advisory systems interfacing with the master enterprise modelwill enable corporate executives to evaluate the value, importance, and impacts of maintaining a corecompetency in light of current and future competitive position and economic conditions. Decision sup-port tools will guide executives in evaluating different options of continued internal support, outsourcing,or other alternatives, quantifying cost/benefit tradeoffs and assessing related risks.

Goals & Requirements for Management of Core Competencies

• Goal 1: Core Competency Model Repository – Establish a shared industry core competencyrepository that captures the business attributes of processes and technologies across the enterprise.(S-M)13

– Common Core Competency Modeling – Survey existing core competency models and modelingtechniques and develop appropriate standards for creating and validating such models in digital form.Address interoperability requirements to ensure their compatibility with analytical tools. (S)

– Repository Structure – Design and implement the shared core competency repository using open-architecture information management standards that allow companies to populate the repository withtheir own models concurrently with sharing access to public domain models. (S)

– Repository Population – Develop and prioritize a comprehensive list of core competencies forwhich models should be developed, and implement development in accordance with those priorities.(S-M)

• Goal 2: Core Competency Evaluation System – Provide a system to evaluate core competencies,determine their value, and make informed decisions with respect to maintaining, discarding, or aug-menting them. Include the capability to report and forecast usage, the value of use, the cost of main-taining the competency over time, and the availability and desirability of alternative options. (M)

– Core Competency Cost/Benefit Analysis – Establish a knowledge-based approach and standardbusiness rules to enable cost/benefit evaluation of the various methods of providing core competen-cies to the enterprise and managing those competencies over time. The system should accommodateall options including procurement, development, outsourcing, technology partnership, or suppliersourcing. (M)

– Process Competencies – Develop and provide competency analysis capabilities specific to manu-facturing processes. Include the capability to assess process evolution for improved capabilities andforecast the timeframe for maturation of emerging process technologies that will render the existingprocess uncompetitive or obsolete. Include the capability to model a process’s flexibility to generatenew steams of revenue through modification to support new and different product types. (M)


M (Medium) = 3 to 5 years, and L (Long) = 5 to 10 years.



– Product Competencies – Develop and provide competency analysis capabilities specific to existingand potential product lines. Include the capability to forecast competitive trends in the product areain order to determine if product lines should be sustained, phased out, or reconfigured to capitalizeon emerging technologies. (M)

– Technical Competencies – Develop and provide competency analysis capabilities specific to thegeneral technical competencies of the enterprise as they reside in the technical staff and productionworkforce. Include the capability to evaluate the pros, cons, and risks of retaining or growing a par-ticular competency vs. divesting internal capability and relying on external partners and supply chainmembers. (M)

4.3.1.2 Technology Selection & Maturation

In the model-based enterprise, a knowledge-rich modeling environment will guide the evaluation and as-sessment of technology options to advance product and process capabilities. On-line decision supporttools will provide robust modeling capabilities and accurate, current, and complete information on whichto base technology investment and entrance/exit decisions. Technology roadmaps, strategic plans, andother tools used to define the direction of the enterprise will be dynamically maintained with the assis-tance of automated knowledge discovery tools.

Technology needs assessments and readiness assessments will be supported by digital tools to ensure ac-curacy and thoroughness of evaluation as well as alignment with the enterprise’s strategic goals and ob-jectives. R&D investment opportunities will be evaluated to ensure that investments are not duplicated orunwisely made in technologies that can be otherwise procured or which will have little or no ultimatevalue.

Goals & Requirements for Technology Selection & Maturation

• Goal 1: Technology Evaluation & Selection System – Provide a comprehensive and flexible systemthat enables the company’s technical leadership team to identify and analyze technology needs, modeland evaluate options, and select the best options consistent with the technical and business imperativesof the enterprise. (M)

– Value Stream Analysis Model Template – Develop a model-based advisory tool and templates toguide value stream analysis for enterprise processes, enabling identification and characterization ofneeded technologies or improvements to existing capabilities. (S)

– Model-Assisted Technology Roadmapping & Technology Planning – Provide a modeling envi-ronment and tools that enable in-depth analysis of all factors associated with targeted technologies,including cost, risk, timing, potential returns on investment (ROIs), competitive position, and impli-cations for current enterprise product, processes, and capabilities. (M)

– Technology Monitoring System – Develop an autonomous technology surveillance system that“trolls” the internet and accessible knowledge sources to search for and identify new information thatrelates to the enterprise’s technology base and future technology plans. Include the capability toprovide routine updates to affected researchers, developers, and managers on developments of inter-est, and red-flag items having potentially disruptive impact. (S-M)

– Decision Support Tools for Process Selection – Develop a decision support advisor to assist in theevaluation of process options and aid in selection of the best processes with respect to all pertinentbusiness factors. Include the capability to accommodate multiple technology options and outlinemultiple paths with selection points and criteria. (M)



• Goal 2: Technology Maturation Management System – Provide a model-based system for manag-ing technology maturation processes and continuously assessing performance and value against theenterprise’s plans and strategic objectives. (M)

– Technology Readiness & Risk Assessment – Develop analytical tools that enable consistent andunbiased assessment of technology maturity level, and requirements for maturation, based on stan-dards and criteria for determination of technology readiness levels (TRLs), manufacturing technol-ogy readiness levels (MTRLs), and advancement risk levels (ARLs). (S)

– Objective Advancement Evaluation – Extend the readiness and risk assessment toolset to manageand monitor technology progression through the readiness levels. Include the capability to defineprogress milestones, monitor progress against the plan, and update any comparative assessment ofcompeting options. Provide interfaces with enterprise product and process modeling systems toautomatically update cost/benefit projections and ROI calculations of selected technologies at eachmilestone or stage of the maturation process. (M)

4.3.1.3 Technology Acquisition, Protection, & Exploitation

Commercially available tools and analysis methods will interact with the master model of the enterpriseto assist the strategic management team in evaluating the best mechanisms for acquiring targeted tech-nologies. The model-based analysis will evaluate options for developing the technology internally, pro-curing intellectual property rights, partneringwith a technology developer, or buying turnkeytechnology with supporting services.

Internal developments will apply a model-basedconceptualization environment as discussed inSection 2. Starting from scientific models ofmaterials and processes, plus a clear definitionof desired and potential applications, the systemwill analyze the key characteristics of the envi-sioned product or process to highlight the richesttarget areas to explore. The system will assistresearchers and designers in evaluating compet-ing approaches, identifying opportunities forR&D collaboration, and turning their ideas intoworking concepts using virtual prototypes. Withthese model-based analysis tools plus humaningenuity and imagination, the enterprise canexplore and weigh all possible solution paths andfocus development efficiently on the mostpromising and valuable approaches.

When a new product or process concept is de-fined, the technology management system willautomatically search patent databases and otherR&D knowledge sources to support determina-tion and characterization of intellectual propertyissues, export control limitations, and other fac-tors that influence how the idea should or mustbe protected. The system will provide a fullyautomated capability for intellectual property documentation, submission of patent applications, and de-termination of export protections, greatly reducing the time, cost, and complexity of legal compliance.

A New Approach forTechnology Surveillance

Technology roadmapping is a valuable tool for helping or-ganizations develop a focused plan to achieve their strate-gic objectives. However, setting the strategic direction oftechnology-intensive organizations is a complex activitythat does not end with the definition of how the objectiveswill be met. For each objective, many solution pathwayscan help the organization meet its goals. Maintainingawareness of new scientific, economic, and corporate de-velopments – and analyzing the relevant information in realtime – is a challenge that cannot be met with the currentgeneration of corporate information systems and commer-cial search engines.

InRAD LLC, a small company in Tennessee, is building atoolset to fill this critical void with the support of the De-partment of Commerce’s Advanced Technology Program.The toolset, known as AKDS (Automated Knowledge Dis-covery System) will enable manufacturers to answer thequestion, “Now that we’ve identified our R&D strategy, howcan we be sure we stay on track and don’t miss emergingand better solutions?”

AKDS is an innovative approach to identifying, retrieving,analyzing, and presenting – to everyone across the manu-facturing enterprise – the information the organizationneeds. Through the creation of company-specific domainontologies and by harnessing the power of semantic-basedtext and data mining capabilities, AKDS not only retrievesthe most relevant information (both internal to the organi-zation as well as from external web sites and databases),but also stores it in exactly the right place for real-time ac-cess.



Goals & Requirements for Technology Acquisition, Protection, & Exploitation

• Goal 1: Real-Time Awareness of Technology Availability – Provide capabilities to monitor thetechnology landscape, identify information of value to the enterprise, and deliver it to the right loca-tions within the organization. (M)

– Autonomous Technology Surveillance – Develop tools that can be trained for specific domains toautomatically search for and categorize information about technologies of interest to the enterprise,providing a living repository of knowledge that is immediately accessible to researchers, programmanagers, and all members of the organization’s technology leadership team. (S-M)

– Custom Presentation of Needed Information – Develop models/profiles of user organizations andindividual users to provide a framework for delivering information of interest in the form and formatthat is most useful to the users. (S)

– Technology Transfer Management – Develop a comprehensive database of technologies producedwith public funds that are available for licensing or other means of exploitation. Refine the businessprocesses that research institutions are required to work to in order to remove or mitigate the road-blocks to commercialization. (M)

• Goal 2: Intellectual Asset Management System – Provide tools to evaluate existing intellectual as-sets and make the best determination of strategy for action. Include the capability to assess the valueof the asset, the cost of maintenance, and potential applications to determine if it is best to exploit,hold, sell, or drop the asset from the enterprise portfolio. (M)

– Gap Analysis & Decision Support – Provide intelligent model-based advisors that capture the spe-cific needs of the organization and match those needs to existing and emerging capabilities withinand external to the enterprise. Include the capability to identify and characterize gaps that representnew opportunities to pursue or problems that must be addressed. (M)

– Discovery Evaluation – Provide knowledge discovery tools that search patent databases for poten-tial matches to topics of interest and help identify opportunities for discovery and innovation. Pro-vide the capability to evaluate the potential value of the discovery and define recommendations forfurther efforts. (M)

– Innovation Management Tools – Provide tools that match technological discoveries and innovativeideas to potential applications and aid in defining the best path to implementation. Include the capa-bility to guide inexperienced researchers/technologists in bridging the gap from concept to applica-tion. (M)

– Commercialization Management Tools – Provide model-based advisory tools to assist fledglingbusinesses and entrepreneurs in moving from first demonstration to viable commercial application.The SBIR program should be considered as a source of input and a testbed for this capability. (M)

4.3.2 VISION FOR FINANCIAL & CAPITAL ASSETS MANAGEMENT

At every point and at any time, managers and executives will have the information they need to runtheir businesses and make the best financial decisions for the future. Major capital investment andredirection decisions will be aided by powerful, highly automated models that combine mathematicalanalysis, knowledge-based rules, and economic analysis of projected scenarios, both locally andaround the world. Allocation of capital funds will be accomplished with a full view of the value andrisk of each potential investment.

In the model-based enterprise, financial modeling tools and their outputs will be standardized within andacross all sectors of industry. This will greatly simplify the process of building financial models, linking



them to living data sources, and integrating the tools into an enterprise’s strategic management systemsenvironment and unique business processes. With XBRL and other standards providing convenient userinterfaces and enabling transparent data exchange, model-based management tools will enable the finan-cial staff to create and maintain extremely high-fidelity financial models of the enterprise and its businessenvironment. These models will be linked to their real-world counterparts, ensuring that changes in thenature or value of variables are quickly updated in the living model. This will improve the speed and ac-curacy of corporate reporting, drastically reduce manual data collection and processing, and enable auto-matic integration of financial systems among all business partners in a supply chain.

Financial analysis modules will accurately and completely model the long-term cost of a capital asset un-der possible acquisition, utilization, and disposition scenarios. The prognostic evaluation function ofthese systems will learn and adapt from experience, continually increasing accuracy of projections.Capital investment decisions will be based on evaluations of how well the projected expenditure supportsthe strategic directions of the enterprise, no longer simply yielding to the “best sales pitch.” Connected tothe enterprise’s internal and external information sources, the systems will continuously “troll” for rele-vant data (political developments, regulatory changes, R&D or product announcements, economic trends,news events, etc.) that bear on the enterprise’s financial interests. The system will provide appropriatealerts whenever such information affects the enterprise financial model, and intelligent advisors will aidexecutives and managers in quickly understanding the impact of the change and identifying options forcorrective action or exploitation of opportunities.

The new-found ease in modeling of enterprise finances will yield a powerful new flexibility in capitalmanagement. As an example, if model-based analysis determines that sufficient profit potential can berealized, new business lines (requiring new facilities, technologies, and equipment) can be pursued withpreviously unimaginable speed. With all levels of the enterprise operating off the same models and thesame data, and with far higher levels of confidence in the models than currently possible, the time re-quired to work financial issues “up the chain” will be reduced from days or weeks to minutes and hours.

Goals & Requirements for Financial & Capital Assets Management

• Goal 1: Unified Financial Modeling Environment – Establish standards and standard techniquesfor creation and use of financial modeling systems that are seamlessly interoperable between partnercompanies and multi-tiered supply chains. (M)

– Standards for Financial Models – Establish open standards for creation, communication, and im-plementation of interoperable financial models and modeling systems. (S)

– Model-Based Validation of Input Data – Develop modeling capabilities that interface with enter-prise information systems to validate all input data used in financial estimating and analysis. (M)

– Uniform Capital Investment Criteria & Assessment – Develop standardized investment andcapital asset evaluation criteria that provide decision guidelines for different kinds of capital invest-ments (facility, equipment, R&D). Establish key success criteria, probability and risk projection, andweighing mechanisms to support a model-based decision environment. (M)

• Goal 2: Knowledge-Based Financial Management & Strategy Advisors – Provide rule-based fi-nancial modeling systems augmented with intelligent advisors to enable rapid creation of robust andaccurate financial models. Ensure that the system is specifically tailored to meet the needs of smalland medium manufacturers. (M-L)

– Smart Modeling Templates – Develop user-friendly on-line advisors that aid users in “filling in theblanks” in creating cash flow models, charts of accounts, cost estimates, and other financial models,without requiring a spreadsheet interface. (S-M)



– Forward-Looking Costing Models – Develop systems that continuously collect, report, and projectcosts based on automated analysis of actuals, trends, and risk factors including inflation, cost ofmoney, currency rate fluctuations, fringe costs, supply shortages, etc. (M)

– Financial Trends Assessment – Provide a knowledge base and means to identify numerical andempirical economic trends to allow companies to make investment or divestiture decisions with afull understanding of the probable and potential economic environment over the defined time win-dows. Include the capability to provide automated risk assessment and automatically flag and char-acterize soft variables that are outside the enterprise’s control. (M)

– Model-Based Acquisition Targeting – Develop a system that can access commercial and govern-ment financial information sources and identify compatible capital acquisition targets based on keyparameters such as corporate objectives, cash/debt position, financial returns, and cost risk. (M)

– Scenario-Based Capital Investment Advisor – Develop a desktop “virtual cockpit” to allow man-agers to quickly and accurately evaluate various options for investment. Include the capability toverify assumptions and input data in real time based on information available from the enterpriseknowledge base. (M-L)

4.3.3 VISION FOR KNOWLEDGE MANAGEMENT & APPLICATIONS

Future knowledge applications will be fully integrated with modeling capabilities to support all strate-gic management processes. Both the explicit intellectual assets of the company and the intangible as-sets captured in the skills and knowledge of the workforce will be accurately reflected in the strategicenterprise model. Knowledge will be harvested both within and external to the enterprise, to continu-ally enrich the knowledge base. Decision support tools will tap into this dynamic knowledge base toenable the best decisions from the best possible information.

In the model-based enterprise, effective knowledge management and application will be an intrinsic com-ponent of all enterprise processes and systems. Enterprise models will reflect the explicit assets of thecompany, including intellectual capital as well as financial and physical assets and liabilities. Businessmanagement systems in all functional areas will use intelligent advisors to integrate new knowledgeautomatically and update their functional models to reflect current reality. These systems will integratethe know-how and institutional memory that is dispersed across the organization. This tacit knowledgeincludes problem-solving expertise, project management and engineering experience, process expertise,market understanding, and more. The capture of all this knowledge, its application, and integration withall the processes and models that run the enterprise, will provide a rich and powerful toolset for bettermanaging the complexity of the manufacturing enterprise from the strategic level.

Techniques such as competency modeling will be used unobtrusively in daily operations to capture theskills, values, and behaviors of top-performing employees. Intellectual assets will be modeled along withtechnical assets and production capacities, thus allowing more accurate analysis of options for enterpriseresponse to new opportunities and challenges.

Applying a deep ontological understanding of the industry sector, automated information monitoring,mining, and integration tools will capture and process massive amounts of data into useful knowledge.These systems will provide a continuously updated view of the competitive landscape, the market land-scape, the R&D landscape, and other views of interest to the enterprise. Integrated modeling tools willdraw on these resources to enable strategic managers to look into the future with unprecedented clarityand project the best decisions for the health of the enterprise today and sustainability for tomorrow.



Goals & Requirements for Knowledge Management & Applications

• Goal 1: Enterprise Knowledge Capture – Provide the capability to automatically capture lessonslearned and other forms of enterprise knowledge and make these assets available for reuse throughmodel-based processes and systems. (M)

– Knowledge Discovery for Corporate Memory – Develop knowledge management systems thatcapture information in digital form about the operations of the enterprise and convert that informa-tion to useful knowledge. Include the capability to continually review candidate materials and storeinformation in a knowledge repository for easy access and use. (M)

– Conversion of Lessons Learned to Actionable Knowledge – Develop standard-format models forcapturing lessons learned and systems to extract relevant data and information from the lessons-learned knowledge base. Develop systems to convert the data and information to knowledge that canbe institutionalized for inclusion in policies, procedures, and other corporate documents. (S-M)

• Goal 2: Knowledge-Based Strategy Advisors – Develop a suite of corporate/enterprise advisors aspart of the strategic management cockpit to support the decision processes in setting and maintainingcorporate direction. The advisors below are an initial set for consideration. (M)

– Competitive Positioning Advisor – Develop a model-based advisor supporting evaluation and de-termination of optimum strategies for competitive positioning based on enterprise strengths and thepositioning of primary and secondary competitors. (M)

– R&D Advisor – Develop a model-based advisor supporting evaluation and determination of opti-mum strategies for R&D investments based on a deep understanding of enterprise strategic direction,core competencies, and ongoing and planned R&D in areas of interest to the enterprise. (S-M)

– Capital Investment Advisor – Develop a model-based advisor supporting evaluation and determi-nation of optimum strategies for capital investment based on a full understanding of enterprise stra-tegic direction, financial resources, and economic/business/market forecasts. (M)

• Goal 3: Process-Based Ontologies – Develop ontologies by industry sector to capture the essence ofprocess knowledge and enable model-based characterization of all processes. (S-L)

– Common Ontologies – Develop ontologies for common manufacturing industry business processesto support development of knowledge management systems that can be put to use by any companywith little or no tailoring. (S)

– Sector-Specific Ontologies – Develop ontologies specific to industry sectors (e.g., electronics,automotive) and process classes (e.g., machining, forming) to enable characterization and knowledgeprocessing by model-based systems. (M-L)

• Goal 4: Model-Based Human Resource Management – Establish the capability to accurately modelhuman resources and quantify skill capabilities, training and qualification, attrition/turnover, andsimilar factors. (M)

– Job Requirements Profiles & Models – Establish methods to fully define job requirements in astandard model format. (S)

– Training & Qualification Models – Establish methods to fully define training, qualification, andcertification requirements in a standard model format. (S-M)

– Workforce Factors Models – Establish methods to model workforce composition, attrition, turn-over, retention, and similar factors in a standard model format. (M)



4.3.4 VISION FOR STRATEGIC PLANNING & EXECUTION

Strategic direction for the future manufacturing enterprise will be captured in a master enterprisemodel that automatically evaluates all current and planned activities for fit and contribution to the en-terprise plan and vision. The master model will provide a powerful framework for managing the plan-ning process and associated performance metrics, ensuring that the strategic plan is fully integratedinto, and with, all enterprise processes.

In the model-based enterprise, strategic planning, execution, and performance measurement will be anintegrated process that integrates both top-down and bottom-up perspectives. Desktop tools will enableexecutives and senior managers to digitally define the enterprise vision and mission; link those drivers toenterprise objectives, processes, and organizational elements (line of business, product line, operatingunit, department, etc.); and develop appropriate goals and performance assessment metrics. The systemwill aid in evaluating options for achieving the defined goals, selecting the best course of action, and en-suring that adequate resources are allocated. It will also aid in up-front development of alternatives, fall-back plans, and workarounds so that the enterprise can respond quickly as previously gamed contingen-cies arise.

A key benefit of this capability will be that executives and managers will have a systematic process fordefining strategic objectives and then setting realistic performance targets – instead of arbitrary goals –with clear strategies for achieving those targets.

The master enterprise model will continuously represent current strategic directions, and institutionalizethe strategic plan into the everyday business of the enterprise. The strategic planning maintenance proc-ess will operate dynamically in response to changes in direction or new information captured by the en-terprise model. It will interface with the execution systems that drive the process throughout the enter-prise, issuing updates for action to affected business units and organizations, and collecting performance,status, and progress data in order to compare results against the plan. Well-defined metrics built into theenterprise model and strategic plan will be applied to continuously monitor performance and measureprogress against strategic goals at every level of activity. Staff at every level of the organization will beclearly aware of the company’s strategic direction, and their role in fulfilling the plan. Senior manage-ment will have a continuous high-level view of the “state of the plan” and the ability to quickly drill downto whatever detail they desire.

Integration of the monitoring and surveillance system with the systems that execute the processes of thebusiness, down to the level of individual processes on the factory floor, will provide the capability toquickly flag and investigate potential problems before they escalate into crises. Automated performancemonitoring systems will autonomously flag negative performance trends or potentially serious incidentsand alert multiple appropriate levels of management to ensure that an issue is not merely addressed, butresolved in the best possible way.

Goals & Requirements for Strategic Planning & Execution

• Goal 1: Model-Based Strategic Planning – Provide a toolset that supports an entirely computer-based interactive strategic planning process readily useable by any type or size of manufacturing firm.(M)

– Strategic Planning System – Develop a generic strategic planning system that uses model-basedtechniques to develop and document the enterprise’s vision and goals, facilitate alignment of thegoals with the enterprise model, and create the framework for defining and managing requirementsin each element of the enterprise (business unit, process, facility, department etc.) to achieve the en-terprise vision. (M)



– Metrics & Performance Measures – Provide modeling and advisory systems that assist managersin assigning metrics and performance measures to each element of the strategic plan and flow thosemetrics into an implementation model. (M)

• Goal 2: Strategic Performance Monitoring – Provide the automated capability to collect data andinformation on a continuous basis to provide up-to-date assessment of the state of the enterpriseagainst the strategic plan. (M-L)

– Integrated Performance Monitoring – Develop approaches and techniques for integrating enter-prise performance reporting systems (e.g., cost/schedule status reporting systems) with the strategicplanning system to provide automated reporting and analysis of performance against strategic goals.(M)

– Occurrence Updating – Integrate operations occurrence reporting systems with the strategic plan-ning system to continuously refresh the strategic plan. (M)

– Strategic Alarms – Provide control limits based on defined performance metrics to automaticallytrigger alerts and alarms for deviations in performance from the strategic plan. Include the capabilityto rapidly model options for corrective action or changes to the plan. (M-L)

4.3.5 VISION FOR STRATEGIC OPERATIONS MANAGEMENT

Future operations processes will be seamlessly integrated with strategic planning and performancemonitoring processes to ensure that all operations are conducted in full concurrence with strategicobjectives.

Strategic operations management in the model-based enterprise will be characterized by a broad field ofvision and full integration with the enterprise’s higher-level business processes through the master enter-prise model. This model will put all challenges and opportunities in the context of immediate solution,strategic impact, and linkage to other functions. Some of the characteristics of the future state are dis-cussed below.

Design and manufacturing are fully integrated – Unity of design and manufacturing follows automati-cally if enterprise operation is based on integrated product and process design. A major component ofthat unity is the flow in both directions – to and from design and operations. Model-based systems willautomatically evaluate product and process design alternatives for life-cycle impact and value, continuallyfeeding preferred operations alternatives to the design function.

Business development and production are united – The strategic operations management function andthe business development function will set their directions based on the enterprise’s strategic goals, andjointly work within the framework of the master enterprise model. The master enterprise model, rich withoperations knowledge, will provide the capability to model business alternatives and provide go/no-godecisions about which opportunities to pursue. Modeling and simulation capabilities will also determinehow opportunities should be best pursued in order to maximize orders, sales, and profits, and take bestadvantage of operational resources.

Efficiency is balanced with capability – By thoroughly modeling enterprise capability, throughput anddemand scenarios, proposed rightsizing, and efficiency changes will be evaluated to determine their im-pact on enterprise ability to support both present and future demands. This will provide the means to sur-vive both surges and droughts.

Workforce capabilities are assured – Models of the enterprise will provide a continuously updated andaccurate projection of staff and skill-mix needs and will aid managers in selecting and implementing thebest strategies to meet those needs, including hiring, subcontracting, and employee education and devel-opment. Robust models of industry conditions will enable managers to optimize compensation and bene-



fits structures to remain competitive while ensuring that the human resources pipeline delivers the rightmix of staff to meet operational requirements. Model-based training will drastically reduce the time re-quired to bring new employees up the learning curve, and will provide the flexible cross-training requiredfor the enterprise to respond quickly to changing requirements.

Inventories are optimized – Optimization of inventories will be closely aligned with lean practices, bal-ancing efficiency and capability as discussed above. Manufacturing enterprises will use finely tunedmodels to adjust raw material, component, or subsystem acquisition, production activities, and logisticssystems to align with actual product orders. This will ensure the capability to keep inventories low – butsufficient – and protected from interruption.

Regulatory compliance is assured – Adherence to regulatory requirements will be assured throughmodel-based planning, design, operations management, and product life-cycle management systems thatinterface with on-line knowledge bases and integrate compliance into their processes. Robust product,process, and operations models will provide the capability to rapidly evaluate the implications and im-pacts of potential or pending regulatory changes. This will enable quick and efficient implementation ofchanges to minimize downtime and provide a market advantage over lagging competitors.

A less adversarial relationship between regulators and implementers will be supported by proactive com-munication, sharing of models, and impact analysis before regulations are drafted into law. Based onmodels (generic and specific) of the industries affected, lawmakers and regulators will be able to readilyevaluate the impact of a regulation on the cost and performance of affected companies and their products.The closer relationship fostered by better knowledge on both sides will strengthen the competitiveness ofAmerican manufacturing.

Seamless Global Operations – Models will be an integral part of business-to-business operations aroundthe world. Model-based planning systems will enable greatly improved decision processes in evaluatingoptions for global partnering, outsourcing, or siting of offshore facilities, taking into consideration com-plex factors such as political and economic stability as well as costs and distribution system impacts.These systems will also enable local optimization to realize efficient, harmonious operations in any re-gion, country, or locale, and transparent integration of dispersed operations regardless of language, cul-ture, currency, and time zones. Model-based operations management systems will also enable rapidanalysis of problems (e.g., work stoppages, economic instability) in global operations and will supportevaluation and selection of the best responses.

Strategic Lessons Learned from Tactical Experience – Model-based operations management systemswill interface with operational process systems to not only continuously optimize performance, but alsodetect problems and capture knowledge to support process improvements and mistake-proofing. Deci-sions now made on the factory floor will be visible at much higher levels, providing better oversight ofproblem responses. Decisions made on the floor today by one person with limited knowledge of the po-tential impact will be made by a team of knowledgeable people – supported by intelligent models thatenable fuller understanding of the potential consequences of different response options.

Goals & Requirements for Strategic Operations Management

• Goal 1: Unity of Design, Manufacturing, & Business Development – Provide intelligent, model-based systems that fully integrate design, manufacturing, and sales/marketing functions in order to takebest advantage of operational capabilities. (M-L)

– Operations-Focused Product Design – Develop design system interfaces and capabilities to accessa rich operations model that defines process options, process capabilities, limitations, risks, cost, andother relevant factors to ensure that product designs are producible and optimized for total value andoperational efficiency. (M)



– Integration of Business Development & Operations – Provide real-time systems that connect thebusiness developers with the operations function to evaluate any potential contract or order againstthe ability of the enterprise to deliver at a profit. Include the capability to rapidly develop and vali-date strategies and plans for meeting requirements that cannot be fulfilled by current capacity or ca-pability. (M)

– Living Process Models – Develop and establish a process model library that is continuously updatedfrom operations data and which enables operational planning systems to quantify process capabilityand capacity, identify areas for improvements, evaluate risks, and mitigate failure. This capability isparticularly critical for small and medium manufacturers who must continually seek optimized per-formance from equipment without aid of large engineering support organizations. (M-L)

– Life-Cycle Optimization – Develop intelligent operations life-cycle modeling capabilities that ac-cept input from multiple sources and continually update themselves to support optimization in prod-uct and process design, manufacturing, operation, support, and eventual disposition. (L)

• Goal 2: Model-Based Strategic Operations Management Toolset – Provide intelligent, model-based systems that fully integrate design, manufacturing, and sales/marketing functions in order to takebest advantage of operational capabilities. (M-L)

– Workforce Modeling – Develop an operations workforce modeling system that enables operationsmanagers to assess the attributes and performance of the current workforce against operational re-quirements and explicitly define future requirements and strategies to meet both near- and long-termprojected needs. Include the capability to evaluate different options including addition and trainingof new staff, and outsourcing of core or peak work. (M)

– Lean Advisor – Develop an optimization advisor that enables senior operations managers and staffto evaluate options for cost and capability reduction against the value of flexibility and capability.Include the ability to assess risk in moving operational capabilities to other entities (partners, sub-contractors) in a lean supply chain. (S)

– Compliance Advisor – Develop knowledge-based systems and models to evaluate regulations, de-termine the requirements and cost of compliance, highlight opportunities to reduce complexity, anddrive compliance implementation. Design the system to provide a collaborative environment that isshared with regulators to enable arbitration and best solutions. (M-L)

– Global Operations Advisor – Develop a model-based advisor system to aid operations strategists inevaluating options for international outsourcing or offshoring of operational capabilities. Include thecapability to build local operations models that interface with the master enterprise model and trans-parently manage language, time, financial, and other differences that impact business processes.(M-L)

– Operational Monitoring & Occurrence Reporting – Develop systems interfaces that enable de-tection, reporting, and resolution of deviations in operations. Develop intelligent models that assistthe operations staff in analyzing root causes and impacts, and determining the best response. (M)


MBE Project Plans 5-1

5.0 MBE PROJECT PLANS

This section presents the current compendium of white papers that were developed to outline the high-priority research, development, and implementation projects that support the NGMTI vision of futuremodel-based manufacturing enterprises.

The projects are presented in the order of priority that resulted from the NGMTI Technology AdvisoryBoard and Industry-Government Forum meetings held in late 2004 and early 2005. The projects are asfollows:

• MBE 13 – Information Delivery to Point of Use

• MBE 7 – Product-Driven Product & Process Design

• MBE 1 – Flexible Representation of Complex Models

• MBE 5 – Intelligent Models

• MBE 6 – Configuration Management for the Model-Based Enterprise

• MBE 3 – System-of-Systems Modeling for the Model-Based Enterprise

• MBE 4 – Enterprise-Wide Cost Modeling

• MBE 10 – Model-Based Distribution

• MBE 11 – Multi-Enterprise Collaboration

• MBE 8 – Model-Based Product Life Cycle Management

• MBE 9 – Model-Based, Real-Time Factory Operations

• MBE 2 – Shared Model Libraries

• MBE 12 – Model-Based Resource Management



NGMTI Project MBE-13

INFORMATION DELIVERY TO POINT OF USE

1.0 PROJECT SUMMARY

The objective of this project is to develop and demonstrate model-based technologies that deliver infor-mation to end users at the point of use, wherever that user might be. The information will be deliveredthrough flexible, affordable systems that provide for heads-up, hands-free operation. Concentrating ongraphical views of information provided by model-based systems, this project will demonstrate the shar-ing of information created in the enterprise’s planning processes (e.g., product design, manufacturingplanning) to the four primary “execution systems” of the enterprise: manufacturing execution, productservice/support, factory maintenance, and training. It will go far beyond the passing of visual files andtext, providing users with interactive access to physical and logical models useful in guiding operations,with appropriate levels of detail and security.

2.0 CHALLENGE

Model-based tools have been used for years in manufacturing enterprises, mostly in designing products,in operations planning, and in financial management.1 The model-based enterprise vision of NGMTIdeepens and adds to these capabilities and extends them to all functions of the enterprise. This presentsnew challenges in order to make the vision an operational reality. The kinds of systems needed to provideintegrated model-based capabilities are extremely complex, and the high degree of autonomy envisionedfor these systems makes the human-system interface critically important. The information delivered tousers must be accurate, clear, and free of unnecessary complexity. The interface must also enable rapid,intuitive interrogation to aid different kinds of users (novice and expert, engineer and business manager,etc.) in gaining a full understanding of the delivered data and in making the best decision based on thatdata. The information delivery systems must also be capable of easily accepting input to execute com-mands, modify models, update knowledge bases, rectify errors, and capture real-world experience andlessons learned. This requires that model-based systems have the ability to verify information inputs andarbitrate conflicting data, to ensure that all information accepted into the system is valid before actingupon it.

Managing all of the information sources that feed the underlying models that drive the processes andequipment of the model-based enterprise is likewise a huge barrier. Much valuable information is con-tained in legacy systems, electronic flat files, or hardcopy documents that are no longer in current use orare not readily convertible to open digital formats. Much vital data will also reside in, or be controlled by,shared models or information repositories external to the enterprise. The system must also have the flexi-bility to accommodate new human/machine interface technologies as they become commercially avail-able.

Many challenges must be met to achieve model-based information delivery to point of use. They are:

• Establishment of model-based planning systems that determine how processes need to operate andwhat information needs to be communicated.

• Creation of operational models that link the plan to the execution mechanisms (i.e., to the processsystems and equipment)

1 “Operations planning” in this context covers a wide range of functions, such as modeling of production capacity and distribution networks, cost

and resource estimating, optimization of manufacturing flows, logistics planning (e.g., modeling of spares requirements), and similar activities.



• Creation of logical, knowledge-based models that arbitrate situations to assure the proper solutions.

• Creation of user interface tools that provide robust, affordable, heads-up, hands-free access toneeded information.

Development of model-based processes and intelligent controls is key to realizing the Model-Based En-terprise vision. These requirements are addressed in several other NGMTI white papers, including Intel-ligent Models; Model-Based, Real-Time Factory Operations; System-of-Systems Modeling for the Model-Based Enterprise; and Product-Driven Product & Process Design.2

Certain infrastructure needs also must be addressed to provide information delivery to point of use acrossdifferent industries. The service will require delivery of very large data files and need to reach locationswhere high bandwidth – or even basic network connectivity – is not available. Mechanisms for ultra-highdata compression are essential, and compact media (e.g., flash memory devices) with terabyte capacitywill be the repositories of choice for operation in remote areas.

The specific challenge addressed by this project involves communication between the model-based sys-tems of the enterprise and the users at the point of use. The information provided must be convenientlypresented to the user, without impairing safety or worker functionality. There are wearable, hands-freevisualization products emerging that give operators information similar to cockpit heads-up displays andhelmet-mounted sight systems common in military aviation. In general, these first-generation commercialproducts lack the flexibility and power to satisfy a broad range of user requirements. The device tech-nologies need further capabilities as secure wireless communications and use-specific configuration tem-plates. Evolution of these technologies currently centers on mass-market devices that may not have thesafety, ergonomic or durability and cost factors solved for wide use in manufacturing or military envi-ronments. Most important, the ability to logically integrate massive amounts of complex information forintelligent, appropriate delivery in the manufacturing environment has not been addressed.

3.0 PROPOSED SOLUTION AND PROJECT PLAN

This project will develop the model-based integration and data interface performance requirementsneeded by future information delivery technologies. It will demonstrate the high value of integratingpoint-of-use information delivery from the enterprise’s planning processes to the four execution domains(“value vectors”) of the enterprise: 1) manufacturing execution, 2) service/support, 3) factory mainte-nance, and 4) training. The project, led by industry participants including John Deere, General Motors,Ford, Boeing, Procter & Gamble, Lockheed Martin, Honeywell Kansas City, and Caterpillar, will demon-strate the ability to not only fulfill the explicit functional needs of each execution domain and their sup-porting systems, but also to provide derivative forms or abstractions of models needed by downstreamand upstream processes.

The proposed project will develop and demonstrate the capability to deliver model-based information(about products, processes, equipment, and other facility information) to users in a largely visual/graphicform that is rich in detail, annotated as needed, and conveys the intent of the task in context to the point ofuse. The underlying models will be stored in a flexible, vendor-neutral format, and support delivery ofassociated information in the form needed at all points of use.

The information delivery tools will provide hands-free, wireless interactive presentation, thus ergonomi-cally enhancing productivity, reducing errors, and continuously enriching and validating the contents ofthe underlying models via both human and system/machine feedback. The system will support functionalworkflows naturally and non-invasively, providing information views that are specific to the user, task,and activity context. The delivery devices may be heads-up or local displays (for individuals), or largeglass screens (for work teams), and the system will provide the flexibility to accommodate new presenta-tion technologies such as holographic display, voice command, and other sensory interfaces.

2 These and other NGMTI white papers are available through the NGMTI Communities of Practice at www.ngmti.us.



The interactive information delivery mechanism will enable operator input to contribute to real-time vali-dation of underlying models against the actual physical reality of production, thus putting planning func-tions in a continuous feedback loop with the execution function. Information flows in real time from op-erators, users, and mechanical/electronic feedback mechanisms in the physical world and gets matchedagainst the associated information in the affected models, thus providing validation of the model-basedobject or process definitions.

All downstream execution functions are supported by the comprehensive product and process models andinformation delivery systems. For the Product Service/Support domain, instead of hardcopy files orshelves of paper and softcopy manuals for all models still in use, workers are supported by real-time de-livery of required and requested information. In the Factory Maintenance domain, equipment history andas-built and as-used information is available on line and just-in-time via the model, eliminating the needfor hanging file folders and hardcopy logs for each machine.

In the Training domain, formal training is reduced to primarily a skills inculcation and certification role.Task training is merged with real-time task support through real-time, just-in-time presentation of neededinformation needed at the moment. If training enables the operator to select the proper tools (whichwrench, procedure, etc.) for a task, then the task support component provides specific information (whichbolt next, where to apply the tool, with what torque, etc.).

Figure 3-1 provides a high-level view of the relationship of the enterprise’s model-based planning proc-esses with the execution domains (in this example, the manufacturing execution domain). The relation-ship for the other three domains (Product Service/Support, Factory Maintenance, and Training) is verysimilar.

Figure 3-1. Delivering needed information to the point of use requires seamless integration of model-basedprocesses to their corresponding execution functions.



3.1 GOALS AND REQUIREMENTS FOR INFORMATION DELIVERY TO POINT OF USE

The goals and requirements that must be met to realize the vision for this project outlined below. Addi-tional goals that relate to this topic, and define many of the underlying capabilities required to supportmodel-based information delivery, are provided in the NGMTI white papers for Intelligent Models;Model-Based, Real-Time Factory Operations; System-of-Systems Modeling for the Model-Based Enter-prise; Product-Driven Product & Process Design; and Configuration Management for the Model-BasedEnterprise.

• Goal 1: Authoring of Planning Information – For all planning information in the enterprise proc-ess models, provide mechanisms to identify critical characteristics and define the appropriate viewand presentation style needed by different enterprise functions over the life-cycle of the product,process or business function. (M)

– Integrated Information Framework – Define and demonstrate the integration and flow of infor-mation generated by model-based tools into a coherent and comprehensive model that can supportall enterprise planning and execution functions over the entire life-cycle. Use the framework toestablish a complete understanding of each function (including critical characteristics) and of itsrole in the larger operation, including upstream and downstream functions. (M)3

– Model-Based Information Authoring Tools – Develop automated authoring tools that enablecreation of appropriate point-of-use information extracted from process models and underlyingknowledge bases. For common types of models (e.g., product models), provide tools that auto-matically extract needed information with no human intervention and convey it to users for specifictasks. (M)

• Goal 2: Publishing & Distribution of Planning Information – Develop means of representing in-formation from planning functions in a highly functional, vendor-neutral format (e.g., XML) that iscompatible with enterprise resource planning/management systems and manufacturing executionsystems. Provide the ability to publish the information in different formats such as discrete pages,executable animations, etc. that are required for different tasks and to facilitate real-time delivery ofextremely large files. (M)

– Generic Data Storage – Provide standards-based data format storage solutions that can be man-aged by any major database management system and are accessible to any major ERP or MES orsupporting model-based applications. (S)

– Large File Delivery – Provide high-bandwidth communications and ultrahigh data compressiontechniques and other mechanisms to enable delivery of very large files in real or near-real time toremote parts of the enterprise. (M)

– Interconnected File Storage Management – Provide storage and data management solutions forefficient management of very large files and which enable rapid access to interconnected informa-tion stored and widely dispersed sites within and external to the enterprise. (M)

• Goal 3: Point of Use Information Delivery Devices – Provide technologies to enable hands-free,wireless information delivery with intuitive navigation and human interface, with emphasis on humanfactors such as safety and ergonomics. (S-M)

– Ergonomic Information Presentation – Provide information presentation and input/outputmechanisms, including hands-free displays, natural language interaction, etc., sufficient to cover abroad range of manufacturing work environments. (S-M)

3 This framework is defined primarily by goals outlined in the NGMTI White paper for Flexible Representation of Complex Models.



– Information Selection & Interface Mechanisms – For all manufacturing enterprise functions,develop convenient and easy-to-understand means of navigating through options and interfacingwith the system. Address technologies including voice-commanded selection/interrogation andsystem-enabled mechanisms (e.g. barcode or RF tag navigation). (S)

• Goal 4: Task-Appropriate Information Access – Provide easily navigable, real-time access to allneeded information from enterprise models and other sources, with appropriate security. (M)

– Information Rights Definition – Develop schemas that identify the appropriate information andlevel of detail and data protection required for every manufacturing enterprise function. This goalshould initially address one well-bounded manufacturing sector and then extend the solution toother sectors. (S)

– Functional Information Mapping – For each enterprise function defined under the InformationRights Definition requirement above, develop a mapping of information needs including level ofdetail needed, needed/desired delivery formats, and targeted presentation devices. (S)

– Information Security Mechanisms – Provide the capability to assess the information and level ofdetail requested by an enterprise function or user, determine the access privileges of user and pointof use, and then provide the information within the defined security constraints. (M)

• Goal 5: Integration of Legacy & External Information – Provide the capability to integrate andmaintain enterprise legacy information suitably along with new/current information. (M-L)

– Model Linkages to Major Legacy Systems – Develop techniques for organizing and presentinginformation derived from legacy modeling tools and information systems, and link these informa-tion structures into the comprehensive enterprise information management models. (M)

– Model Linkages to “Foreign” Systems – Develop techniques for organizing and creating custompresentations of information from custom applications and from systems external to the enterprise,and link these information structures into the comprehensive enterprise models. (M-L)

– Model Version Management System – Develop a system to manage all previous versions of allmodels used within the enterprise, to maintain a configuration audit trail and enable retrieval of themodels and associated data when needed. (M)

• Goal 6: In-Process Validation of Logical Models – Provide means of matching feedback fromphysical processes (e.g., via sensors, human interaction) against process models and performancemetrics, and issuing alerts and requests for actions if the situation does not match expectations.(S-M)

– Mapping Feedback to Models – Provide means of continuously comparing sensor readings or in-strument/equipment measurements or human observations to the performance expectations definedby the controlling models, and taking appropriate action when measured values exceed specifiedtolerances or indicate a negative trend. (S-M)

– Corrective Action & Alerting Capability – Develop broadly applicable ground rules to guidesystem and user response to off-normal events, including requests for intervention, prioritization ofintervention options, verification of requested action, and issuance of higher-level alerts (andlaunching of fail-safe actions) to ensure the problem is contained and properly mitigated. (S-M)



3.2 PROJECT STATEMENT OF WORK

Task 1 – Model-Based Information Authoring & Publishing: This task shall develop means for ex-tracting and representing information from enterprise planning processes in vendor-neutral formats thatare compatible with enterprise resource planning and manufacturing execution systems. The objective isto provide the capability to publish and manage model-based information and instruction sets that can beautomatically formatted for effective and secure delivery to the point of use, including remote locations.This task shall also address requirements and solutions for integration of information from legacy systemsand file formats.

Task 2 – Information Delivery & User Interface Toolset: This task shall develop a set of informationdelivery device options and user feedback mechanisms which are evaluated for form, validated for pur-pose, assured for safety, and durable for service in the manufacturing and support environment.

Task 3 – Point of Use Pilots

Task 3.1 – Model-Based Factory Operations: This task shall apply the capabilities developed under thepreceding tasks to demonstrate point-of-use information delivery for manufacturing execution in an in-dustrial setting for one or more industry sectors.

Task 3.2 – Model-Based Support of Maintenance & Repair: This task shall apply the capabilities de-veloped under the preceding tasks to demonstrate point-of-use information delivery for model-based fac-tory maintenance and product maintenance and repair.

Task 3.3 – Model-Based Support of Training: This task shall apply the capabilities developed underthe preceding tasks to demonstrate use of product and process models and point-of-use information deliv-ery systems for different types (e.g., factory worker, maintainer, product user) of training requirements.

Task 4 – Technology Extensions

Task 4.1 – New Information Delivery Capabilities: This task shall investigate visualization advancesbeyond present day technology and develop an understanding of likely advances, such as Augmented Re-ality, that can be applied over time to enhance model-based information delivery.

Task 4.2 – New Human Interface Capabilities: This task shall draw on emerging technologies to ex-tend current capabilities beyond simple voice commands to include mechanisms such as more extensivenatural language interaction, gesturing, eye tracking, and brainwave monitoring.

4.0 BENEFITS AND BUSINESS CASE

4.1 BENEFITS TO COMMERICAL INDUSTRY

The ability to “publish” model-based information on demand in convenient, interactive visual format toall enterprise functions will benefit all sectors of manufacturing. These benefits include:

• Closer adherence to the design and process plan, with fewer mistakes – thus increasing manufac-turing yields and other performance metrics while enabling fast, flawless response to problems,challenges, and changes in requirements.

• Greatly improved user understanding of processes, procedures, equipment, and systems – thus de-creasing time and cost for training and troubleshooting and radically shortening learning curves.

• Improved productivity through immediate access to definitive information.

• Reduced warranty costs for returns and allowances, thus offsetting the typical 2% gross cost impactthat most businesses incur.

• Improved ability for users – and enterprises – to deal with an increasingly complex and technicalproduct mix and work environment



• Rapid, accurate modeling of proposed changes, with virtual testing and analysis enabling more effi-cient switchovers and faster problem-solving.

• Reduced training, with instruction provided “just in time” to workers already trained in basic func-tional skills.

• In-depth product knowledge available to service personnel and end users even at remote locations,reducing the time and cost of maintenance and repair.

There are a number of significant advantages to be gained from wider, more effective use of informationdelivered from model-based systems. Quality, overall efficiency, and first-pass yield in manufacturingwill be enhanced by real-time knowledge and understanding of the product and its processing status. Andas the number and complexity of products in enterprise portfolios keeps growing, information delivery topoint of use will help companies address the problems of an aging skilled workforce and a more transientworkforce – facilitating transfer of knowledge and capabilities to the next generation of workers. Theuseful skill level of all workers will increase with real-time, hands-free access to comprehensive, model-based knowledge presented in highly graphical forms. This information will include work instructions forshop floor operations and equipment maintenance activity, and life-cycle support in product operation,service, and repair. It will also reduce the delays of off-line training and the language translation burdenassociated with global operations and increasingly multi-cultural work environments. John Deere, forexample, reports such techniques have provided a 90% savings in language translation costs for the 76languages they support for their manuals, procedures, and training documents.

Improved information delivery will help sustain high productivity for any manufacturing facility. Themodel-based factory management system will continuously monitor key performance indicators (KPIs)and other vital metrics by monitoring performance of all enterprise processes, systems, operations,equipment, tasks, etc. against their respective control models. Status and trends will be continuously dis-played to points of use, and any issues will be flagged for action by appropriate users. The point-of-useinformation delivery systems will provide those users with the intuitive tools they need to quickly under-stand the issue, evaluate options, and command the best response. This radically improved capability toanticipate and respond to problems will help companies large and small minimize planned or unplanneddowntime caused by changes in tooling or product requirements, equipment failures, scheduled and un-scheduled maintenance, technology insertions, etc. – anything that prevents being in smooth productionstatus.

4.2 BENEFITS TO DOD

The DoD will realize direct benefits from this project through the evolution of an increasingly more reli-able, capable, and responsive defense manufacturing base. Model-based capabilities and informationmanagement mechanisms will enable a faster, more cost-effective response to surge and mobilization re-quirements, reducing the lead time required to build up stocks for rapid-response deployments.

Likely the most visible impact will be in the logistics support realm, where model-based maintenance,repair, supply support, and training processes coupled with model-based information delivery mecha-nisms will reduce the cost and time of all logistics functions. A far larger proportion of skills training forweapon system operators and maintainers will be delivered at point of use, reducing the need for costlypart task trainers and time-consuming out-of-unit training. This concept is entirely consistent with DoD’sphilosophy for upcoming generations of weapon systems such as Joint Strike Fighter, where maintenancetechs will use the aircraft’s onboard maintenance interfaces and PDA devices to diagnose problems andcall up technical documentation and repair procedures before they roll the aircraft into a hangar.

The improved capability for multilingual support will also simplify interoperability with NATO and othercoalition forces, and reduce the cost and complexity of training and support for joint operations and for-eign military sales (FMS) programs. Greater emphasis on embedded training and on generic reconfigur-able information delivery devices for training also supports the future vision for U.S. allies. The United



Kingdom, for example, is aggressively exploring these technologies for implementation on programs suchas the Maritime Composite Training System (MCTS).

DoD’s emphasis on improved information delivery for battlefield command and control, particularly onprograms such as Future Combat Systems (FCS), is an area where military R&D and fielded systems iswell ahead of commercial industry in both concepts and technology. Opportunities for synergy shouldcertainly be explored in this NGMTI project, since commercial leverage can enable DoD to significantlyreduce the cost of acquisition and ownership for end-user information delivery devices.

5.0 PROJECT PLAN & RESOURCE REQUIREMENTS

The initial project plan outlined below spans 24 months with an estimated resource requirement of $9.75million, of which approximately $7 million is allocated for technology development and acquisition ofcommercial software and hardware required to integrate working systems for subsequent demonstrationsunder Task 3.

Task Duration Start Date Est. Cost

1.0 Model-Based Info Authoring/Publishing 2.75M1.1 Integrated Information Framework 12 mo Program Start 250K1.2 Model-Based Authoring 21 mo Mo 3 1.5M1.3 Data Storage & File Delivery/Management 6 mo Mo 6 250K1.4 Legacy Systems Integration/interface 24 mo Program Start 750K

2.0 Information Delivery & User Interface Toolset 4.0M2.1 Information Delivery Devices 18 mo Program Start 2.0M2.2 User Interface & Feedback 18 mo Mo 6 1.0M2.3 Information Requirements Mapping 6 mo Mo 6 250K2.4 Information Security 12 mo Mo 12 750K

3.0 Point of Use Pilots 2.0M3.1 Model-Based Factory Operations 3 mo Mo 12 1.0M3.2 Model-Based Maintenance & Repair 3 mo Mo 15 500K3.3 Model-Based Training 3 mo Mo 18 500K

4.0 Technology Extensions 1.0M4.1 New Info Delivery Capabilities 6 mo Mo 18 500K4.2 New Human Interface Capabilities 6 mo Mo 18 500K

6.0 RISK/READINESS ASSESSMENT

The risk for this project is assessed as high, due to the scope and complexity of information that must beaddressed, the wide variety of user environments, and the need to integrate or interface with potentiallyhundreds of existing applications and systems. Risk is mitigated by the availability of existing technolo-gies that can be leveraged to support demonstration of basic capabilities.

The technology readiness level is assessed at TRL 3-4, with elements such as hands-free displays being ininitial commercial use (TRL 7-8) and other elements, such as automated mode-based authoring, being inthe conceptual phase of development (TRL 2-3).



NGMTI PROJECT MBE-7

PRODUCT-DRIVEN PRODUCT & PROCESS DESIGN

1.0 PROJECT SUMMARY

The objective of this project is to develop and pilot modeling and simulation capabilities that enable aproduct model to automatically drive downstream applications. While “product design” creates a modelthat describes what is to be made, “process design” provides a model of how the product will be pro-duced. These are usually two very different kinds of models requiring highly skilled (and usually man-ual) translation of information from one domain to another. This project will demonstrate collaborativeinteraction between product and process models in an enterprise’s product realization environment, toevaluate the current state of capability and provide business-case data regarding the impacts of decisionsmade at each step of product design and manufacturing.

2.0 CHALLENGE

Model-based product realization liberally applies simulation in the design, engineering, manufacturing,and support processes of the enterprise. Product realization is the core of the manufacturing enterprisemission – the creation of products to generate revenue and fulfill the needs and wants of the enterprise’scustomers. However, product realization processes are highly interdependent with the other functions ofthe enterprise. In the model-based enterprise concept, these relationship are greatly heightened in impor-tance due to the intensive collaboration required among all processes, systems, and information flows(Figure 2-1).

Despite the widespread avail-ability of computer-aided de-sign (CAD) tools4, creation ofdigital product models israrely on the critical path ofproduct development. Simi-larly, process models are oftencreated at a high level to de-sign manufacturing flows or ata detailed level to help diag-nose a problem, but rarely as astandard tool to optimizeprocess designs. Historically,products and processes aredeveloped by creating andtesting a design to see howwell it works, then modifyingthe design and testing it again.Computer-aided design andanalysis tools have greatlyimproved the ability to arriveat ready-to-manufacture de-

4 CAD tools are currently estimated to have an installed base of 20 million users. (http://www.jonpeddie.com/special/CAD.shtml).

Figure 2-1. Future design and manufacturing systems will operate fromproduct models that link to all relevant information across all enterprise

processes and the entire product life cycle.



signs with fewer prototype iterations, but process development still too often relies on engineering draw-ings, specifications, and bills of material that are handed off to the production organization to figure outhow to make the product. The manufacturing team assesses the design to determine what can be made in-house and what needs to be subcontracted out, develops the manufacturing process plan and procedures(including fabrication, assembly, test, and inspection functions), and runs several iterations of pilot pro-duction to verify readiness to make deliverable product.

Most manufacturers today practice concurrent engineering and integrated product/process development(IPPD) by engaging the manufacturing team directly in product development. This helps optimize de-signs for features such as producibility, quality, reliability, and affordability, and reduces the time re-quired to move to initial production. Model-based tools are commonly used to lay out manufacturingflows, develop assembly procedures, and define skill and equipment resource requirements to meet thetarget schedule and production rates. However, use of detailed process simulation is very limited due tothe time and cost of creating the models, which restricts use to critical applications with a clear return oninvestment.

The lack of general awareness of (and confidence in) process simulation tools also makes it difficult tosecure support for process simulation development efforts, even though they have potentially large pay-offs in time, resources, and profitability. Government investment in this area has been limited for similarreasons.

For chemicals and other continuous process industry products, the product starts off as a material trans-formation model and a material balance sheet. These drive the design of the process systems and manu-facturing plan. Simulation is used extensively to engineer the process for throughput, quality, and safety.However, the process design is usually made without detailed consideration of control parameters. Thisis a major deficiency considering that design decisions determine up to 40% of the process cost.

In the NGMTI vision for the model-based enterprise, product and process modeling functions will trulycollaborate. Creation of a product design will automatically “pull” associated manufacturing processmodels, material models, and supplier capability models, enabling the product realization team to delivera complete manufacturing plan concurrent with completion of the product design. Intelligent advisortools will aid designers inoptimizing design features,tolerances, and material se-lections to take best advan-tage of process capabilities.The result is a total productdesign that can be “down-loaded” for manufacturingexecution (Figure 2-2).

Realizing this vision is asignificant challenge. Mate-rials engineering and manu-facturing process design arecurrently not well integratedwith product design appli-cations. Geometric repre-sentations are not mathe-matically complete, nor arethey sufficiently precise fordirect use in process designand control. Process simu-

Figure 2-2. In the NGMTI vision, manufacturing execution specificationsand plans will be downloaded directly from the product/process

model to drive and control all manufacturing processes.



lations are often independent of the actual design and, in general, results are communicated manually tothe design team. Material models are not scaleable and have limited ability to account for variability inmaterial quality (e.g., impurities).

Collaborating product/process models are widely regarded as a need, but the needed tools have not yetemerged. Many engineering computing tools exist to facilitate the transition of a product from the con-ceptual stage to a final, detailed design. While electronic product data exchange is common, it often re-quires human intervention to ensure accuracy of results.

Interoperability of simulation tools is sorely lacking in the process realm. While discrete simulations maydeal with product stress and temperature profiles during individual processes, they seldom address thetotal performance profiles of products and processes across multiple operations. Concurrent optimizationof multiple product and process parameters is likewise rare.

Lack of standards is a major concern across all model-based applications for product and process design.Compatibility in product data exchange, representation standards, compatibility of simulation systemswith process information management systems, and scaleability from micro to macro levels, all must beaddressed to provide cost-effective and robust simulation capabilities.

Industry lacks collaboration strategies to solve these basic issues, and export control regulations imposeadditional restrictions (particularly in the aerospace/defense sector) on sharing of product and process in-formation across international supply chain. Solving all of these challenges requires a large-scale andcoordinated effort between the manufacturing user community, which must specify required functionali-ties and integration needs, and tool developer/vendor community, which must deliver affordable solutionsthat meet requirements for both small and large manufacturers.


The full set of goals and requirements for product-driven product and process development as defined inthe NGMTI Roadmap for the Model-Based Enterprise is given in Section 3.1. Delivering the full extentof capability defined by many of these goals will be an evolutionary process spanning many years oftechnology development, incremental implementation, and extension across different sectors. This pro-ject will develop that part of the set that represents the next step in enabling product models to automati-cally drive downstream manufacturing and support processes. The project will demonstrate seamlessinteroperability of product and process models in a “system-of-systems” enterprise environment.

3.1 GOALS AND REQUIREMENTS FOR PRODUCT-DRIVEN PRODUCT & PROCESS DEVELOPMENT

• Goal 1: Automated Comprehensive Product & Process Design – Provide the capability to createand optimize a complete product and process definition containing or linking to all related specifica-tions, requirements, analytical results, or other pertinent information. (M-L)5

– Common Product & Process Specification Standards – For different industry sectors and prod-uct types, develop standards for defining product and process specifications that can be accessed bythe design system and that are consistent with the parameters, attributes, and features on whichproduct and process designs in each sector are based. (S)

– Design Knowledge Base – Develop a knowledge base of certified materials, commercial compo-nents, and manufacturing process and equipment data and models that is accessible and directlyuseable by human designers and automated design tools. (S-M)

– Sector-Specific Design Knowledge Bases – Extend the basic design knowledge base with sector-specific information and knowledge to support the unique needs of each industry. Include appro-





priate provisions for security and control of proprietary, export controlled, and classified data. (M-L)

– Multi-Attribute Manufacturability Assessment – Develop and integrate design evaluation toolsthat interface with the enterprise’s process and facilities knowledge base to assess manufacturabil-ity and provide feedback on ways to improve manufacturability in terms of both feasibility andcost. Include the capability to simultaneously evaluate multiple design attributes to enable rapidoptimization of product designs for efficient manufacture. (M-L)

– Unified Performance Evaluation Applications – Assess existing performance evaluation appli-cations (i.e., analytical tools) and develop a framework for integrating those applications into a uni-fied development environment for specific classes of products and processes. Conduct a gapanalysis to define the extensions required to various tools to support the integrated environmentand to identify what new analytical capabilities need to be developed. Initiate development of themissing capabilities. (M-L)

– Automated Design for Assembly – Develop extensions to current CAD tools to automatically as-sess assembly attributes for components, parts, and subsystems and guide the product designer inoptimizing the design for ease, speed, reliability, and cost of assembly as well as future mainte-nance of the delivered product. Include the capability to automatically repair or flag features thatrequire nonstandard tools, fixtures, or assembly aids. (M)

– Integral Packaging Design – Extend product modeling systems to include packaging issues as anintegral design factor contributing to minimum product cost and assuring product protec-tion/preservation and compatibility with handling and transport systems. (S)

– Manufacturing Capability Interface – Develop product model interface mechanisms that providereal-time access to information that affects producibility and production cost, including mate-rial/commodity/supplier availability as well as enterprise manufacturing capability. (M)

– Accurate Process Simulation Tools – Develop effective simulation tools that address materialsand manufacturing processes of interest to industry, and are validated against certification stan-dards within defined boundaries and parameters. (M)

– Material & Process Advisors – Create knowledge-based process advisors for individual materialsand manufacturing processes to support a variety of design and engineering functions. Develop amethodology to capture the needed knowledge and develop rule sets for existing materials andmanufacturing processes based on guides, handbooks, and other industry reference resources. (M)

– Model-Based Material Transformation – Develop applications to design transformation proc-esses for the best result from a scientific understanding of the interactions involved, enabling proc-essing in ways that deliver compliant product with minimum waste. (M-L)

– High-Fidelity Multi-Process Analysis – Develop a suite of analytical applications that accuratelypredict the results (including time, cost, and environmental considerations) of different manufac-turing processes and materials for a given component, part, or formulation. (M-L)

– Rapid Product/Process Design Optimization – Develop modeling and simulation applications toprovide rapid exploration, evaluation, and selection of optimal options in product and process de-sign. Include the capability to launch analytical applications from the desktop CAD/PDM interfaceand have them execute automatically, with no dependence on the user. (M-L)

– Automated Product/Process Definition – Develop the capability to automatically create a com-plete and unambiguous, computer-sensible product and process definition that includes all infor-mation needed to manufacture the product. (L)



– Design Conventions Development & Learning System – Develop the capability to non-intrusively monitor the development process and learn from the decisions made by design teams.Apply this knowledge to develop a continuous learning system that automatically executes thesteps in the design process and defines voids in the knowledge base or the rule set, to support evo-lution of expert automated design systems. (L)

• Goal 2: Multi-Scale Material Modeling Capability – Develop material modeling and analysistechnologies enabling scaling of properties and behaviors from micro (e.g., molecular) to macro(product) levels to support development of robust product models that accurately reflect the influenceof real-world material properties. (L)

– Molecular Material Modeling – Extend existing, and develop new, molecular modeling tools forhigh-priority classes of materials to enhance understanding of material properties and capture, incomputer-sensible form, the relationship of molecular properties to material variability. (S-M)

– Material Transformation Model Repository – Establish an industry-wide shared repository thataddresses material phase transformation and the impact of various attributes on transformationprocesses. Include standardized, validated transformation models plus tools to use these models tosupport design, optimization, and troubleshooting of transformation processes. (M)

– Integrated Material Modeling – Develop interfaces from product and process modeling systemsto material models and knowledge bases so that the properties and behaviors of the product andprocess design accurately reflect the properties of the materials used. (M-L)

– Multi-Scale Material Modeling – Develop capabilities to predict macro-level process behaviorsresulting from micro-structural material attributes, and address requirements on microstructure toattain desired macro properties in-process and in the finished product. (L)

– Multi-Scale Process Modeling – Identify high-priority material needs for micro-scale models tosupport high-fidelity process modeling. Develop material behavior models for critical micro-scalephenomena such as grain growth and size fractions, dislocations, and crystal structure. Providemodeling tools that manage the linkages and information exchange between levels. (L)

• Goal 3: Automated Process Planning – Provide the capability to automatically generate the proc-ess plan as the product is being designed, consistent with product attributes, processing capabilities,enterprise and supply chain resources, and enterprise business objectives. (L)

– Process & Resource Capability Models – Develop tools and techniques for creating process andresource models that capture complete descriptions of enterprise manufacturing resources andprocess capabilities, enabling plug-and-play integration of resource/capability models throughevery level of the supply chain. (S)

– Process Model Repository – Define required process attributes and standard formats for processmodels that support automated process planning and manufacturing execution, and establish ashared repository of process models for use by different industry sectors. (S)

– Shared Process Models – Work with industry to acquire well-characterized models of commonprocesses and make those models available via the Process Model Repository. Assess gaps in theprocess set to define high-priority model needs for critical processes, and initiate development ofthe required models. (S)

– Multi-Level Interoperable Process Models – Develop capabilities and standards enabling inte-gration of multi-element process models at the unit process, line, shop floor, factory, and enterpriselevels. (M)



– Automatic Process Requirements Extraction – Develop the capability to extract process re-quirements from higher-level process models in order to evaluate applicability for specific pur-poses (e.g., to evaluate the ability of an existing process system to make a new product). (M)

– Planning System Interface to Factory Floor – Create interfaces between process planning sys-tems and enterprise information systems to guide decision-making for optimizing manufacturingprocess plans with respect to factors such as process flow, manpower/skills requirements, andproduct mix). (M)

– Multi-Step Process Planning – Develop the capability to perform "what-if" and sensitivity analy-ses to optimize multi-step processes and generate simulations and operational models for processcontrol and verification. (M-L)

– Technology Insertion Modeling – Develop modeling tools to create structured plans for deploy-ing process technology advances across the life cycle of a production line or facility, supporting in-sertion of new capabilities to extend process life or meet future requirements for increased capabil-ity or capacity or shifts to different products. Include the capability for financial analysis to evalu-ate issues related to capital expenditure and return on investment. (M)

– Process Planning Direct from Product/Process Design – Develop generative planning systemsthat operate directly from product and process definitions to provide all information needed todrive product manufacture. Include the capability to incorporate material and unit process modelsthrough transparent interfaces to enterprise model libraries and knowledge bases. (L)

• Goal 4: Tools to Manage Product/Process Development Uncertainty & Risk – Develop modelingand simulation aids that enable effective management of risk, uncertainty, and sensitivities in productand process development. (M-L)

– Uncertainty Bounding Techniques – Develop mechanisms, techniques, and protocols for identi-fying, quantifying, and providing adequate margins for uncertainty and risk in complex product andprocess models. (M)

– Robustness Evaluation – Develop performance modeling systems that determine the sensitivitiesof the design, quantify uncertainties, and define the robustness of product solutions. (M)

– Automated Risk Scoring – Develop a modeling utility that automatically assesses a proposedproduct or process design, automatically scores it for technical, schedule, and cost risk based ontechnology maturity (e.g., technology readiness level) and design uncertainties, and creates a pri-oritized assessment of the detected risks. (M)

– Multi-Scale Uncertainty Management – Create methodologies for accounting for and trackingthe uncertainties associated with materials, component designs, subsystem designs, and other de-velopmental elements in system-level product and process models. (M)

– Product/Process Risk Mitigation Tools – Develop modeling tools that capture risk items from theproduct/process design and aid in development and monitoring of mitigation actions. Include busi-ness-case templates to quantify the benefits (cost savings, performance, and life-cycle advantages)of a high-risk product or process element to support management decision processes. (M)

– Automated Risk Minimization – Develop a modeling utility that draws on product and processexperience captured in the enterprise knowledge base (or shared knowledge bases maintained byindustry sectors) to recommend lower-risk alternatives for risk elements identified in a product orprocess design. (M-L)



• Goal 5: Model-Based Product Assurance – Develop methodologies and standards for virtual test-ing methods that use simulations in lieu of physical testing in product and process development, overtime eliminating all but safety/performance-critical physical tests. (L)

– Model-Based Quality Assurance – For different industry sectors, develop quality assurance stan-dards for verifying product and process models and for certifying analytical simulation resultswithin clear bounds of confidence for specific applications. (M-L)

– Self-Validating Designs – Develop simulation and analysis tools that verify component, subsys-tem, and system designs against requirements and quality assurance criteria, reducing or eliminat-ing the need for iterative physical testing in product and process development. (M-L)

– Model-Based Certification of Production Readiness – Develop the capability to test and validatemodels of product designs and their simulated manufacturing processes with sufficient fidelity andaccuracy that production readiness can be certified by simulation. (L)


This NGMTI pilot project will develop simulation capabilities that enable a product model to drive down-stream process planning and manufacturing planning applications, and provide tools to optimize productdesigns for all attributes of interest. The project will demonstrate seamless interoperability of product andprocess models in one or more representative industry environments for a selected family of products, andcreate a number of shareable knowledge bases and model repositories. At its conclusion, the project willprovide detailed information useful for developing business cases for further development in this area.

The project plan has five major tasks as discussed below. For each of the tool, model, or knowledge basedevelopments, maximum leverage will be obtained by using best-in-class tools and existing data assets asa point of departure for achieving the desired capabilities. The major tasks are as follows:

Task 1 – Project Organization: This task shall bring together technical contributors from both commer-cial and government areas. The team will produce a detailed plan defining the specific scope, deliver-ables, schedule, and approach for developing and demonstrating the specified capabilities.

Task 2 – Automated Comprehensive Product & Design: This task shall develop, for a representativeproduct family in a selected industry sector, initial capabilities to create and optimize a complete productand process definition that contains or links to all related specifications, requirements, analytical results,and other pertinent information. Specific subtasks include:

1. Creation of common product and process specification standards

2. Development of a design knowledge base

3. Development of automated design for assembly tools

4. Integration of product and process simulation tools.

Each of these capabilities will leverage and extend existing applications and tools (e.g., analytical codes)to the maximum extent possible.

Task 3 – Automated Process Planning: This task shall develop the initial capabilities to automaticallygenerate, for the product design developed under Task 2, the associated process plans and manufacturingexecution plan. Specific subtasks include:

1. Development of a process model repository

2. Development of shared process and resource capability models

3. Development of multi-level interoperable process models.



Task 4 – Product/Process Development Uncertainty & Risk: This task shall develop modeling andsimulation aids that enable effective management of risk, uncertainty, and sensitivities in product andprocess development. Capabilities to be developed include uncertainty bounding techniques, robustnessevaluation tools, and automated risk assessment and mitigation planning tools.

Task 5 – Product-Driven Environment Demo: The final deliverable from this project shall be a demon-stration of the developed technologies at one or more selected industry sites and assessment of benefits ofautomating the interface between product design and process planning. The demonstration results will bedocumented along with recommendations for future efforts.


4.1 BENEFITS TO DOD

The DoD will realize the same benefits as commercial industry as described below, but the reductions inthe cost and time of moving new military systems to production are expected to be orders of magnitudegreater due to the more complex development cycles of DoD products. The ability to drive manufactur-ing execution directly from the product design is key to delivering the next generation of Simulation-Based Acquisition (SBA) capabilities. It will enable military products to be much better optimized en-tering Engineering and Manufacturing Development (EMD) and Low-Rate Initial Production (LRIP),thus significantly reducing the level and extent of design changes typical for new production systems.This in turn will reduce cost impacts on training and maintenance system development as well as scheduleimpacts on deployment milestones. The ability to thoroughly simulate all aspects of product usage willalso enable far better optimization of product operation and maintenance attributes, including skills re-quirements and safety hazards. These capabilities will be particularly valuable for systems now early indevelopment, such as the Navy’s DD(X) and Littoral Combat Ship, and upcoming generations of un-manned combat air vehicles (UCAVs). The capabilities developed will also benefit programs expected tobe in early production in the 2010 timeframe, such as Joint Common Missile (JCM) and the Future Com-bat Systems (FCS) family of vehicles and sensors, providing tools to further optimize designs for pro-ducibility, reliability, and cost reduction.

The creation of product and process model repositories will directly support DoD’s goals for greatercommonality across new weapon systems and implementation of horizontal technology insertion (HTI)upgrades to fielded systems, particularly with respect to electronics and sensors. Sharing of these kinds ofmodels does present unique issues for DoD, including control of classified data and resolution of con-tractor data rights issues. Although none of these issues are intended to be addressed by this project, DoDinput and guidance will be solicited to define a workable strategy for future implementation.

The risk analysis and mitigation tools to be developed by this project are expected to be of particularvalue to DoD, enabling contractors to do a much more thorough job of addressing risks in each stage ofproduct development. With DoD using the exact same toolset, risks can be assessed and managed with amuch higher degree of certainty than is possible today.

4.2 BENEFITS TO COMMERCIAL INDUSTRY

This project will deliver significant improvements in product and process development capability for allsectors of the U.S. manufacturing industry. Most of the functional capabilities will not be radically new;what will be new is the extremely high level of integration and concurrency enabled by the product-drivenproduct and process development environment. Engineering analysis functions and business planningthat today are performed off-line at great time and expense – and routinely invalidated whenever a con-figuration is modified or a requirement changes – will be launched from the designer’s desktop with asingle command. Re-running of analyses and recalculation of plans to respond to design changes will beautomatic, eliminating labor-intensive tasks such as updating bills of material, procurement packages, andcost estimates. This will radically reduce the time and cost of moving from initial design to production,



and will greatly improve the ability of product teams to optimize the product design, process design, andmanufacturing strategy for total satisfaction of all customer and stakeholder requirements.


The proposed project schedule and estimated resource requirements are provided below. The initial esti-mate for the 38-month effort is $7 million.


The proposed project is assessed as high to moderate in risk. While many of the capabilities to be devel-oped will leverage existing tools, securing participation of major tool vendors – particularly for the CADfunctions – will be an issue. These vendors typically keep their technology developments focused on ad-dition of incremental capabilities, and have not been responsive to pressure from the user community toaddress compatibility and interoperability issues across competing tools. Computational demands arealso an issue. Even with leading-edge computing tools, complex simulations and analyses require hoursor even days of run time, which is a major barrier to delivering the near-real-time simulation capabilitiesrequired to meet the goals of the project.

The project must also be ready to bound the requirements for some of the envisioned tools, particularly inthe area of design risk analysis, since it is unlikely that all of the potential risk factors applicable to acomplex design can be quantified to the extent required for accurate modeling.

Technology readiness for product-driven product and process design is assessed at TRL 2 to 3. Model-based tools are available and in routine use for product and process design, and integrated product/processdevelopment environments are maturing, but the level of interoperability and the types of functionalitytargeted for this project are presently in a visionary stage. Significant development is required to advancethe technology to a point where it can be practically implemented.



NGMTI PROJECT MBE-1

FLEXIBLE REPRESENTATION OF COMPLEX MODELS

1.0 PROJECT SUMMARY

The objective of this project is to develop the capability to create a product model that is rich enough tosupport all development, production, support, and end-of-life disposition activities throughout the productlife-cycle. The resulting product model will have the flexibility and power to quickly provide the exact“view” of the model or underlying data to support desired functions. The model of the product (and itsassociated manufacturing and support processes) will integrate all needed information, either within themodel or by linking to data within the enterprise or accessible from external sources.

2.0 CHALLENGE

Design and manufacturing have become computing-intensive activities, with digital design and electroni-cally controlled processing equipment in widespread use in every industry sector. However, for manymanufacturers – particularly the small firms that comprise the bulk of the U.S. manufacturing base –modeling and simulation remain too complex and costly to be mainstream tools. More importantly, whilecomputer-aided design (CAD) and computer-aided manufacturing (CAM) have become commonplace,for the most part we still work from the same foundations that have been in place for 30 years. The solidgeometry models of the 1990s were a major step forward, but there remain great limitations on the abilityto actually use these models for more than visual depiction.

While there are well-publicized successes in the use of modeling and simulation for product and processdesign, the tools remain largely geometry-based and not integrated with downstream production processesor associated functions such as procurement, tooling preparation, labor scheduling, or facility mainte-nance. High-fidelity rendering and visualization remain time-consuming tasks on even the fastest desktopPCs; and computationally intensive processes such as finite element meshing and running of analyticalcodes can take days. Preparation of specialized inputs from product models for analysis applications canrequire weeks of work by expert analysts, and time and cost typically preclude re-running analyses whendesigns change. For these reasons, new products look almost exactly like old ones, with new technologyintroduction limited to a very few functions with each model, and costly repetition of old problems.

Interoperability of modeling systems is a key challenge. Interfacing and melding of different kinds ofmodels – or similar kinds of models generated by different applications – is a major barrier to the inter-connected product and process modeling needed to support true model-based engineering and businessprocesses. Getting information from application to another typically requires manual reentry of the dataor translation and cleanup, which is inefficient as well as a significant source of errors.

Much progress has been made, but, in the main, this has been with single-source solutions. Limited inte-gration has been successful, but typically only when a large company mandates use of a common toolsetby its supply chain partners. This creates a problem for smaller companies who may have to acquire ex-pensive software and skills for multiple tools to support their various contracts. A neutral framework forintegrating different tools and information sources could save billions of dollars each year just by avoid-ing data incompatibility and eliminating the need to translate or re-enter the same data multiple times indifferent systems.

There are pointers to the future. For example, progress has been made in model-based product manage-ment, including the recently concluded NIST-funded Federated Intelligent Product Environment (FIPER)project. FIPER’s goal was to “streamline the design of highly engineered products, integrating legacy



and best-of-breed design and analysis tools through a web-enabled environment” (Figure 2-1). As a resultof this work, Engineous Software now has commercially available products (iSight and FIPER 1.5) thatprovide an initial infrastructure for model-based engineering design collaboration.6 The FIPER systemuses custom-built software “wrappers” to interface existing tools and models, enabling design engineersto more easily evaluate design options.

Another aspect of model-based design of products is addressed by Cognition’s Six Sigma Cockpit (SSC).With SSC, design engineers can visually explore the entire design space for a product and its parameters(customer requirements, features critical to quality, etc.); manage the many-to-many interaction relation-ships among those parameters; and control the applications and documents involved in design for SixSigma.7

Achieving the ability to access all model-basedtools from a single product model interface, ac-curately and quickly evaluate alternatives, anddeal with uncertainty and risk across differentdisciplines, offers great opportunity for im-provement in all areas of design, manufacture,and product support. It is generally accepted that80% of the ultimate cost of a product is commit-ted in the first 20% of the development phase.Even with all the improvements over the last fewdecades, design and manufacturing flaws con-tinue to cost billions of dollars per year in prod-uct corrections, degraded performance, andsafety problems. Modeling and simulation withintegrated tools are essential technologies tomeet this challenge, allowing good design deci-sions to be made, risk and uncertainty to be accu-rately assessed, and failure modes to be explored and “engineered out” in the virtual realm so that prod-ucts perform as intended.

The key challenge is to develop a comprehensive, computer-based representation of a product (be it onemodel or an integrated suite of mod-els) that is complete in its ability tocapture all information about a prod-uct and to support all analysis anddownstream manufacturing and life-cycle support applications (Figure 2-2). The product model must be ableto communicate and negotiate its in-put and output requirements auto-matically, without requiring customwrappers or other manual interfaces.A shared knowledge repository willmanage integration of the productinformation and integrate this infor-mation with the applications.

6 “A Distributed, Component-Based Integration Environment for Multidisciplinary Optimal and Quality Design,” Brett A. Wujek et al.

http://www.fiperproject.com/pdf_files/fiper_engineous.pdf.7 “Six Sigma Cockpit and Design for Six Sigma.” http://www.ci.com/products/ssc_proddesc.htm.

Figure 2-1. FIPER’s goal was to establish a distributed,web-based product development environment that

integrates leading-edge and legacy design applications.

Figure 2-2. The solution approach for flexible representationfocuses on a complete product model that contains or links to all data

needed for design, manufacturing, and product support.



The components of the product model must be interoperable with each other and with the informationsources and downstream applications from multiple domains, including the manufacturing processes in-volved with producing the product. In each case, the model must be able to deliver specific views of theinformation at different levels of abstraction as needed by different processes, applications, or users.

Achieving this goal requires that three tightly coupled issues be solved together.

Interoperability can be solved in the framework of automated model generation. First, the product modelmust support the input requirements of all of the applications that interact with it. Second, it must be ableto provide needed information automatically, without manual translation. There are two approaches tosolving these problems. Neutral-format product definition representation and plug-and-play compatibilityof model-based applications is the preferred approach. The second approach, being used as a migrationstrategy while the required technologies are developed and standards established, is to use applicationprogramming interfaces (APIs) and wrappers to provide compatibility with individual applications.

Levels of abstraction are the heart of the issue of flexible representation of complex models. The abilityto automatically spawn all needed models from a “master” product model is the foundation of a revolu-tion in design and manufacturing. An abstraction in this context is an aspect of a model separatelyviewed or manipulated to serve a purpose; e.g., an analysis, generation of a production plan, or prepara-tion of control instructions for directing a process. More simply, an abstraction is a “sub-model” suffi-cient to support a specific need. If the product model enables capture (through a combination of embed-ding and linking) and representation of all product attributes via a single interface, then custom views fordifferent applications and different users can be easily produced.

Multi-domain support means that all enterprise functions interface with the product model. Mechanical,electrical, aerodynamic, reliability, affordability, and other analyses can be launched as needed (or evenrun continuously in the background) because the single model supports all of these functions. This wouldalso enable needed analyses to be re-run automatically whenever an aspect of the design changes, thusproviding clear visibility of the consequences of the change, supporting change propagation, and enablingconcurrent optimization of all attributes to make the product the best it can be.


The solution approach for flexible representation is focused on providing the capability to create a com-plete product model that contains or transparently links to all of the information needed for all design,manufacturing, and downstream support applications in the particular product domain. This is a complexchallenge because of the tremendous scope of the downstream applications environment.

Some of the key features that must be included in the product model solution include:

• Ability to seamlessly integrate individual component and material models (with associated physi-cal, chemical, electrical, etc. properties) to create complex, multi-part/constituent product modelsthat can “plug-and-play” with higher-level models.

• Support for different levels of fidelity, with clear definition of limits of uncertainty and risk at eachlevel, to enable quick-look analysis with short simulation run times.

• Dynamic synchronization among all model elements, enabling cascading of changes in one part ofthe model to all other parts affected by the change, with appropriate alerts to affected functions.

• Ability to embed captured product knowledge into the models to take full advantage of experience,lessons learned, and other forms of real-world feedback.

• Support for trade-off analysis and optimization including risk, uncertainty, and variability.

• Ability to automatically extract only that information needed to make specific product decisions,and present that information in easily understandable forms at different levels of detail as requested



by the user. This includes the capability to instantly provide different "slices" of data to supportexploration of options and technical/business decisions.

• Ability for models to understand and communicate features in human-sensible terms (e.g. "slot" or"hole") to track requirements through design and production and to enable human query of themodel-based product management system.

The solution is highly dependent on the ability of the enterprise infrastructure to provide real-time accessto supporting information from all sectors and functions of the enterprise, including its partners and sup-ply chain members.

3.1 GOALS AND REQUIREMENTS FOR FLEXIBLE REPRESENTATION OF COMPLEX MODELS

The vision for flexible representation of complex models, and the ability to provide specific views or ver-sions (or abstractions) of the product model to support specific technical or business functions, requirescapabilities far beyond retrieval and visualization of data. Many of these capabilities are defined in the"Crosscutting Goals" outlined in the NGMTI Roadmap for the Model-Based Enterprise. Of the crosscut-ting goals documented in the MBE roadmap, the following two goals are most specifically relevant to thisproject. Two other goals (System of Systems Modeling Capability, and Intelligent Models) are addressedin separate NGMTI MBE white papers.

• Goal 1: Flexible Representation of Complex Models – Provide modeling technologies that enablecapture and representation of all design, manufacturing, and support attributes in a comprehensive,continuously current, computer-based model that enables real-time selectable, customizable views bydifferent users and applications. (L)8

– Full Product Model Representation – Develop technologies and standards enabling creation andautomated updating of a complete, mathematically accurate product model that allows all enterprisesystems and applications to interact with it through standard interfaces. The resulting models mustbe able to accurately capture and communicate customer requirements, design intent, physical andnonphysical attributes and their relationships, functional performance, and include parametric fea-ture definition for design and manufacturing. (M-L)

– Multi-Model Federation – Develop techniques and standards that enable complex product andprocess models to be quickly assembled by integrating physical representation models (e.g., CADmodels) with material, process, quality certification, and other supporting models. (S-M)

– Automated Abstraction – Develop techniques for automated generation of specialized views ofmodels at desired levels of detail for different enterprise functions (e.g., technical review, costanalysis, project planning). Include the capability to expand, collapse, or de-feature the model toprovide the correct data and detail required for a particular application or use. (M)

– Graphical Representation – Develop representation techniques that enable users to call up differ-ent visual depictions of a product or process in order to convey, to different kinds of users, a com-plete understanding of the product/process and its attributes. (S)

– Multi-Sensory Representation – Develop interface methods and representation standards enablingmodels and simulation environments to incorporate tactile, sound, smell, and other useful sensoryattributes in order to provide a more complete representation of a product or process in the virtualrealm. (M-L)





– Model Versioning – Provide a system to document and manage configuration of product and proc-ess models as changes occur over time, including capture of the requirements or decisions that ledto each revision. (S)

• Goal 2: Plug & Play Collaborative Modeling & Simulation Environment – Provide a standards-based, easy-to-use collaboration environment that supports inexpensive integration of complex productmodels using components and designs from multiple supply chain members, where any model is inter-operable with any other model and with any standards-compliant simulation tool. (M)

– Product/Process Model Integration Standards – Develop standards for integrating material mod-els, manufacturing process models, business process models, and product models into a comprehen-sive collaborative enterprise modeling environment that is easy to use without extensive training.The approach should accommodate capture of product and process performance requirements; amethod to locate available models and supporting data; approaches for identifying and resolvinggaps and conflicts; and methods (e.g., federation techniques) to share information among the mod-els without compromising data integrity or information security. (S-M)

– Plug-and-Play Vendor Models – Develop standards and protocols that enable vendors to supplyplug-and-play product and process models for purchased parts, components, and equipment that canbe transparently integrated into larger product and process models in real time. (S-M)

– Collaborative Analysis Systems – Develop a framework for integrating current and future ana-lytical tools into engineering and business management workgroup applications to provide a col-laborative simulation environment with decision support tools for resolving technical and businesstradeoffs. (M)


Providing companies with the capability to automatically generate user-commanded views of product andprocess models and other model-based visualized information requires concurrence on a technical frame-work for integrating different types of models and simulations through a common user interface and fed-eration environment; close cooperation with the application vendor community; support of different engi-neering and business domains; strategies for management of security and proprietary data; and extendedcapabilities such as natural-language and voice-command interaction.

Significant advances in underlying model-based technologies are required to create this framework anddevelop supporting applications and capabilities. The following tasks are proposed to launch and guidethe required effort:

Task 1 – System Requirements & Architecture

Task 1.1 – Common Modeling Terminology & Practices: This task shall develop a standard set of in-dustry-wide terminology and practices for representation and capture of different model features and at-tributes, whereby a common and complete understanding is conveyed regardless of context, and like fea-tures can seamlessly transfer from one domain, model, or level of abstraction to another.

Task 1.2 – Technical Specs & Interim Plan: This task shall survey existing industry standards andmodels (commercially available and others) and develop technical specifications for the required life-cycle functions and interfaces, including interim measures to maximize early integration benefits acrossthe life-cycle of a chosen industry sector.

Task 1.3 – Product Model Framework: This task shall develop a robust, production-quality objectmodel framework as a complete source (or enabling resource) for all product information. Include tech-nologies and standards enabling creation of a complete, mathematically accurate product model that al-lows all enterprise systems and modeling tools (including material models, manufacturing process mod-els, and business process models) to interact with it through standard interfaces. The resulting models



must be able to accurately capture and communicate design intent – with complete traceability torequirements – physical and nonphysical characteristics, and functional performance, and include para-metric feature definition for design and manufacturing. Work within the chosen industry sector at first,but with consideration to broader applicability.

Task 1.4 – Product/Process Model Integration Standards: This task shall develop standards and tech-niques for integrating product models with material models, manufacturing process models, and businessprocess models into a comprehensive collaborative enterprise modeling and simulation environment. Theapproach should accommodate capture of product and process performance requirements; a method tolocate available models and supporting data; approaches for identifying and resolving gaps and conflicts;and methods to share information among the models without compromising data integrity or informationsecurity.

Task 1.5 – Hierarchical, Composable, Shareable Models: This task shall establish interface standardsthat support creation of complex models at successive levels of detail, enabling integration of individualproduct models into system-of-systems models that support deep understanding of interdependencies andinteractions. Initially focus on enabling integration within related product families for one selected manu-facturing sector.

Task 2 – User Interface

Task 2.1 – Human/Computer Interface: This task shall develop intuitive interface techniques (visuallyoriented when possible) that focus on usability of the tools and provide a learning environment that sup-ports both training and real-time task support.

Task 2.2 – Automated Abstraction: This task shall develop techniques for automated generation of spe-cialized "views" of models at desired levels of detail for different enterprise functions (e.g., technical re-view, cost analysis, project planning) for any product-related application or decision process. Include thecapability to expand, collapse, or de-feature the model to provide the correct data and detail required for aparticular application or use.

Task 2.3 – Natural Language Interaction: This task shall develop the ability of the product model tointerpret and communicate requirements and product features in natural-language terms to support humanquery of the system.

Task 2.4 – Multi-Sensory Representation: This task shall develop interface methods and representationstandards enabling models and simulation environments to incorporate tactile, sound, smell, and otheruseful sensory attributes of a product or process.

Task 3 – Model Functionality

Task 3.1 – Unit Process Models: This task shall develop models for manufacturing processes that in-clude representation of their capabilities, characteristics, and attributes; and enable interaction with prod-uct models to automatically generate process plans.

Task 3.2 – Plug & Play Vendor Models: This task shall develop standards and protocols that enablevendors to supply plug-and-play product and process models for purchased parts, components, andequipment, or plug-and-play business models or industrial design models, any of which can be transpar-ently integrated into larger models in real time. Extend current model-based applications to include thecapability to automatically generate the input required (e.g., mesh or flat file or model subset) for use byspecific analytical tools. Prioritize desired tool compatibility across industry sectors and work with soft-ware tool vendors to accomplish the needed extensions with real/near-real-time processing capability.

Task 3.3 – Process Performance Models: This task shall extend product and process models to includeunderstanding and representation of performance; e.g., the ability of a process to hold a specified toler-ance level over repeated runs, or a product’s ability to perform its function over time with normal and ab-normal modes of operation.



Task 3.4 – Self-Monitoring Product & Process Models: This task shall develop tools and methods thatenable product, process or business models to monitor the enterprise knowledge base and respond appro-priately (e.g., propagate a change or issue an alert) whenever the data underlying the model – or the re-quirements the model is intended to fulfill – change.

Task 3.5 – Self-Composing Models: This task shall develop the capability to create models that knowtheir own attributes and can interact with other model objects to complete a resulting superset of attrib-utes, relationships, and behaviors. Enable models to automatically search for, acquire, and integrate ex-isting information and "sub-models" needed to complete their intended design. Include the capability toautomatically populate manufacturing process simulations with equipment models, material models, etc.;and the capability for product models to automatically extend themselves with material models, part andcomponent models, and similar assets available from the model libraries accessible to the enterprise. In-clude the capability for models to mine for information about products, processes, and systems withwhich the product or process will interact in operational use.


4.1 BENEFITS TO DOD

The ability to fully understand and explore all aspects of a weapon system design, its production and sup-port, and its interactions with other products in the operational environment (as part of a system of sys-tems) will save DoD millions each year as a result of greatly improved abilities to optimize system designat each stage of development, prior to committing to physical prototyping and production.

The flexible modeling environment will be a major step forward in achieving the vision of simulation-based acquisition, shortening the development cycle for new weapons systems and ensuring that the sys-tems deployed are ready to perform flawlessly. The cost of not “modeling well” is well documented. Asone example, the Army spent $6.9 billion and 21 years of effort developing the Comanche armed recon-naissance helicopter to the point of production readiness, only to terminate the program due to design andmanufacturing problems that ultimately made it unaffordable in light of other DoD priorities. While muchof the technology developed for the Comanche will be exploited in other systems, the program remains aprime example of failure to model the impacts of requirements changes and design decisions.

On the positive side, programs such as Joint Strike Fighter (JSF) and Future Combat Systems (FCS) ismaking extensive use of model-based capabilities. On JSF, modeling and simulation are enabling the AirForce, Navy, and Marine Corps to concurrently develop three different variants of the same aircraft (Fig-ure 4-1), saving tens of billions of dollars over independent development and reducing the cost of pro-viding future variantsfor export.

On FCS, productmodels and simula-tions are being widelyshared across thecontractor teams asthe FCS vehicles andsensor systems pro-gress through devel-opment. However,there are limitationson the usefulness ofthe models because ofthe time and cost ofgenerating different Figure 4-1. Flexible modeling technologies will directly support DoD’s simulation-

based acquisition vision for the JSF program.



versions of different models to support different needs. Flexible representation technology could be in-valuable in helping the FCS integrators (Boeing/SAIC, Raytheon, Northrop Grumman) and the FCS vehi-cle developers simplify the demands of exploiting the terabytes of modeling and simulation data beinggenerated.

Flexible representation technologies will also significantly enhance the services’ simulation and trainingcapabilities. Rather than building or re-building separate models (e.g., to JSAF standards) for use in dif-ferent kinds of simulators, simulation environments, and wargaming systems such as those being devel-oped under WARSIM, ADST, and similar programs, simulators would simply “tap” a shared knowledge-base and upload the desired model with the specified attributes and fidelity. This capability would saveDoD millions in future development costs for common simulation environments such as OneSAF. Re-configurable simulators such as those being developed under the Marine Corps Aviation SimulationMaster Plan (MCASMP) program would be particularly well-suited to operate using a shared modelknowledgebase with flexible representation capability.


The benefits to industry outside the defense sector are huge, as the capabilities developed in this projectwill redefine the design and manufacturing landscape and deliver billions of dollars in savings and com-petitive impact. The ability to create comprehensive product definitions and flexible models that enableautomated generation of representations at user-selected levels of abstraction will radically reduce thetime and cost required to analyze and optimize the design of products and the processes for their subse-quent manufacture. The change from incremental, iterative product and process design to totally opti-mized first-product correct will deliver cost and performance savings on a scale of billions of dollars peryear. Other benefits include:

• Reduced time to market

• Enhanced innovation with lower risk

• Reduced design cycle time

• Evaluation and optimization of designs – and validation of performance and other attributes – be-fore committing resources to prototyping and production

• Lower cost for product development, manufacture, and life-cycle support

• Better products and better product performance.


The proposed project schedule is provided on the following page. The estimated cost for the approxi-mately 7-year effort is $33 million.


The risk for this project is assessed as moderate to high, due to the comprehensive scope and complexityof the information to be integrated into a rich, plug-and-play information representation and the reluc-tance of the vendor community to give up their proprietary advantages. Needed technologies for portionsof the project are not yet available outside of the research environment – e.g., in recognizing and creatingsuitable model representations for natural language or sensory interaction. Technology readiness is there-fore assessed at TRL 2-3.



Project Schedule for Flexible Representation of Complex Models



NGMTI PROJECT MBE-5

INTELLIGENT MODELS

1.0 PROJECT SUMMARY

The objective of this project is to develop enabling technologies and demonstrate the use of intelligentmodels that understand, seek out, acquire, and act on the information they need to execute their functions.The project will establish linkages between the physical modeling realm and the logical models that pro-vide intelligence to product, process, and enterprise models. The addition of intelligence in the modelingenvironment will radically improve the ability of manufacturers to design products and processes, config-ure and operate production facilities, and manage their business processes.

2.0 CHALLENGE

For maximum efficiency, manufacturing systems must operate at all times under optimum conditions andbe able to respond to off-normal circumstances. Physical models provide an awareness of expected re-sponse, but they lack the ability to interpret that response and to guide for correction and optimization.That is where intelligent models enter the manufacturing equation.

While a product or process model provides a mathematical and/or visual representation of a physical ob-ject or a system, an intelligent model acts on sensed or received information to execute a function – re-configuring itself to respond to change, or commanding an action to direct an associated task or process.Intelligent models play an essential role in the realization of the model-based manufacturing enterprises ofthe future. Some of these models will replace the static databases of today with repositories of deepknowledge, while others will possess the logic required to seek out and process the information they needto fulfill their functions. Thus, several categories of models will be needed. A number of varieties of in-telligent models are described in the NGMTI Roadmap for the Model-Based Enterprise. These includeexamples that are associated with the creation, testing, and optimization of product definitions, materialproperties, process knowledge and control, equipment characteristics and performance, and resourcemanagement.

Intelligent, “self-learning” models able to interface with all of the relevant enterprise functions andknowledge sources (Figure 2-1) present a significant challenge. A product model, for example, should beable to pull in the appropriate material properties data when the designer selects a material for a productcomponent, and automatically run mechanical, thermal, chemical, and other analyses in the context of thehigher-level product to verify that the material meets performance requirements at the lowest possiblecost. It must also be able to draw on the materials knowledge base to prompt the designer with alterna-tives consistent with the design requirements and priorities (e.g., lower cost, longer life). It must also beable to interrogate the production planning function and supply network to verify material availability andcost as well as compatibility with downstream manufacturing and product support functions.

Once an intelligent model has been created, it must be able to recognize when its underlying data changeand respond appropriately. This includes monitoring of input data sources and updating of changes, re-running analyses, and providing alerts to affected functions and users throughout the supply chain. Themodel must also be able to recognize conflicting, spurious, and inadequate inputs, identify and quantifyrisks, seek resolution, and capture all decision history in a complete audit trail.



Figure 2-1. Intelligent product and process models will interact through the enterprise knowledge base,enabling feedback across the product life cycle to ensure and enhance customer satisfaction.

Interaction between intelligent models, data sources, and humans must be seamless. Since all possiblepermutations of interaction may be impossible to define, the models must have the ability to recognizeand adapt to new interactions. Since some of the knowledge processed by such models will have legal,proprietary, or other sensitivities, the models must be capable of recognizing and responding to such con-flicts. This includes routine functions such as querying regulatory knowledge bases to extract safety re-quirements and issuing alerts to ensure the requirements are addressed.

The models must also provide the appropriate level of information, or views, for ease of human interac-tion. For example, views of a product definition for marketing staff will be very different from those ofthe manufacturing engineers. Also, as new interactions arise, the models must be capable of generatingthe new views with a minimum of manual intervention.

Intelligent models will be expensive to create, and cost effectiveness will be an issue. No single “silverbullet” can satisfy all the needs for intelligent models; however, key attributes are common. Capture ofthe data that models and simulations need to fulfill their functions must be automated and require littlehuman effort. Experience with knowledge-based and expert systems has revealed that knowledge capturerequires a great deal of human effort and is very time consuming. The cost of creating and populatingintelligent models using current techniques would be prohibitive; hence, new techniques must be found.For this reason, common frameworks, templates, and repositories of reusable models and knowledge willbe essential.


The goals defined in the NGMTI Roadmap for the Model-Based Enterprise related to intelligent model-based systems represent a far larger scope than is appropriate to address in this project. Many of the goalsfocus on providing the information that intelligent models need to execute intelligent functions, particu-larly with regard to manufacturing process control (Goals 2 and 3 below). Therefore, the project focuseson developing and demonstrating key capabilities in one representative industry sector in order to demon-strate the value and power of the technology. A project team will be assembled, requirements for intelli-gent model functionality defined, prototypes developed, and a series of demonstrations conducted.



3.1 GOALS AND REQUIREMENTS FOR INTELLIGENT MODELS

• Goal 1: Intelligent Models & Modeling Environments – Develop intelligent modeling capabilitiesto automate and accelerate labor-intensive modeling tasks and reduce the need for human interventionin modeling processes, enabling modeling and simulation functions to be automatically invoked at re-quired stages as a product or process evolves from conception to production. (M-L)9

– Common Modeling Semantics – Develop a standard, industry-wide terminology for representa-tion and capture of different model features and attributes, whereby a common and complete un-derstanding is conveyed regardless of context, and like features can seamlessly transfer from onedomain, model, or level of abstraction to another. (S)

– Automatic Model Conversion – Extend current Computer-Aided Design (CAD) applications toinclude the capability to automatically generate the input required (e.g., mesh or flat file or modelsubset) for specific analytical tools. Prioritize desired tool compatibility across industry sectorsand work with CAD vendors to accomplish the needed extensions with real/near-real-time proc-essing capability. (M)

– Adaptively Detailed Models – Provide models that can adaptively offer defeatured abstractions ormore detailed versions of their attributes to suit the requesting function, presenting a version readyfor use or analysis by that function. (M)

– Automated Requirements Linking – Develop methods and approaches enabling product andprocess models to automatically search for requirements that impact their domain or function (e.g.,safety, health, and other regulatory requirements) and interact with the model creator to ensurethese requirements are addressed as the mode is developed. (M)

– Self-Composing Models – Develop methods enabling models to automatically search for, acquire,and incorporate existing “sub-models” needed to initially complete their intended design. Includethe capability to automatically populate manufacturing process simulations with equipment models,material models, etc.; and the capability for product models to automatically extend themselveswith material models, part/component models, and similar assets available from model libraries.(M-L)

– Self-Monitoring Product & Process Models – Develop tools and methods that enable productand process models to monitor the enterprise knowledge base and respond appropriately (e.g.,propagate a change or issue an alert) whenever the data underlying the model – or the requirementsthe model is intended to fulfill – change. (M-L)

• Goal 2: Model-Based Intelligent Process Control – Develop intelligent, adaptive process controllersthat sense material and its geometric/chemical/physical properties in-process and dynamically adaptprocessing parameters (e.g., temperature, process speed, flow rates, equipment position) to assurecontinuous production of certifiably correct product. (L)

– Adaptive, Real-Time Process/Equipment Control Models – Develop self-tuning process andequipment control models based on first principles, validated material and process knowledgebases, and continuous feedback of sensor test and inspection data. Include models for legacyequipment as well as recently released equipment. (M)

– Rapid Material, Part, & Process Characterization – Develop characterization technology ena-bling fast, accurate in-process assessment of material/part condition and characteristics (shape,





composition, distribution, viscosity, temperature, etc.) and associated processes, to support model-based process monitoring and control. (L)

– Standardized Process Models – Develop robust, standardized definitions of processes such thatthe outcome of successfully performing the process is certifiably the same, independent of thetechnical platform (legacy or current equipment) that produced the outcome. (M-L)

– Sensors & Sensor Fusion for Process Monitoring – Develop non-intrusive sensors and sensor fu-sion technologies for current and legacy manufacturing equipment that enable the model-basedprocess controller to recognize and quickly adjust to any in-process variances (e.g., tool wear andmaterial variation), maintaining the quality of process output. Include the capability to evaluatesensor data to determine if readings from each sensor are credible based on inputs from other sen-sors, and to take appropriate action to maintain the health of the process if one or more sensors arenot functioning correctly. (M-L)

– Self-Diagnostic Equipment Maintenance & Performance Monitoring – Develop techniques formonitoring equipment status parameters against the validated equipment model, detecting andanalyzing trends toward out-of-spec performance, and automatically issuing commands/requestsfor needed maintenance. (M)

– Model-Based Failure Prediction & Recovery – Develop the technology and tools to accuratelymodel equipment and facility failure modes and effects, identifying predictive indicators for im-pending failures and process upsets. (M-L)

– Operational Feedback to Process Models – Develop the capability to monitor the shop floor andupdate process control models (for one or more unit operations, as appropriate) to adapt to changesin the processing environment to continuously ensure the correctness of the product being pro-duced. (M-L)

– Enterprise-Wide Control Capability – Provide the capability to support model-based control inmultiple-process, enterprise-wide (supply chain) applications. (L)

– Zero Finishing – Develop model-based process control schemes that eliminate the need for fin-ishing steps by dynamically managing product surfaces during operations such as forming, assem-bly, and blending. Include the capability to dynamically identify and recalculate mid-surface loca-tions for thin shells. (L)

• Goal 3: Self-Configuring Manufacturing Execution Models – Provide self-organizing manufactur-ing execution models able to integrate all applications, systems, equipment, and process instructions toensure readiness to satisfy all requirements for producing correct product, and which have the capa-bility to automatically adapt to changes in requirements. (L)

– Manufacturing Planning Model Templates – Develop a series of model-based templates, formajor classes of products in different sectors, that can integrate "sub-models" of processing equip-ment, unit processes, line operations, and material flows to create an end-to-end model of a givenmanufacturing process. (S)


– Equipment Characterization Models – Establish standards and requirements for integration ofperformance characterizations into existing or vendor-supplied models and simulations of processequipment (machine tools, valves, process sensors, material handling devices, etc.), including leg-acy equipment as well as current models. (M)



– Machine-Specific Equipment Models – Develop tools to extend generic or vendor-suppliedequipment performance models to reflect the as-installed configuration and use real-time sensor in-formation to accurately represent specific equipment system performance. Include the capability tocapture the baseline signatures for each production machine in its supporting model. (M-L)

– Intelligent Manufacturing Execution Models – Develop methods to automatically update manu-facturing execution models by recognizing and responding to approved changes in underlying ma-terial/process/product models, or in response to direction from the shop floor control system. (L)


Realization of the full potential of intelligent model technology will be an evolutionary process whereadvances in capability are demonstrated in a focused application then migrated to additional applicationswhere the data sources and communication/integration functionality can be defined, developed, and im-plemented. The proposed project is expected not only to provide working prototypes but also to identifya necessary minimum set of standards that must be defined to enable the development of much more so-phisticated future models. These initial deliverables will demonstrate tangible benefits in a selected rep-resentative manufacturing sector.

The project is comprised of four tasks as follows.

Task 1 – Project Planning: This task shall establish the project technical team, select the manufacturingsector to be addressed, define the specific functionality to be developed and demonstrated, and establishthe detailed project plan with task assignments.

Task 2 – Intelligent Model Requirements: This task shall define the specific functionality to be devel-oped for the selected manufacturing enterprise application. The team shall recruit external experts toserve as a standards body; define the full set of model-based tasks to be addressed; and specify the classesof intelligent models necessary for accomplishing the range of tasks. General functionality to be providedincludes:

• Auto-completion of product and process designs

• Automated analysis via interface to simulation tools

• Automatic query of resource availability and status

• Automated process monitoring to ensure certifiably correct product

• Autonomous monitoring and response to changes in source data such as configuration updates.

The documented requirements shall be circulated to team participants and interested government and in-dustry stakeholders for review, comment, and finalization prior to start of the development tasks.

Task 3 – Functionality Development: This task shall create, test, and refine prototype models and appli-cation functions providing the capabilities defined in Task 2 and addressing to the maximum extent pos-sible the desired capabilities specified in Section 3.1. Efforts associated with databases and knowledgebases that serve as sources of input to the prototype intelligent models shall be limited to integration ofavailable information sources (e.g., existing materials knowledge bases). Vendors of model-based tools(e.g., CAD, PDM, and ERM applications and analytical codes) shall be engaged to provide developmentsupport for their respective products. Results shall be demonstrated in both legacy shops and newer fac-tory installations.

Task 4 – Technology Demonstrations: This task shall demonstrate successful functionality of intelligentmodels in an industry setting. A project final report shall be produced that documents the capabilitiesdelivered and requirements for further development.




4.1 BENEFITS TO DOD

Defense acquisition programs will realize the same kind of benefits from intelligent models as describedbelow for commercial industry, although the payoff in terms of cost savings will be far more significantdue to the much higher complexity of military systems development. The ability of weapon system prod-uct models to automatically “self-complete” by drawing in supporting data; to automatically verify con-formance to detailed requirements; to autonomously re-run thermal, aero, producibility, reliability andother analyses when configurations or requirements change; and to automatically update design detail tothe lowest level of assembly to reflect the as-delivered configuration, will radically reduce design cycletimes and development costs. Automation of many routine engineering functions and avoidance of com-mon errors is expected to provide cost savings estimated at a minimum of 10% to 20% for engineeringlabor alone. The time and cost of preparing data packages for subcontracting and for major design re-views could be cut in half at a minimum, since these packages will be generated automatically by designsystems working in concert with intelligent product models. The time required to move from conceptualto detailed design could be reduced significantly, although the critical DoD development programs willcontinue to be driven by hardware build-and-test cycles.

Logistics support for weapon systems is also expected to benefit greatly from intelligent modeling capa-bilities. Intelligent life-cycle models will interoperate with intelligent product models to optimize the de-sign of the product and its support approach for all attributes of interest (reliability, level of repair, sparesrequirements, etc.) When a product upgrade is introduced, the intelligent model will automatically updateall linked and associated information, including training media, interactive electronic technical manuals(IETMs), and onboard prognostics systems for health monitoring.

Intelligent modeling technology developed under this project may also have application to advancedsimulation environments such as OneSAF to support warfighter training and mission rehearsal. Addinghigher levels of “intelligence” (and verisimilitude with real-world physics) to vehicle, platform, andweapon entities offers the potential to significantly increase the fidelity of entity behaviors. This wouldprovide a more realistic simulation experience. It would support evolutions of very high-fidelity engage-ment scenarios to aid in one-on-one, one-on-many, and many-on-many gaming and mission rehearsal.


Benefits derived from successful development of intelligent models will fall into two classes. The mostobvious benefit will be the ready availability of smart modeling and simulation applications that:

1. Reduce design cycle times by 50% or better compared to current tools

2. Eliminate design iterations by delivering optimal solutions on the first pass

3. Automatically generate plans and machine instructions for driving manufacturing execution.

Probably the greatest contribution of intelligent models toward realizing the vision of the model-basedenterprise, however, will come from the crosscutting, or supportive, models that enable the success ofnearly all other components of the enterprise’s business infrastructure.

Intelligent models will enable automatic, directed analysis of many more potential solution paths, whichwill produce more optimized solutions to technical and business problems. The ability to add intelligentsensors and process control capability to the invested capital base of legacy equipment will save billionsof dollars while preserving business competitiveness of the affected companies. Companies in the proc-ess industries are already realizing benefits from similar technology. BP Amoco, for example, imple-mented a model-based control system for crude oil processing that saves more than $500,000 a year andgreatly reduces waste streams associated with changes in the composition of incoming crude oil.




The proposed project schedule and estimated resource requirements are provided below. The estimatedcost for the 36-month effort is $5.6 million.


This project is high-risk. The creation of intelligent models that are sufficiently generic to serve manyfunctions is a difficult challenge at best. Providing models with the self-learning capability such that theycan capture knowledge without human intervention, and verify the accuracy of their inputs and outputs, iscertainly a “stretch” endeavor. Finally, the need for the models to interoperate with their networked ex-ternal components (e.g., knowledge bases, other models, simulation codes) in plug-and-play fashion isalso very difficult considering the variety and complexity of functions that must be served. Overall, thestate of the present technology is assessed at TRL 2.



NGMTI PROJECT MBE-6

Configuration Management forthe Model-Based Enterprise

1.0 PROJECT SUMMARY

The objective of this project is to develop an integrated system that ensures association and traceability ofthe right information with any product or process throughout its life cycle. To do this, the project willdevelop requirements and an integration strategy for managing complex configuration entities within themanufacturing enterprise, to the lowest level of its supply chains, and across the full life-cycle of theproducts it manufactures.

2.0 CHALLENGE

As manufactured products continue to increase in technological complexity and manufacturers rely moreon their supply chain partners to design, build, and support new products, the importance of managingconfiguration data well will only increase. Configuration management must be part of a seamlessly inter-connected technical and business infrastructure that supports and actively facilitates all activities acrossthe product life cycle.

Configuration management includes all activities associated with managing all data, information, andknowledge related to all the life-cycle processes of the product (from initial requirements through design,manufacturing, and support to final disposition at the end of the product’s life). It ensures traceability ofthe product throughout its life, and specifically addresses the assurance of providing current/approveddata to support each process in the life cycle. The nature of the product largely determines the level ofdetail retained.10 Configuration management requirements also vary according to whether the productoperates in isolation or is part of a larger system of products.

Configuration management of processes includes documented definition and certification of manufactur-ing processes against standard process definitions; documentation of process parameters and in-processproduct against defined requirements and tolerances; documentation of performance on products (as-builtconfiguration and process data to document the product’s pedigree); and maintenance of process equip-ment and systems themselves. It also includes maintaining an accurate baseline of maintenance, repair,training, and other processes and infrastructure that support a product throughout its life, including tools,consumables, and spares.

Management of configuration data is important in a broader business context as well. The technologyinfrastructure of an enterprise is an exceedingly complex array of facilities, systems, equipment, softwareapplications, and information assets in many forms. Careful coordination of new technology implemen-tations is required to avoid operational disruption. The information and knowledge assets themselvesmust be managed to assure protection and appropriate accessibility to different users and functions.Technology refresh initiatives must be well designed to ensure continued access to data that is trapped inlegacy systems.

Perhaps less obvious – but also important – is documentation and management of the business processesin effect for different states of the product life-cycle. These may range from labor rules and shift sched-ules to accounting practices and government regulations that are applied in different ways on different

10 For weapon systems, for example, configuration data is maintained for every delivered unit; for commercial products, configuration data is

typically maintained only for makes, models, and lots or batches.



programs or in different operating units of the enterprise. It is often important to understand how thebusiness environment was “configured” at the time a product was designed and produced.

Although the basic principles of configuration management are simple, managing and controlling con-figuration data is a challenge even in the best-run companies. Even with good integration of the productdesign, manufacturing, and support domains, a “final” production configuration usually undergoes nu-merous changes after the first unit comes off the production line. This greatly complicates product sup-port functions, since training, maintenance, repair, and logistics processes must support each productionvariation. As-built configurations are often inadequately documented and may diverge significantly fromthe official design drawings. Product definition changes over time may even cause loss of compliancewith an original requirement – a potentially unpleasant surprise for the customer in the operating envi-ronment. If the original design definitions are primarily captured on paper or in legacy systems, mainte-nance and support presents a huge challenge. This situation also severely hampers the process of captur-ing life-cycle support insights that can be passed back to the design and production functions for productand process improvement.

In the NGMTI vision, model-based tools will automatically capture product and process information intoan enterprise-wide configuration management system. For every baselined configuration of the productand its associated processes, the system will provide the associated digital records to support life-cyclemanagement of that product, and feed back lessons learned to continuously improve product and processcapabilities. Configuration management processes will run in the background, capturing in real time allinformation needed to document the design for engineering, quality, security, and other purposes – cap-turing the total genealogy of the product across its life (Figure 2-1). Data management protocols will bedeveloped to verify the integrity and usability of all information captured by the system, with automaticcapture of the audit trail for configuration-controlled data assets. Finally, lifetime management mecha-nisms will be put into place to transfer data as media and applications evolve. These tools will ensure thatno useful information is ever lost, corrupted, or trapped in a legacy system, and that information which isno longer needed is phased out of the system.

There is no commercially available tool that approaches the configuration management capabilities re-quired to support the NGMTI vision of the model-based enterprise. However, there are some productdata management (PDM) and product life-cycle management (PLM) tools that provide part of the neededcapabilities. The many PDM systems commercially available today generally support configuration man-agement as part of managing information about a product as it moves through engineering and manufac-

Figure 2-1. Model-based configuration management processes will capture all information neededto document the total genealogy of the product across its life.



turing. This generally includes functions such as management of engineering drawings, processing ofchange notices, and version control. Configuration management across the product life-cycle is alreadysupported by some (largely CAD-based) PLM tools such as IBM/Dassault’s CATIA-based Enovia sys-tem, enterprise resource management systems such as SAP, and PTC’s Windchill family of products.However, the cost and complexity of PLM implementation are major barriers to entry for small manu-facturers despite increasing emphasis on open, web-based standards.


Providing a comprehensive configuration management solution for the model-based manufacturing enter-prises of the future will be a huge undertaking. Thus, this project is designed as a series of phased devel-opments and demonstrations aimed at delivering key enabling capabilities to validate the technical ap-proach and stimulate a coalescence of vendor and user community focus. In Section 3.1 below, Goal 1(Model-Based Configuration Management) is directly addressed by this project. Other preliminary foun-dation tasks are also defined in Section 3.2.

It is important to note that the objective of this project is not to develop new PDM/PLM tools that com-pete with current applications, but rather to specify required model-based enterprise functionality andwork with the vendor community to develop and demonstrate the required capabilities. Open architec-ture, standards-based vendor components will be used wherever possible as part of the solution, but it isexpected that much additional development will be required. The overall technical approach will supporta “system of systems,” because of the risk of a single system and because companies will need to gradu-ally migrate their different configuration management systems to support a common capability acrosstheir supply chains.

The configuration management capabilities delivered will automatically capture product information intothe standards-based framework of an enterprise-wide, model-based configuration management system.For every baselined configuration of the product, the system will provide the associated digital records tosupport life-cycle management of that product, plus feedback of lessons learned to improve product andprocess capabilities. Data associated with each unit (or model or batch, as appropriate) will validate thatproduct configuration’s compliance to requirements and will be automatically provided to whateverdownstream functions require it. This will include provision of shared product support databases that up-date as-maintained product configuration data as well as information on spares and other support assets.

The model-based configuration management system will deliver on demand the data and informationneeded to optimize products, processes, and operations for life-cycle performance, cost-effectiveness, ef-ficiency, and certainty in every task. With this system and the required technical infrastructure in place,support personnel will be able to call up the specific history of any product or component to view the as-produced design and identify who created it and how (down to the specific manufacturing equipmentused), the receiving inspection logs of the raw material lots, and the process history logs of the personnelwho created it. This system will also eliminate the need to re-create and re-enter (or worse, infer) infor-mation at multiple points in the life cycle, thus eliminating a significant source of error and associatedimpacts on cost, safety, and other critical factors.

The configuration management system will provide the basis for true product intelligence, interfacingwith simulation tools and knowledge bases to support configuration change decisions in every stage ofdevelopment. This includes support for new technology insertions as well as design changes to addressperformance and support issues; risk assessment and mitigation; and rapid quantification of impacts tocost, schedule, and other requirements.

3.1 GOALS AND REQUIREMENTS FOR MODEL-BASED CONFIGURATION MANAGEMENT

Enterprise-wide configuration management is a key theme of the NGMTI vision. Both the Product Reali-zation and Resource Management sections of the NGMTI Roadmap for the Model-Based Enterprise de-fine multiple needs in this area. Specific goals and supporting requirements are outlined below. Other



goals and requirements supporting configuration management are outlined in the MBE Roadmap (andother NGMTI white papers including Flexible Representation of Complex Models and Model-BasedProduct Life-Cycle Management.

• Goal 1: Model-Based Configuration Management – Provide model-based configuration manage-ment capabilities supporting product, process, and systems evolution across the entire life cycle. In-clude the capability to manage and associate all data, information, and knowledge related to life-cycleprocesses and deliver appropriate views of the information to all enterprise functions that need it.(M-L)11

– Model-Based Configuration Management Frameworks – For common types of products, process,and systems, develop generic life-cycle frameworks that support automated prompting, generation,and distribution of various models at the appropriate point in the development process. Include thecapability to alert responsible functions/personnel when a particular model requires creation, andprovide an initial model shell and requirements definition that gives users the most complete startingpoint for the required work. (M)

– Automated Change Management – Develop a process and notification/authorization scheme forreviewing, approving, documenting, communicating, and archiving changes in configuration-controlled models to all affected functions, processes, systems, and individuals, including customersand lower-tier suppliers. (M)

– Automated Change Propagation – Develop the capability to automatically ripple the effects of anyone change to a product or process model to all other product and process models (e.g., tooling,training, maintenance documentation) that are affected by the change. Include the capability toautomatically update associated analyses, cost estimates, bills of material, purchase orders, and otherdependent information and assets and feed the changes to the change management system for dis-semination. (M-L)

– Enduring Data Storage – Develop the capability to preserve the accessibility and integrity of ar-chived electronic definition records and associated data throughout and beyond the life of the prod-uct, process, or facility. Include the capability to automatically verify the accuracy of retrieved flatfiles and models generated in applications or versions no longer in use; and establish industry stan-dards for ensuring backward compatibility of product definition applications. (M)

– Remote Product Upgrades: Provide mechanisms for some classes of product to be produced withunexpressed features (potential future product upgrades) that can then be activated either remotely orwith minimal service when the new features are ready for implementation. Include the capability forproduct models to manage changes to products in the field, and to accurately reflect the impact of thechanges in the life-cycle cost baseline. (L)

• Goal 2: Multi-Enterprise Model-Based Business Systems Integration – Provide mechanisms andmethods for rapidly interconnecting the systems of different enterprise partners to integrate PDM,ERP/ERM, and other business management system functionality to the lowest tier of the supply chain.(M-L)

– PDM/ERP/ERM Interface Frameworks – Develop interface frameworks and standards for quicklyand seamlessly integrating disparate product data management and enterprise resource managementsystems across different companies. Include the capability for enterprise systems to automaticallynegotiate full or limited interfaces depending on the capabilities of the systems being interfaced andthe permissions defined for specified business relationships. (M-L)





– Linkages to External Resource Sources – Develop tools for linking enterprise business manage-ment systems to external data sources to enable continuous update of resource information to supportplanning and decision processes. (S)

– Distributed Status Tracking – Develop model-based tools for continuously tracking the status ofresources and activities throughout the supply chain, enabling real-time updating of operational plansbased on internal and external constraints. (M)

• Goal 3: Life-Cycle Model Feedback to Product & Process Design & Planning – Provide the abilityto acquire and use captured information from users and maintenance/repair and final disposition op-erations to: 1) enrich the fidelity and depth of product life-cycle models, and 2) feed back and enhancethe process and product design function. (M-L)

– Robust Requirements Modeling Tools – Develop modeling tools that integrate the entire chain of aproduct’s life-cycle events, including environmental, safety, health, and other regulatory require-ments. (M)

– Integrated Life-Cycle Support Modules – Develop plug-and-play modules for current CAD andPDM systems that enable accurate modeling of all product design factors relevant to product support,including reliability, availability, maintainability, and supportability, to optimize product designs forperformance, cost-effectiveness, and customer value. (M-L)

– Life-Cycle Performance Feedback Tools – Develop tools and methods to automatically capturelife-cycle performance data (e.g., actual reliability and repair turnaround times) from the enterprise’sproduct support systems and update the master product knowledge base. (S-M)

– Technology Impact Forecasting – Develop the means to link knowledge and projections about fu-ture technology progressions (e.g., faster processors, new materials) to optimize a product design forits intended useful life, including technology refresh or product phaseout. (M)


This project will develop and demonstrate an integrated configuration management capability that sup-ports all activities across the product life cycle, focusing on design, manufacturing, and product support.Specific tasks to be accomplished are as follows:

Task 1 – Configuration Management System Requirements & Architecture: This task shall developbasic system requirements and a systems engineering approach for model-based configuration manage-ment, including the following:

1. Standard Data/Knowledge Representation & Management: Develop standard, compatibleapproaches for capture, use, and configuration management of model-based data and knowledgeto eliminate errors and significantly reduce associated costs.12 Ensure compatibility with ISO10303 AP 239.

2. Model Management Lexicon: For selected industry sectors, establish a common nomenclaturefor storage and retrieval of model-based information, enabling users or applications to quickly ac-cess needed models and supporting information/data for the product in question.

3. Life-Cycle Configuration Management Requirements Definition: Define explicit configura-tion management requirements for each function in the product life cycle – analysis, design, pro-curement, manufacturing, inspection, maintenance, refurbishment, technology refresh, retirement,disposal/recycle, etc.).

12 This task should be worked in collaboration with the NGMTI project for Flexible Representation of Complex Models.



4. Model-Based Configuration Management Frameworks: For a selected set of types of prod-ucts, process, and systems, develop generic life-cycle frameworks that support automatedprompting, generation, and distribution of various models at the appropriate point in the life-cycleprocess. Include the capability to alert responsible functions/personnel when a particular modelrequires creation, and to provide an initial model shell and requirements definition that give usersthe most complete starting point for the required work.

Task 2 – Configuration Management System Components: This task shall develop and integrate thefollowing core capabilities for model-based configuration management. Leading vendors will be engagedin producing an interoperable set of capabilities and maintaining compatibility to satisfy government andindustry requirements for configuration management.

1. Extraction of Product Configuration Data: Develop the capability to automatically generate atotally complete bill of material for a product and all its associated manufacturing and supportprocesses directly from the associated product and process models.

2. Automated Model Information Delivery: Provide automated delivery of, and controlled accessto, correct models and data for each life-cycle function at the correct points in the life-cycle.13

3. Automated Change Management: Develop a process and notification/authorization scheme forreviewing, approving, documenting, communicating, and archiving changes in configuration-controlled models to all affected functions, processes, systems, and individuals, including cus-tomers and lower-tier suppliers.

4. Automated Change Propagation: Develop the capability to automatically ripple the effects ofany one change to a product or process model to all other product and process models (e.g., tool-ing, training, maintenance documentation) that are affected by the change. Include the capabilityto automatically update associated analyses, cost estimates, bills of material, purchase orders, andother dependent information and assets and feed the changes to the change management systemcomponents for dissemination across the value chain.

Task 3 – Associated Systems: This task shall develop and integrate functional capabilities required tofully implement operational model-based configuration management capabilities, including:

1. Model-Based Product Requirements Management Environment: Develop a model-based re-quirements management environment enabling demonstration for selected products that the mas-ter product model satisfies all functional requirements for model-based configuration manage-ment.

2. Data Management & Auditing System: Develop a data management and auditing system thatis compatible with the configuration management system and ensures continuous integrity andaccessibility of all configuration data assets. Include metadata tagging capability that enablescaptured information to be migrated into new systems for future use.

3. Real-Time Product Support Linkage: Develop methods and protocols to link product data andrepresentations contained in maintenance and training media directly to the configuration-controlled master product model and support real-time interaction, including between live andvirtual training activities. Include techniques and procedures for automatically updating supportmaterials when a product configuration or procedure change is authorized, and alerting userswhen such change occurs so that they can receive needed updates.

13 This task shall be worked in collaboration with the NGMTI project for Information Delivery to Point of Use.




4.1 BENEFITS TO DOD

Disciplined configuration management is critical for DoD products and processes, but the military serv-ices today depend on the contractor community to maintain configuration data for the weapons, vehicles,sensors, and other equipment in inventory. Every contractor has their own processes for configurationmanagement, and although the industry adheres to common practices, configuration management rela-tionships with DoD customers are unique for every program and product. By virtue of its compliancewith ISO 10303 AP 239, this project will support creation of neutral data sets compatible with EIA-836,the configuration management data exchange and interoperability standard that grew out of MIL-STD-2549.

While this project will not implement a universal configuration management system, it will begin inter-connecting manufacturing enterprise processes and systems around a model-based configuration man-agement backbone that ties together, and makes available, the universe of data associated with everyproduct and process. Depot organizations, for example, will not only be able to call up product drawingsand repair history – they will be able to pull up the unique manufacturing record for a unit, including themachines and tools used to build it, the original process parameters, and the actual tolerances obtained onthat machine as a specific part was machined, formed, or assembled. Training units will no longer haveto wait for updated manuals and procedures when a configuration changes; these will be transmittedautomatically to all affected electronic training media, with alerts highlighting changes and configurationrepresentations automatically updating to show the most current baseline. This will support the continuedevolution of high-fidelity immersive simulation environments for training of maintainers, pilots, drivers,weapon operators, and other users.

Configuration updates and underlying models will also be uploaded automatically to onboard systems,ensuring that not only is the user maintainer alerted to a change, but so is the platform itself. This willsupport the emergence of smart weapon systems that are able to self-configure for specific missions basedon their on-board knowledge of the capabilities and limitations of their specific hardware/software con-figuration.

The capabilities delivered by this project will also enable capture of a wealth of operational performanceinformation that can be fed back to the product support team and the design team to aid in solving prob-lems and designing product upgrades. This is expected to greatly reduce time, cost, and uncertainty inmanaging block and spiral upgrades, and allow DoD to implement shorter technology refresh cycles thatkeep its front-line weapon systems much closer to the cutting edge of capability.

4.2 BENEFITS TO INDUSTRY

Model-based configuration management will deliver significant benefits to industry, tying together appli-cations and processes that today provide limited model-based capabilities and radically reducing the time,complexity, and cost of operating and managing interrelated processes that are not interconnected. Manycompanies and application vendors claim to have solved the configuration management challenge withPDM, ERM, and related tools, but present functionality is extremely application-dependent and organiza-tionally unique. The project will comply with ISO 10303 AP 239, and thus ensure compatibility throughcommon data definitions and provision of feedback on as-maintained configuration, usage, properties,operating state, and behavior. The capabilities delivered by this project will enable a more responsiveenterprise environment that enables product and process integration across all functions of the enterpriseand its supply chains and stakeholders. Engineers, for example, will be able to link process models toproduct models in minutes rather than days, and have all the associated data and knowledge needed tomake the best design decisions.

Model-based configuration management will also provide powerful capabilities for long-term traceabilityof product data, which is essential to improving life-cycle support for military systems and products or



facilities subject to close regulatory oversight. The ability to quickly access the total audit trail of anyproduct, including the materials and the processes used to produce it, will reduce the time and cost oftroubleshooting by orders of magnitude. The ability to link the product definition to its real-world experi-ence, and feed that experience back to the product and process designers, will reduce the time and cost ofresolving performance issues and support quantum leaps – rather than incremental advances – in devel-oping upgrades, enhancements, and next generations of products.

5.0 PROJECT PLAN AND RESOURCE REQUIREMENTS

The proposed project schedule is provided below. The estimated cost for the 5-year effort is $23 million.


Risk for this project is assessed as low, since the scope of the tasks is bounded to an achievable set of de-liverables and the technologies required primarily involve application-focused development leveraging alarge base of existing tools and standards. Modification of existing commercial tools is the main area ofrisk, since the vendors must be willing to support the required extensions and new capabilities on thesame timeline. Such agreement may be difficult to achieve. The potential benefit in terms of future com-petitive advantage, however, is seen as significantly large enough to provide strong motivation from thelarge field of vendors currently active in the PDM/PLM markets.

Technology readiness for this area is assessed at TRL 4-5, since the basic technologies exist but requireextension and a significant degree of integration to provide the required model-based functionality.



NGMTI PROJECT MBE-3

System-of-Systems Modeling forthe Model-Based Enterprise

1.0 PROJECT SUMMARY

The objective of this project is to develop and demonstrate capabilities, approaches, and tools for inte-grated multi-level, multi-system modeling of products, processes, and life-cycle functions for a represen-tative set of products in a selected manufacturing industry sector. The project will demonstrate the abilityof system-of-systems modeling techniques to reduce product development time and cost, eliminate cur-rent needs for manual integration among and across enterprise processes, and deliver product/process de-signs that are optimized for performance across the product life cycle.

2.0 CHALLENGE

“System of systems” is a concept that evolved over the past decade in the defense community with therecognition that we can no longer afford to design and support complex weapon systems as standaloneproducts. In the military environment, individual weapons must work together as an integrated system in-theater and on the battlefield to accomplish individual and collective objectives. This concept is evenmore important for the organizations that support these products – supplying, maintaining, and servicinghem; providing training; troubleshooting problems; and coordinating the often conflicting requirements ofdifferent stakeholder organizations. In the non-defense world, the same approach is needed in environ-ments such as medical care, where a vast array of products and technologies must all work togetherseamlessly as a single complex system.

The same principles apply in manufacturing enterprises. Design systems, planning systems, control sys-tems, business management systems, communication systems and myriad of other systems have evolvedto automate and improve the performance and capabilities of various enterprise functions. On the whole,unless a company has bought into a full line of compatible applications from a single vendor (typically atgreat expense and requiring further large investments in integration and “business process reengineer-ing”), these systems do not work together well, if at all. Models created in different kinds of applications,or using different brands of the same kind of application (e.g., two different CAD tools), in general do notintegrate easily, if at all. A model-based approach to integrating processes and systems, with the ability tocreate composable and self-integrating models14, is key to overcoming this challenge.

The system-of-systems concept has many origins. In the realm of product design and manufacturing,“systems engineering” arose in the 1970s as companies realized that simply designing and developing thecomponents of a product did not necessarily deliver products that worked well when all the parts wereassembled. Systems engineering principles were created and applied to enable organizations to managedevelopment from a system-level perspective, ensuring that each element of the product was not only op-timized to perform its own specific function, but that it meshed smoothly with all of the other parts, com-ponents, and subsystems with which it interacted.

The system-of-systems concept emerged in the 1990s as Congress and DoD realized that it had many newweapon systems in the development pipeline, many of which were designed for redundant or overlappingmissions (e.g., killing Soviet main battle tanks – an overriding priority of the Cold War that largely

14 This topic and related requirements are covered in further detail in the NGMTI Roadmap for the Model-Based Enterprise (Section 2, Product

Realization & Support) and in the NGMTI white paper on Intelligent Models.



evaporated with the collapse of the USSR threat to Western Europe). Changing missions and global pri-orities dictated that DoD develop lightweight, highly flexible and lethal, rapidly deployable, and readilysupportable forces that enabled the services to accomplish their missions affordably, with great precision,and at minimum cost.

Today, virtually all weapon system development is done in a system-of-systems context that is best ex-emplified by the Army’s Future Combat Systems (FCS) program. FCS is developing a family of weaponsystems (Figure 2-1) with unique and complementary functions that are all tied together with networks ofsensors and tightly integrated command and control functions. The joint services’ F-35 Joint StrikeFighter embodies the system-of-systems concept on the discrete weapon system level, with one airframetailored into three different versions and sharing a common logistics chain to meet the unique missionrequirements of the Air Force, Navy, and Marine Corps. The system-of-systems principle is thus savingbillions in acquisition costs, and is expected to deliver even greater savings in operations and maintenancecosts while radically improving the combat effectiveness of our warfighters.

Figure 2-1. The FCS program is developing a tightly integrated system of systems that will enable Army war-fighters to “see first, understand first, act first, and finish decisively.” Extensive modeling and simulation of FCSground and air vehicles, weapons, sensors, logistics support, and battlefield command and control function inde-

pendently and collectively is enabling the U.S. Army to save billions in development costs.

Much work must be accomplished to turn “system of systems” from a principle into tools and applica-tions for the future manufacturing enterprise. Modeling and simulation is a critical enabler of this trans-formation. Currently there is no common modeling framework to support concurrent evaluation, optimi-zation, and management of life-cycle requirements for multiple complex products that share a commonoperational environment, nor is there a framework for integrating different model-based systems in a uni-fied enterprise environment. DoD has made excellent progress in the former area with programs such asADST II, WARSIM, and JSIMS, which are developing and managing simulation and modeling capabili-ties that support analysis and training in realistic virtual environments that effectively integrate multiple“players” and multiple systems.

The NGMTI vision for the model-based enterprise requires similar capabilities, but goes beyond simula-tion of entities and interactions to provide real-time integration of “live” assets. Product and processmodels will link to and maintain connectivity to the knowledge bases that define their attributes, includ-ing material models, equipment models, production capacity models, skill models, source requirements,and supplier resources. The following scenario illustrates some of these capabilities:

A designer specs a screw to fasten a cover plate for a custom electronics box. As he moves on tothe next feature, the design application autonomously reviews the requirements associated withthe cover plate and discovers that it must be opened periodically for maintenance, and that thecustomer wishes this function to be performed in the field by any user, without any tools. The de-sign system automatically re-specs the screw as a quick-release fastener and alerts the designerto the change.



When the designer OKs the mod, the design system calls on the enterprise’s product archive andpulls up previous designs for such fasteners, and interrogates the manufacturing facility model toascertain if ready production capability is available. At the same time it queries the suppliernetwork to obtain capability, availability, and pricing on a suitable part, verifying ability to pro-vide the required size, configuration, and ruggedness. The design system selects the optimumpart based on cost and performance, downloads the fastener model from the preferred supplier,and integrates it into the cover plate design. It then launches multiple simultaneous backgroundsimulations to evaluate performance. These simulations verify that: 1) the fastener can be read-ily accessed by a soldier wearing full NBC gear in arctic conditions; 2) that it will remain se-curely fastened when subjected to the specified vibration, shock, and handling environments; and3) that it is within the weight allowance for the electronics box. With designer approval, thesystem adds the part to the bill of material and forwards a ready-to-place procurement spec tothe purchasing function. The simulation results are archived and a note is automatically addedto the maintenance documentation that the fastener requires lubrication check as a part of theregular maintenance schedule for the electronics unit.

This step has taken 15 minutes with no cost for engineering or administrative labor.

As indicated in the above example, product and process models will seamlessly integrate in the enter-prise’s system-of-systems environment – including partners in the supply chain, and with appropriate se-curity for data and communications – to evaluate the impacts of technical and business decisions in de-sign, development, manufacturing, and operation. This will provide significant life-cycle cost savings forproduct support through streamlined maintenance and closely coordinated technology management.Product and process models will also exchange information with “gatekeeper” models supporting the en-terprise’s resource management and strategic management functions. This will allow users to evaluatethe broader impact of decisions beyond the product or process at hand.

The supporting systems architecture will also provide a means of robust operation when a subsystem isnot available, using a managed substitution “shell” for missing functions or data. The missing systemmay be out of commission, not yet fully developed (i.e., full functional definition is not yet known indetail), or intentionally withheld. The overall system will compensate for the missing subsystem, withresulting diminished function or uncertainty being noted in downstream results. This concept is similar tothe current practice of using simulators and emulators to conduct system-level testing when a specificsubsystem is not yet available.


This project will develop and demonstrate capabilities, approaches, and tools for integrated multi-level,multi-system modeling of products, processes, and life-cycle functions for a representative set of productsin a selected manufacturing industry sector. It will demonstrate the ability of system-of-systems model-ing techniques to reduce product development time and cost, eliminate current needs for manual integra-tion among and across enterprise processes, and deliver product/process designs that are optimized forperformance across the product life cycle. A key capability to be demonstrated in this project is that ofcomposable models – complex models that can be quickly assembled from component/constituent modelscreated by different applications, without the need for manual translation or repair.

3.1 GOALS AND REQUIREMENTS FOR SYSTEM-OF-SYSTEMS MODELING FOR THE MODEL-BASED

ENTERPRISE

The vision of the future of manufacturing as defined in the Roadmap for the Model-Based Enterprise isbuilt around the concept that models will drive and control all enterprise processes, and that the models ofthe enterprise’s processes and systems will be able to interact in a highly autonomic fashion. The systemsthat rely on other systems will be able to feed information requests and receive not merely requested data,



but the right knowledge along with the context needed to apply those resources to model products andsimulate processes in an integrated fashion.

The core goal and supporting requirements to enable this capability are outlined below. Other goals andrequirements supporting this capability are outlined in other NGMTI model-based enterprise white pa-pers, including Intelligent Models, Product-Driven Product & Process Design, and Flexible Representa-tion of Complex Models.

• Goal 1: System-of-Systems Modeling Capability – Provide integrated system-of-systems modelingcapabilities to guide and improve product and process design decision-making for different producttypes and manufacturing sectors. (M-L)15

– System-Level Product Modeling – Develop system-level modeling capabilities for classes ofproducts and processes, whereby a comprehensive decomposable product model captures or linksto all types and levels of information needed to support all aspects of development. (M)

– Intelligent, Hierarchical, Composable, Shareable Models – Establish interface standards thatintelligently support creation of complex models at successive levels of detail, enabling integrationof individual product models into system-of-systems models that support deep understanding ofinterdependencies and interactions. Initially focus on enabling integration within related productfamilies for one selected manufacturing sector. (M)

– Scaleable System-of-Systems Simulation Architecture – Leverage emerging scaleable architec-tures to provide a system-of-systems modeling and simulation environment supporting thousandsof component elements and dozens of modeling tools and analytical applications. Develop stan-dards for supporting scalable architectures and for interfacing architectural components and tools.Evaluate existing standards efforts in exploring approaches to this requirement. (M-L)

– Secure System-of-Systems Data & Risk Management – Provide capabilities for compartmentali-zation, security, and long-term management of data and estimated risk to support system-of-systems modeling that integrates information from multiple sources having different security con-straints, levels of functional detail available, and levels of risk and uncertainty. (S)

– Self-Completing Models – Develop the capability to create models that know their own attributesand can interact with other model objects to complete a resulting superset of attributes, relation-ships, and behaviors. (L)


This project will develop and demonstrate system-of-systems modeling capabilities to guide product andprocess design decision-making, development, and life-cycle support for different product types andmanufacturing sectors. Information security issues shall also be addressed.

Specific tasks to be accomplished are as follows:

Task 1 – Product/Process System-of-Systems Modeling Architecture – This task shall develop ascaleable architecture for a system-of-systems product/system/business modeling and simulation envi-ronment supporting hundreds to thousands of component elements and dozens of modeling tools andanalytical applications. The project team shall evaluate existing standards efforts16 in exploring ap-proaches to this requirement. The architecture shall include a logical information model that defines themodel objects (entities) supported for each product type and process type; a functionality definition, with


M (Medium) = 3 to 5 years, and L (Long) = 5 to 10 years.16 Standards to be evaluated include the Object Management Group’s Model Driven Architecture, the ISO Reference Model for Open Distributed

Processing, the DoD High Level Architecture, and the ISA 95 Enterprise – Control System Integration standards being developed by the In-strumentation, Systems and Automation Society..



inputs and outputs, for each entity; and an application map that associates required functions with discretemodeling/simulation tool definitions.

This task shall also survey and assess, at a high level, the ability of current modeling, simulation, and in-formation management tools to fulfill the needed application functionality, and perform a gap analysis toidentify desired enhancements to current tools as well as functions for which new tools must be devel-oped.

Task 2 – System-Level Product & Process Modeling – This task shall develop system-level modelingcapabilities for a representative set of products and processes, whereby the comprehensive product modelis able to capture or link to all information needed to support its associated technical or business process(e.g., process models, analytical codes, cost estimating applications, and scheduling functions). Themodel shall automatically display customized domain views (e.g., electrical design, mechanical design,thermodynamic design, product assembly, bill of material, make/buy) on command; simultaneously loador call up associated domain applications for launch by a user; and query associated knowledge bases anddatabases to update and ensure currency of the model’s data basis.

This task shall include a system-of-systems demonstration that validates the integrated, faithful interop-eration of the selected product and process models at varying levels of fidelity.

Task 3 – Composable, Shareable Models – This task shall develop product and process model struc-turing approaches and interface standards that support creation and automated integration of complex“multi-model” models at successively higher and lower levels of detail, enabling integration of individualproduct and process models into system-of-systems models with accurate relating and cascading of inter-dependencies and interactions.

The task shall focus on providing and demonstrating automated system-of-systems model composition forone representative product family and associated processes for one selected manufacturing sector, in-cluding life-cycle modeling functions for domains including customer and logistics support (supply,maintenance, repair, etc.).

Task 4 – Secure System-of-Systems Data Management – This task shall adapt and apply leading-edgemulti-level information security tools to provide capabilities for compartmentalization, security, and long-term management of data to support system-of-systems modeling that integrates information from multi-ple sources having different security constraints. The task shall demonstrate the ability of the system-of-systems management environment to invoke appropriate security actions as models and associated infor-mation are acted upon by applications and users – preventing access to data for which a user is notauthorized, without barring appropriate access. As an example, such functionality includes enabling auser to access a process model in order to run a simulation of a manufacturing step, but preventing theuser from accessing the underlying simulation code.

This task shall also demonstrate the capability to automatically assign security properties to a product orprocess design when it achieves sufficient fidelity or adds features requiring protection. The demonstra-tions associated with this task shall be conducted on a stand-alone secured system but involving no actualsensitive data, and representatives of interested government security organizations (e.g., DSS, NSA) shallbe invited to provide review and technical recommendations.

Task 5 – Self-Completing Models – This task shall explore approaches to create models that know theirown attributes and can interact with other model objects to complete a resulting superset of attributes, re-lationships, and behaviors. This effort shall leverage work expected to be ongoing under the NGMTI In-telligent Models project.




4.1 BENEFITS TO DOD

This project will provide significant advances in DoD capabilities supporting the next generation ofsimulation-based acquisition processes. The advanced functionalities described in Section 4.2 for generalindustry will be especially valuable in the defense manufacturing base, where the products are far morecomplex and dependent on developmental technologies and customer-driven requirements in design,manufacture, and support. DoD labs and evaluators will be able to work directly “in the loop” with con-tractor teams in collaborative engineering environments, where product models can be quickly verifiedwith analytical codes, tuned, and exercised in operational simulations in days instead of weeks or months.This will provide a much improved ability to identify, quantify, mitigate, and manage risks in every phaseof development. Conceptual development programs will be able to deliver conceptual designs that aresignificantly advanced over those obtainable with current practices, and costs and time for engineeringand manufacturing development should be notably reduced.

Current practices using “engineering judgment” and rough approximations of technical performance andcost will be replaced by high-fidelity estimates and calculations based on well-defined, government-accepted models. This will greatly reduce the burden of creating and evaluating cost estimates, and pro-vide a better ability to counter low-balling, defective pricing, and similar issues that continue to challengethe defense acquisition community.

Developers of sensors, weapons, electronics boxes, and other subsystems that must integrate with air,ground, and naval platforms will be able to exercise their designs in realistic environments that incorpo-rate high-fidelity platform models instead of merely running individual models based on temperature, ac-celerate, vibration, and other specifications. This is expected to reduce final system integration time andcost and aid in identification of “emergent” properties, effects, and issues that will likely be encounteredwhen the subsystems are physically integrated and tested in OpEval programs.

Operational support activities will realize significant benefits through the ability to simulate, with highfidelity, the effects of wear and tear on complex systems in combat and training. Development of newproducts and product improvements will draw on accurate life-cycle process models incorporating actualperformance history for similar and prior-generation products they will replace, greatly increasing accu-racy in predicting and optimizing for key attributes such as Ao, MTBF, MTTR, and level-of-repair.Weapon system operators and maintainers will not have to train with virtual part task trainers; rather, eve-ryone will interact with a single master product model that contains or links to all constituent product andprocess models related to that product – including support equipment, tools, spares, and weapons.


The ability to integrate manufacturing enterprise systems and processes into a “system of systems” or“full digital mock-up” of the product system through model-based methodologies will deliver dramaticbenefits for U.S. manufacturers. The composable and decomposable models created with the system-of-systems philosophy will enable evaluation of total system performance within its operational contextwhile in the virtual realm, before physical systems are built. The time and cost of product and processdevelopment, production planning, and life-cycle support could be reduced by an estimated 20-40% byenabling engineering and planning tools to automatically and autonomously access real-world data to op-timize solution approaches. Typical errors and cost/schedule impacts associated with designers and plan-ners lacking the full and correct information they need to make the right decisions will be eliminated.Designers will be able to “exercise” their solutions in robust, mathematically accurate simulation envi-ronments that mirror the real-world environment in which the product will be built, operated, or used,identifying issues that typically are not uncovered until a product has been in the marketplace for monthsor even years.



Product design applications will automatically interface with highly precise process models for manufac-turing and life-cycle support functions, ensuring that product designs are production-ready and totallyoptimized for low cost and compliance to all requirements within hours of design approval. Programmanagers will know exactly how much a product will cost because the product model will continuouslyinterrogate the enterprise’s procurement and finance systems for the latest data, including supplier pric-ing. This includes the capability to rapidly re-price product realization and support strategies on demandto evaluate the impacts of changes in specifications, quantities, production rates, product mix, processimprovements, and other factors.


The proposed project schedule is provided below. The estimated cost for the 32-month effort is$4.4 million.


Risk for some parts of this project is assessed as moderate, since the technical approach uses existingtechnologies as a point of departure and specific capabilities to be developed will be bounded throughselection of a specific set of existing, representative products in a limited number of manufacturing sec-tors. However, risk for the ultimate system-of-systems capability is high because of technical complexityand vendor proprietary issues that must first be resolved. Both the product owner (e.g., the devel-oper/manufacturer) and the model-based tool vendor community will be actively engaged, minimizingexternal funding requirements while ensuring a deep base of expertise to support development of the de-sired capabilities.

Technology readiness for this area is assessed at TRL 2-3 from the manufacturing enterprise perspective,although related concepts are well advanced in the DoD simulation community. This project is expectedto advance the technology to TRL 6 with demonstration of prototype capabilities in a relevant environ-ment.



NGMTI PROJECT MBE-4

ENTERPRISE-WIDE COST MODELING

1.0 PROJECT SUMMARY

The objective of this project is to develop the capability to establish and manage comprehensive, highlyprecise total product cost models that reflect not only traditional materials and direct production costs, butalso time-discounted (net present value) design and other investment costs, capital resources, overheadburdens, regulatory compliance costs, and other indirect cost factors. These cost elements will not bestatic inputs; rather, the models will link to the “live” sources of cost data, down to the lowest level of thesupply chain. This will provide a continuously updated and accurate view of all cost factors to guidetechnical and business decisions.

2.0 CHALLENGE

U.S. manufacturing enterprises face tremendous challenges in maintaining economic competitiveness inglobal markets where competitors have the advantage of lower direct labor and fringe costs, lower oper-ating costs due to lesser regulatory requirements, or government subsidization, countered only by in-creased transportation costs and higher potential for distance-induced delay. While these issues cannot besolved directly through technological means, technology is the key to maximizing competitiveness – byreducing costs as part of the total product value equation. Implementation of lean principles and mod-ernization of processes and equipment help companies eliminate waste, reduce uncertainties and othernon-value-added costs, and increase productivity. Outsourcing or moving selected operations offshore totake advantage of lower labor costs is also helping U.S. manufacturers remain competitive, although jobloss for American workers is an increasing volatile issue.

In the defense arena, the cost issue is primarily one of affordability. Despite more than a decade of in-tense focus on improving the affordability of U.S. weapon systems and more efficiently controlling de-velopment and production costs, major Department of Defense (DoD) acquisition programs continue toexperience significant cost problems. While many factors play in the defense acquisition equation, thecommon denominator in cost escalation is the poor ability to estimate costs accurately up front and accu-rately calculate the financial impacts of changes during the system development process. This can lead toa “death spiral” where increasing unit costs force the customer to reduce production quantities, which inturn forces the manufacturer to increase unit costs to re-spread the nonrecurring costs that must be recov-ered in production.

Model-based technical and business processes are key to solving these challenges for both the commercialand defense sectors. In the NGMTI vision, the enterprise’s design, production, operations, and supportenvironments will be seamlessly integrated, with model-based processes linked to living knowledge basesthat provide continuously current technical and business data to all applications that need them.

Although most manufacturers rely heavily on modeling applications (i.e., spreadsheets or discrete finan-cial models) to develop cost estimates, and the current generation of tools is providing greatly improvedabilities to model production costs (Figure 2-1), companies must begin to use more integrated model-based systems that enable all the components of the enterprise to communicate the critical informationneeded to understand cost factors and impacts in a collaborative, real-time fashion to support businessdecisions.

Business process modeling techniques are well-developed and widely used for analyzing operations tobetter understand the factors that influence the cost of processes and to identify non-value-added activi-



ties. Activity-based cost modeling techniques have come a long way toward helping manufacturersmodel labor costs, and have been invaluable in reducing costs in the service sector. Although the avail-able base of specialized cost modeling tools continues to grow, such models must go further than thosecurrently available.

Product data management (PDM), enterprise resource planning (ERP), and enterprise resource manage-ment (ERM) software packages represent the state of the art for today’s model-based enterprise manage-ment functions. All of these systems offer financial modules; however, most off-the-shelf models do notincorporate traditionally non-financial parameters, and thus do not adequately support higher-level busi-ness decision processes with the needed accuracy (i.e., they do not enable accurate financial predictionsbeyond extrapolation of trends from known financial data.) Such models are also highly dependent on theaccuracy of their inputs. These models do not handle uncertainty well. While some allow Monte Carlo-type analysis, it is not applicable when the model structure is variable (uncertain).

Hard barriers remain despite the wealth of financial modeling tools available. Most existing tools are notresponsive to change because they are not directly connected to the sources of their underlying data. Thehigh level of manual work required to collect and update input data, and modify model structures andformulas to reflect changes, makes model accuracy and currency problematic.

Further, current financial models (and modeling tools) are not well integrated with the rest of the enter-prise, especially in those areas outside the normal financial envelope. This makes cost estimating andfinancial forecasting difficult, to the extent that most estimates are valid only a few months into the fu-ture. Much estimating, particularly in the defense industry, continues to be driven by top-down processeswhere “bogeys” are assigned to subsystems and functional departments so that engineers and staff mem-bers can back into their cost targets. As a result, estimators tend to either low-ball or inflate figures toaccommodate perceived uncertainties and navigate internal negotiation processes to ensure their scope ofwork receives adequate funding.

These issues are greatly magnified in the context of supply chain management. Most prime-supplieragreements are based on quotes with levels of fidelity ranging from excellent to nonexistent. Participantsat all levels of the supply chain rely on “scope creep” and customer-driven design changes to cover esti-mating shortfalls and protect profit margins. Problems with one supplier often ripple up, down, andacross the supply chain as the prime and the different team members work to get the project back on trackwhile protecting their respective interests.

Poor ability to model the cost impact of technical decisions across the supply chain remains a majorproblem in the defense and aerospace sectors. High-profile programs such as NASA’s space station, the

CostimatorCostLink/AE

Figure 2-1. Current commercial estimating applications greatly improve capabilities to model production costs.



Air Force’s F-22 Raptor, and the Army’s RAH-66 Comanche experienced extreme cost escalations due toinadequate estimating, failure to fully account for cost risk, and inability to accurately project the finan-cial impacts of design changes. Restructuring the Comanche program in 2002 (the sixth time the programwas restructured since its inception in 1983) doubled the aircraft’s development budget to over $6 billion,and unit cost grew from $24 million to more than $32 million.17 With the development program experi-encing numerous technical issues requiring still further outlays, the Army terminated Comanche in Febru-ary 2004 – all but writing off more than $7 billion of investment due largely to the inability to accuratelyestimate costs and predict the impact of technical changes.

Excellent capability does exist, however. One example of a successful model-based estimating system isCOCOMO, a well-established standard for estimating the cost of software products. Software developersclosely track productivity metrics that enable them to model costs for software systems based on new andmodified lines of code. Coupled with improved standards for software development (e.g., the Carnegie-Mellon Software Engineering Institute’s Capability Maturity Model), these tools have contributed signifi-cantly to reduce the time, cost, and risk of soft-ware development for military products.

Organizations such as the Electronic SystemsCost Modeling Laboratory (ESCML) at theUniversity of Maryland provide on-line modelsfor understanding the cost impacts of technol-ogy obsolescence, test rework, and other finan-cial drivers in electronic systems design andmanufacturing. ESCML’s MOCA (Mitigationof Obsolescence Cost Analysis), shown in Fig-ure 2-2, is an excellent design tool for deter-mining part obsolescence impact on life-cyclecosts for the electronic systems based on futureproduction projections, maintenance require-ments, and part obsolescence forecasts. Using adetailed cost analysis model, MOCA determinesthe cost-based optimum design refresh planover the life of the system. Outputs from thisanalysis are used as inputs to the PRICE H/Lcommercial software tools for predicting systemlife-cycle costs.18

3.0 PROPOSED SOLUTION AND PROJECTPLAN

In the future, manufacturing firms will applypowerful enterprise management tools that sup-port all decision processes with continuouslycurrent financial and non-financial data that are directly accessible to and from product (and associatedprocess) models. These tools will enable product managers at all levels to quickly ascertain, with a highdegree of confidence, the financial implications of any decision or change. The simulations and modelsthat drive the enterprise’s business processes will capture financial data and relationships with sufficientaccuracy to provide a continuous and clear view of performance versus financial metrics, enabling man-agers to continuously fine-tune financial strategies for business success.

17 http://www.globalsecurity.org/military/systems/aircraft/rah-66.htm.18 http://www.enme.umd.edu/ESCML.

Figure 2-2. MOCA models the cost implications of allpossible combinations of design refresh points in an elec-

tronic system's life cycle.



This proposed project will develop, demonstrate, and validate the technologies and strategies required tointegrate the entire enterprise and its supply chain in cost estimating and management, extending far be-yond current generations of PDM, ERP, and ERM systems. Successful demonstration will stimulate theevolution and implementation of model-based cost management in all sectors of industry.

This project will involve financial and engineering experts from industry, academia, and governmentworking together to develop a robust cost traceability architecture, develop software and interfaces to en-able the required communications and data management, and demonstrate the results in a way useful forfuture investment decisions.

The product cost estimating architecture and software developed will provide the capability to automati-cally capture cost information for labor, materials, parts, commodities, and support elements along withappropriate burdens and links to the associated product and process models at each level of the supplychain. These models will update themselves automatically whenever the underlying data change, and willprovide alerts to all affected functions. Because cost information will be captured down to the lowestlevel of the design or activity, basic cost information for an element will travel with it transparently whenthe same element is applied to a different product or process. Routine changes in cost basis (such asfluctuations in material or commodity item costs) will be handled automatically within defined limits. Inthe case of design changes, the system will enable PDM applications to automatically extract the changeinformation from the product/process model and pass it to the appropriate function (Engineering, Pur-chasing, Subcontracts, etc.) for re-estimating.

3.1 GOALS AND REQUIREMENTS FOR ENTERPRISE-WIDE COST MODELING

The proposed project addresses two major goals defined in the NGMTI Roadmap for the Model-BasedEnterprise.

• Goal 1: Enterprise-Wide Product & Process Cost Models – Provide cost modeling systems andtechniques that integrate all required data, from within and external to the enterprise, to support high-fidelity analysis of development costs, production costs, life-cycle support costs, profitability, financialrisk, and other cost attributes of a product, process, or operation. (L)19

– Integrated Cost Modeling Application Architecture – Develop a global cost modeling applicationstructure that provides for capture and linking of all sources of cost – acquisition, nonrecurring de-sign and development, engineering changes, recurring production, product ownership and support,retirement, regulatory factors, etc. – into product, process, and operations models that interface withapplications and business systems to support real-time decision making in all phases of product, de-sign, manufacture, and support. (M-L)

– Common Cost Model Templates – Develop and validate a series of cost model templates that iden-tify the major cost elements for common product and part families, materials and manufacturingprocesses, life-cycle support processes, business operations, and other sources of cost in differentbusiness sectors (e.g., aerospace, automotive, chemical). (S)

– Product/Process Family Cost Models – Develop suites of generic, PDM system-compatible pro-duction cost models for common product and process types. Include the capability to automaticallytailor a generic product or process cost model to include additional features or attributes included ina specific design. (M-L)

– Unified User Interface for Cost Modeling – Develop easy-to-use interfaces that enable differentusers (engineers, estimators, etc.) to generate and apply accurate, comprehensive cost models for anyenterprise function. (M)





– Actual Cost Capture – Develop mechanisms for capture of actuals for all costs (recurring, non-recurring, direct and indirect) associated with product manufacture and other enterprise operations,and feed this information back to PDM and financial management systems to refine cost model fi-delity. (S-M)

– Model-Based Estimating – Develop costing tools that decompose product and process models andinterface with the enterprise’s cost history knowledge base and financial systems to automaticallygenerate bases of estimate (BOEs) for development and production based on programmatic require-ments and historical costs for similar products, labor rates, supplier quotes, and current rates andfactors. Include the capability to automatically score BOEs for confidence level and flag areas ofuncertainty for management/engineering attention. (L)

– Automated Cost Modeling – Develop cost modeling applications that automatically generate billsof material from the product or process model; calculate and communicate the effects of a change inone parameter across the entire cost model; and perform dynamic updates (with appropriate alertsand approvals) from enterprise data sources to ensure currency. (M)

– Integrated Life-Cycle Cost Modeling – Develop methods for integrating life-cycle considerationssuch as maintenance, repair, sparing, recycling, and disposal into product, process, and operationscost models. (M)

– Integrated Supply Chain Cost Modeling – Develop and unify product modeling standards andtechniques to enable seamless, automated interfacing/integration of product and process cost modelsamong partners and suppliers, with provision for protection of sensitive data (e.g., rates, factors, andformulas). (M)

– Cost Sensitivity & Uncertainty Modeling – Develop analytical applications and information elici-tation methods that use probabilistic, statistical, and other mathematical analysis tools to calculatecost sensitivities and quantify uncertainties for any aspect of recurring or nonrecurring cost. Providethe capability to link uncertainty models directly to product, process models, and operations to en-able automatic updating of impacts and risk factors in response to changes. (M-L)

Additional requirements related to this particular goal are extracted from the Resource Managementsection of the MBE Roadmap:

– Model-Based Cost Standards – Establish financial information standards that support the creationand integration of comprehensive cost models for products and processes. Include provision forcapture of both direct and indirect costs; integration of life-cycle factors such as maintenance and re-pair, spares, training, and recycling/disposal; and tailoring to meet the unique requirements of a par-ticular industry sector (e.g., defense). (M)

– Intelligent Cost Models – Develop cost modeling techniques that automatically distribute the ef-fects of a change in one cost parameter across all affected cost models, and automatically performdynamic updates against enterprise data sources to ensure currency of cost data. Include the capa-bility to alert all affected business functions when costs change beyond defined thresholds. (M-L)

– Multi-Level Cost Modeling – Develop tools to model costs at different levels and from differentperspectives (e.g., activity-based versus product-based) and automatically present the user-requestedview. Include the capability to "click down" to the lowest level of the model. (M-L)



• Goal 2: Enterprise Financial Simulation Environment – Provide the capability to obtain and evalu-ate current financial status information and requirements, and accurately predict effects of contem-plated actions or events on capital levels, funds flow, profitability, ROS/ROI, rates and factors, andother financial factors. (L)

– Accounting Integration Model – Develop a comprehensive, generic accounting data model towhich an individual enterprise’s cost structure can be automatically mapped, enabling automatedcorrelation of cost elements and associated data among all partners in a supply chain. (M)

– Distributed Financial Engineering Tool Suite – Develop financial engineering and analysis toolsto enable integrated modeling of all finance functions (estimating, accounting, asset management,cash flow management, etc.) throughout the enterprise. (M-L)

– Extended Enterprise Financial Data Interchange – Develop methods and tools to integrate andcontinuously update financial data across the extended enterprise to provide a unified view of finan-cial health, status, and issues. (M)


Execution of this project will involve the following six tasks.

Task 1 – Project Planning: This task shall assemble a team of industry, academic, and governmentcontributors who will develop the detailed technical plan and approach to accomplish the requirements ofthe project, including the technical goals defined above in Section 3.1. This effort will include interactionwith sponsoring agencies and interested organizations to define the scope for the development efforts andthe pilots to be conducted under Task 3 and Task 5.

Task 2 – Model-Based Cost Architecture: This task shall develop a generic architecture that maps oneor several representative products to all established and potential sources of cost and cost impact, andcharacterizes requirements for providing linkages to and from the product model. Consideration shall begiven to addressing a commercial product and a DoD product of sufficient complexity to serve as chal-lenging testbeds for development and also as applications where the delivered capabilities will providedirect value to the project participants.

The project team shall survey available cost modeling tools and related cost management applications(e.g., PDM and ERM packages) and define the new elements and functionalities needed to enhance thecost estimating capability and provide the required connectivity and communications for real- or near-real-time updating. The linkages expected to require focused development include those to human re-sources, procurement/subcontracting, support elements, and product/process configuration managementfunctions. The project team will work with the application vendor community to select baseline tools andlaunch efforts to develop and implement the required enhancements.

The project will require access to fully populated product and process models with current cost data andPDM interfaces to a participating enterprise’s financial systems. If these cannot be obtained through aproject participant, the team shall develop a working set to support the required development and demon-strations.

Task 3 – Application Development: This task shall develop the needed modifications to enhance theselected design/PDM and estimating tools to provide comprehensive model-based cost estimating func-tionality. Applications shall be developed based on the requirements defined in Task 2 and address therequirements outlined in Section 3.1 above.

Task 4 – Data Linking: This task shall develop the required linkages between the product model and theidentified cost elements. The linkage shall facilitate cost information flow both ways at the productmodel boundary, allow the models to perform autonomous updates when source data changes, issue alertsto affected functions, and support interoperability of the whole complex of models.



Task 5 – Demonstration & Validation: This task shall exercise the enterprise-wide product cost modelsin selected industry sectors to verify the performance of the developed functionalities and demonstrate thevalue of the delivered capability.

Task 6 – Final Report: This task shall document the developed capabilities and assess the results of thedemonstration/validation task to provide industry users with the data they need to support investment de-cisions for implementation of the new and enhanced tools, and provide the tool developers with require-ments for further development and commercialization.



This project will provide technologies and demonstrated capabilities that enable all manufacturers to cre-ate and manage product cost models that incorporate data from all levels of the enterprise and its supplychains. Without this capability, the alternative is to continue the current limited and often very inadequatecost estimating techniques. As discussed in Section 2.0, these costs can be staggering.

Enterprise-wide cost modeling will complement other model-based business management systems – em-powered by fully interconnected business processes – to provide real-time visibility into all aspects ofbusiness performance. These systems will enable managers of all sizes of enterprises to rapidly analyzefinancial issues and consistently make the best decisions by considering all relevant factors and options.Real-time cost reporting systems will continuously status performance to the business models to rapidlydetect cost problems. Desktop analytical tools coupled to product and process models and associated costdata sources will enable fast evaluation of options for workarounds and recovery planning.

With product and process models able to link to actual cost histories captured in the enterprise’s knowl-edge base, preparation of estimates will require minutes rather than hours, while ensuring that estimatesare accurate and complete. This will greatly reduce the need for functional finance staffs while enablingtechnical and management staff to focus on value-added work.

Model-based costing will also enable companies to provide required cost information to partners and sup-ply chain members without revealing sensitive financial data such as rates and factors. This will facilitategreater openness in teaming on large, multi-company programs where competitors today have great diffi-culty working together.

Financial models will be well integrated into all enterprise functions and, as a result, realistic cost andprofit targets will be easy to set and track. Calculating the net present value of expected future cash flowswill continue to be a widely used method for evaluating financial options, but organizations will also be-gin evaluation of the financial implications of non-financial cost contributors using this newly demon-strated pricing model, Monte Carlo simulations, and other powerful predictive modeling tools.

4.2 BENEFITS TO DOD

The enterprise-wide cost modeling capabilities developed under this project have the potential to deliverbillions of dollars of direct savings to DoD. Despite aggressive acquisition reforms, cost estimating andcost management remain two of the most intractable and serious problems in the DoD acquisition com-munity. By more closely linking product and process models to “live” sources of cost data and actualhistory of previous work – as opposed to “engineering judgment based on similar scope on a similar pro-gram” – all of the military services will be able to have much clearer visibility into contractor’s proposedcosts as well as early and accurate warning of the cost impacts of design changes. DoD estimators will beable to obtain much greater fidelity in performing should-cost exercises to bound the scope of plannedprograms. Low-balling and defective pricing practices will be greatly curtailed, since all estimates will bedirectly traceable to underlying sources of cost information – including vendor and subcontractor quotesas well as labor and material estimates. DoD program managers will also be able to get much more accu-



rate estimates of the cost impacts of technical problems, since the system cost model will automaticallycalculate the cost of redesign efforts resulting from requirements changes or test failures.

The ability to collect operation and maintenance cost data across the life of a weapon system or othermilitary product will also provide far greater visibility into total cost of ownership and other measures oflife-cycle cost. This will enable the user community to quickly identify major areas of O&S expense thatcan be modeled and analyzed for potential improvement, allowing upgrade and replacement decisions tobe made with confidence that the savings promised will be the savings delivered.


The following project plan assumes a start date of October 2006 for the 30-month effort. Resources forthe defined scope of work are estimated at approximately $8.4 million.


While cost modeling is a mature discipline, linking of product and process models to sources of cost inputis a significant challenge, as is managing those linkages to ensure accuracy and protect the integrity of the“living” cost estimate. The technical risk associated with providing simple linkages is low; however, theproblems with accomplishing this objective for a complex product increase geometrically due to the muchlarger number of input sources. Reliably relating hundreds or potentially thousands of dependencies isalso a major source of risk, as is the challenge of incorporating difficult-to-quantify factors such as devel-opment risk. Overall risk for the project is assessed as medium.



NGMTI PROJECT MBE-10

MODEL-BASED DISTRIBUTION

1.0 PROJECT SUMMARY

The objective of this project is to develop enabling technologies and conduct proof-of-principle demon-strations of model-based distribution capabilities able to support highly complex distribution requirementssuch as those for fielded military systems. The project focuses on providing a generic system frameworkthat supports design for distribution, distribution planning, management/execution, and re-planning inresponse to changes in demand.

2.0 CHALLENGE

Higher customer expectations, coupled with lean operations strategies, are placing tremendous pressureon manufacturers’ distribution systems. Leading-edge companies that use sophisticated modeling tools todistribute large quantities of small items include well-known shipping service providers Federal Express,UPS, and DHL. The food and beverage industry, automotive industry, and consumer product industriesmove millions of product units to established distribution points every month, and have highly sophisti-cated distribution systems that enable their business units to predict demand, queue up the right products,and get them quickly to where they need to be. These systems are very responsive, enabling customers orusers at the distribution points to submit orders and have the right products en route in 24 hours or less.Distribution modeling tools enable rapid re-planning of order fulfillments in the event of system upsets,including severe weather events (hurricanes, blizzards, etc.), transportation bottlenecks, carrier strikes,and similar problems.

The distribution function for most large and mid-sized manufacturers operates using mature models thatincorporate geographic information, logistics, and delivery schedules into highly effective systems forproduct delivery to point of sale. Ketera, MindFlow, Oracle, and others provide a myriad of solutions forspend analysis, sourcing analytics, and cost performance analysis as well as solutions for modeling com-plex shipping parameters that include duties, taxes, tariffs, and other charges associated with complexinternational distribution systems. Leading enterprise management systems providers such as SAP andASPEN include robust distribution planning and analysis capabilities as part of their product lines. Man-hattan Associates offers its Integrated Logistics Solution suite of programs as part of industry-focusedsupply chain execution and optimization product line that manages the entire supply chain, from source toconsumption.

Distribution management is also closely tied to inventory management, and entity-relationship distribu-tion models typically include inventory management functions (Figure 2-1). Inventory modeling is awell-developed discipline, and the only significant barrier to enabling real-time problem-solving in thisarea is the difficulty of linking live data to the model. The high cost of carrying product and material in-ventory between the point of resource need and the point of product sale has driven most manufacturers toadopt just-in-time practices that minimize in-process inventory. Advanced model-based inventory man-agement (AIM) systems are mature and widely used to improve warehouse operation and efficiency. In-ternal routing models permit the definition of step-by-step paths to follow the movement of goods and allthe properties assigned to each step: label printing, confirmation options, status, reference, and lot/serialchanges.

The future vision for distribution is, for commercial product sectors, one of incremental advances buildingoff of current capabilities. Future model-based distribution management systems will deliver products to



point of sale and point of use through the most efficient means, forecasting and responding proactively toevery fluctuation in geographic demand. The ability to accurately model the ebb and flow of demandbased on seasonal patterns, advertising and marketing outlays, and other factors that influence market pullwill enable manufacturers to apply lean strategies, closely matching production levels to drawdown rates.This will enable enterprises to reduce inventory carrying costs while preserving the ability to quicklysurge to meet upswings in demand.

Advanced modeling and simulation capabilities will enable companies to analyze different distributionapproaches for new or current products, optimizing for location, stock levels, choice of carriers, etc. andproviding the ability to quickly respond to changes in the distribution environment – such as fluctuationsin carrier capacity, changing fuel prices, and congestion of local and regional transportation routes. Thesesystems will also enable modeling of complex financial factors including duties, taxes, tariffs, and othercharges to optimize the design of international distribution systems.

Widespread use of RFID sensors coupled to cellular communications networks will provide managerswith real-time visibility of assets anywhere in the distribution network, moving beyond present common-carrier tracking to enable immediate, precise location of any shipment anywhere in the delivery channel.These technologies will enable every product to be tracked from origin to point of sale, providing the real-

20 Michelle A. Poolet, “Product Distribution Metamodel,” July 2002.

http://www.windowsitpro.com/SQLServer/Article/ArticleID/24912/24912.html.

Figure 2-1. Inventory modeling is a well-developed discipline closely tied to distribution modeling. 20



time information that model-based inventory management systems need to monitor demand, adjust manu-facturing throughput, and direct supply and distribution networks to ensure that the right makes, models,and styles of product reach customers on time in every market. Distribution management systems willcollect this information continuously, make it available to internal and external customer support systems,and compare actual to predicted performance of the distribution system to flag performance issues, ex-plore solutions, and update cost and time information used by the enterprise’s product and process mod-els.

More efficient distribution planning will be enabled by intelligent resource management models that linkto ERP/ERM systems to monitor marketing, sales, and distribution systems and external informationsources and accurately predict near- and long-term variations in product demand by region and locality,automatically recalculating requirements and redirecting inventory at the enterprise level and at all af-fected operating sites. The system will enable product managers and operations managers to understand,with a high degree of confidence, what requirements are coming the next day, next week, next month, andnext year. The system will enable them to quickly and systematically evaluate the pros, cons, and deeperimplications of all options for responding to those requirements. More importantly, the system will en-able them to re-plan quickly as requirements change and as new opportunities and challenges arise day-to-day.

The system will monitor all indicators that directly and indirectly influence demand – local and regionaleconomic trends, weather, new product introductions by competitors, and the like – and enable enterprisemanagers to quickly identify issues, evaluate options, and determine the best response.

The challenges in achieving this vision are relatively straightforward. The distribution system for themodel-based enterprise must be able to tie into the product definition model and the life-cycle supportmodel in order to define the requirements for what product and product elements (e.g., spares, repairparts, consumables, support tools/equipment) will be entering the distribution chain. It must be able to tieinto the customer requirements management function and the manufacturing planning system, in order tounderstand what items in what quantities need to arrive at what location and by when. And, the systemmust be able to tie into the enterprise’s “environmental surveillance” function in order to monitor, ana-lyze, and respond to changes in requirements, including subtle qualitative factors that influence demand.The system must also be able to operate equally efficiently in reverse – providing the capability to acceptproducts back at the end of their useful life for reprocessing, reuse, reclamation, and disposal.

These challenges are significant for the military services. The Department of Defense, through its De-fense Logistics Agency, uses sophisticated models to move people and supplies around the world; how-ever, DoD faces formidable challenges because it must keep pace with rapidly changing requirements forone of the world’s largest and most complex inventory of equipment, supplies, consumables, spares, andreplacement parts. Unlike commercial product fulfillment, defense logistical supply does not enjoy theluxury of simple product requirements with well-defined and stable distribution requirements. Militaryoperations often involve situations that develop rapidly with issues that, although identified in contin-gency planning, cannot be solved quickly or easily.

During the first Gulf War (Operation Desert Shield/Storm), the responsiveness of the logistics systemswere degraded by thousands of duplicate orders placed because operational units had inadequate visibilityover the status of their requisitions. Moreover, an enormous amount of materiel was shipped to the thea-ter which was not readily available to our forces because of poor control and poor visibility of assets in-theater. Such problems reduce the readiness and effectiveness of combat forces and place unnecessarystrain on the transportation system.21

Despite significant improvements afforded by initiatives such as the Defense Logistics Agency’s JointTotal Asset Visibility (JTAV) program and the DoD’s Integrated Data Environment (IDE) program,

21 http://www.dla.mil/j-6/jtav/.



military distribution management remains a huge challenge. The extent of the continuing operations inIraq and Afghanistan, for example, has placed a tremendous strain on the ability of the military to providevehicle armor to counter roadside bombs and batteries to keep weapons, communications, and other pow-ered equipment operational. The military air transport fleet has been severely pressed in moving massivequantities of materiel and supplies to the operational theaters, and the ground transport fleet has beenequally hard-pressed to get the right supplies from main operating bases to forward units. In the earlydays of the war in Iraq, supply problems greatly complicated the ability of field commanders to sustainhigh operational tempos in engaging enemy units that offered either unexpectedly strong resistance or fledin headlong retreat.

Modeling and simulation have been key to improving logistics responsiveness under efforts such as theForward Stock Positioning program (Figure 2-2). Major weapon systems now in development, such asthe F-35 Joint Strike Fighter(JSF), are making extensive useof modeling techniques to opti-mize the logistics approach forthe three JSF variants. The JSFsupport strategy is centered onthe concept of autonomic logis-tics, wherein every aircraftmonitors its health using on-board sensors and reports fail-ures (actual or predicted) to thelogistics support system. Thelogistics system in turn auto-matically places the maintenancerequest and orders the necessaryparts and materials so that therepair team is ready to start workas soon as the incoming aircraftrolls to a stop. The pulling of aspare part from the on-hand as-sets automatically triggers an order through the logistics supply chain for re-stocking, either from a con-tractor depot or from the factory. In addition to enabling the fast combat turns needed to sustain very highoperating tempos, the autonomic logistics approach will enable the JSF support network to maintain clearand continuously current visibility of stock levels. This will enable early detection of unusual draw-downs, maximizing lead time for re-supply as well as flagging potential reliability issues requiring inves-tigation and corrective action.

3.0 PROPOSED SOLUTION AND PROJECT DESCRIPTION

While distribution management is well-developed in many industries, the current capabilities are theproduct of years of development and optimization using in-house developed or proprietary discrete eventtools to systematize and refine well-understood distribution requirements. For the model-based enter-prises of the future, the distribution management capability must be integral to the product realization andsupport system. It must be based on open standards, accommodate qualitative variables and far morecomplex dependencies than possible today, and integrate seamlessly with other model-based enterprisesystems as discussed in Section 2.0. Key goals for this capability follow.

22 Scott Rosbaugh, Stockage Committee Update, DLA CSR Conference Presentation, September 2003.

Figure 2-2. In-depth modeling of transportation capabilities has beenkey to DLA’s aggressive efforts to improve efficiency and responsiveness

in distribution of materiel and supplies.22



3.1 GOALS AND REQUIREMENTS FOR MODEL-BASED DISTRIBUTION

• Goal 1: Real-Time, Responsive Distribution Management – Provide the capability to calculate op-timal product allocation to points of sale/use and staging nodes based on current need, rapidly deter-mine the most efficient means of distribution for new shipments, and interface with product trackingsystems to direct or redirect assets to points of need anywhere in the distribution network. (M-L)23

– Design for Distribution – Develop modeling capabilities to optimize product designs for efficientdistribution. Include the capability to support final assembly at point of sale/use and optimize per-formance and cost-effectiveness in protecting, storing, and transporting the product from origin todestination. (M)

– Integrated Distribution Modeling – Develop modeling and simulation applications that enableplanning and management of distribution requirements based on predicted and actual demand anddistribution network capabilities. Include the capability to automatically determine best deliverymethods and routes based on time, cost, and capacity factors; to analyze stock drawdown patterns toredirect product to demand points; and to identify opportunities to reengineer distribution channels toenhance performance and profitability. (M)

– Model-Based Product Tracking – Develop product tracking systems that enable continuous or on-demand location of products in the distribution network, with the capability to locate any asset toprecise GPS coordinates and automatically direct or redirect assets while updating any resultingchanges in the product distribution model. (S)

– Pull-Based Distribution – Develop modeling capabilities that enable the distribution system toautomatically respond to changes in demand by initiating shipments from inventory and reportingdrawdowns to the factory production planning system. (L)

– Special Materials Management – Develop applications to support modeling and planning for dis-tribution and tracking of radiological materials, hazardous chemicals, and other high-value/high-sensitivity products requiring special handling for safety, environmental, or security reasons. Includethe ability for the distribution model to interface with regulatory requirements databases, automati-cally detect any changes that affect the enterprise’s distribution strategies and mechanisms, and sup-port analysis to develop necessary changes. (M)

• Goal 2: Intelligent Asset/Inventory Modeling – Provide modeling tools that monitor sources of in-ventory requirements change and aid users in defining and implementing optimal responses to change.(L)

– Adaptive Inventory Modeling Applications – Develop generic inventory modeling applicationsthat can be readily adapted to specific industry sectors and different supply chain roles (i.e., OEM,major subcontractor, supplier). Include the ability to integrate products having widely varying in-ventory characteristics, easily add new products to the model, and interface with distribution plan-ning and management systems. (M)


– Automated Demand Prediction – Develop modeling tools able to evaluate variables that impactproduct demand over time, and accurately forecast inventory requirements for all makes, models,





and styles of product. Include the capability to extrapolate demand trends for new product introduc-tions based on initial orders and sales, and the ability to model the demand impacts of disruptiveevents such as strikes, natural disasters, political upheaval, or introduction of competing products.(L)



This project will develop enabling technologies and conduct proof-of-principle demonstrations of real-time, model-based distribution capabilities able to support highly complex distribution requirements suchas those for fielded military systems. The project focuses on providing a generic system framework thatsupports distribution planning, management/execution, and re-planning in response to changes in de-mand.

Task 1 – Real-Time, Responsive Distribution Management: This task shall provide the capability tocalculate optimal product allocation to points of sale/use and staging nodes based on current need, rapidlydetermine the most efficient means of distribution for new shipments, and interface with product trackingsystems to direct or redirect assets to points of need anywhere in the distribution network.

The activity shall commence with a focused effort to develop and verify system requirements with thecommercial and government user community, particularly DLA. Existing commercial tools shall beevaluated for the potential to support a model-based distribution capability consistent with the model-based enterprise vision, and one or more application vendors shall be brought onboard to support modifi-cation of existing tools, development of new tools, and system integration. The project team shall choosea representative set of complex product types of interest to both DoD and commercial industry, and en-gage one or more leading manufacturers in each sector to support testing and piloting of the capabilities tobe developed. The project team shall coordinate with DLA in order to maximize synergy with an ongoingagency such as JTAV/IDE.

Specific capabilities to be developed under this task include:

1. Modeling capabilities to optimize product designs for efficient distribution, including the capa-bility to optimize for final assembly at point of use and for performance and cost-effectiveness inprotecting, storing, and transporting the product.

2. Modeling and simulation capabilities that enable planning and modification of distribution re-quirements based on demand and on distribution network capabilities. This shall include the ca-pability to automatically determine best delivery methods and routes based on time, cost, and ca-pacity factors; to analyze stock drawdown patterns to redirect product to demand points; and toidentify opportunities to reengineer distribution channels to enhance performance.

3. Product tracking approaches that enable continuous on-demand location of products in the distri-bution network, with the capability to locate any asset to precise GPS coordinates and automati-cally direct or redirect assets in transit while updating any resulting changes in the product distri-bution status system.

4. Modeling capabilities that enable the distribution system to automatically respond to changes indemand by initiating shipments from the optimal inventory points and reporting drawdowns tosupply node management and factory planning systems.

5. Applications to support modeling and planning for distribution and tracking of radiological mate-rials, hazardous chemicals, and other high-value/high-sensitivity products requiring special han-



dling for safety, environmental, or security reasons. Include the ability for the distribution modelto interface with regulatory requirements databases, automatically detect any changes that affectthe enterprise’s distribution strategies and mechanisms, and support analysis to develop necessarychanges.

Task 2 – Intelligent Asset/Inventory Modeling: This task shall provide modeling tools that monitorsources of inventory requirements change and aid users in defining and implementing optimal responsesto change. Specific capabilities to be developed include:

1. A generic inventory modeling application that can be readily adapted to specific industry sectorsand different supply chain roles (i.e., OEM, major subcontractor, supplier). Include the ability tointegrate products having widely varying inventory characteristics, easily add new products to themodel, and interface with distribution planning and management systems.

2. Interface solutions enabling inventory management systems to acquire and continuously updateall information that impacts inventory requirements, including direct factors such as orders, sales,and market trends, and indirect factors such as economic forecasts, weather, and governmentalactions (e.g., changes in regulations).

3. Modeling tools able to evaluate variables that impact product demand over time, and accuratelyforecast requirements for all makes, models, and styles of product. Include the capability to ex-trapolate demand trends for new products based on initial orders and sales, and the ability tomodel the demand impacts of disruptive events such as strikes, natural disasters, political up-heaval, or introduction of competing products.

4. Methods for interfacing inventory modeling tools with factory management systems to enablecalculation of factory impacts resulting from shifting production demands. Include the capabilityto interface with supply management systems to ensure just-in-time provision of raw materials,components, labor, and other assets required to fulfill product demand.


4.1 BENEFITS TO DOD

DoD will realize perhaps the greatest benefit from this project, as its supply and logistics chains are tre-mendously complex, have large consequences, and very large numbers of highly varied product. Imple-mentation of model-based distribution in the contractor community will provide the foundation for themilitary to better integrate total visibility of assets throughout the product life-cycle. Logistics supportmodels developed for individual systems will be readily integrated into a master DLA logistics supportmodel that takes into account all product attributes that ultimately impact the distribution chain. Factorssuch as MTBF and MTTR integrated into every product’s life-cycle model will enable DoD to have in-stant visibility of the impacts of increases in operational usage based on planned deployments and currentoperational realities, providing early warning of requirements for re-supply of spares, repair parts, con-sumables, and the like. This is key to realizing the potential of the autonomic logistics concept.

Modeling and simulation to support distribution planning will also be highly automated. Capacity andthroughput models at the lowest level (truck, C-130, sea container, etc.) will be linked to live transportasset status, enabling rapid direction of the right transport asset to the right location for loading of mate-riel and expedited transit to the required delivery point. Associated requirements such as the need for in-flight refueling will be calculated and scheduled automatically, greatly reducing administrative coordina-tion workload, particularly across the services. Commanders will have full on-demand visibility of assetsen route, and know with confidence exactly when the requested materiel or supplies will reach each tran-sit point.




The model-based distribution capabilities to be delivered by this project will deliver a varying range ofbenefits to U.S. manufacturers. Companies that already have highly refined and efficient distributionsystems in place will realize limited benefits since the upside potential for improvement over their exist-ing systems is small. For companies and organizations whose distribution functions are not alreadyhighly optimized (due to being highly complex, highly variable, or both), or who cannot afford the cost ofcurrent “enterprise-class” applications, the benefits will be dramatic. The ability to plan for distributionconcurrent with the development of a product design will enable planners to engineer out, on the frontend, many of the distribution problems typically encountered in new product rollout. The model-baseddistribution management system will seamlessly interface – without the cost of hardwired or custom inte-gration – with all other enterprise functions that impact it or depend on it, including manufacturing plan-ning, shipping and transportation, asset management, and product support. Distribution planning willaccommodate far more variables than currently possible, and will provide a greatly improved ability tomodel qualitative factors. Successful conclusion of this project should provide useful tools to provideflexibility to rigid supply chains. The project is also expected to deliver greatly improved capabilities forre-planning and for management of downstream life-cycle functions including product returns for recycleand disposal.


The proposed project schedule is provided below. Estimated cost for the 39-month effort is $6.8 million,which includes conduct of a prototype demonstration for each of the defined capabilities.


This project is assessed as medium risk. Although many of the desired functionalities can be demon-strated using existing tools and technologies (such as real-time asset location tracking), the ability to inte-grate the capabilities as part of a unified model-based enterprise environment is a significant challenge, asis developing tools that can deliver broad applicability for different industry sectors (e.g., consumer prod-ucts and defense) and for small as well as large manufacturers. The technology readiness level is assessedat TRL 4-5 due primarily to the significant integration that must be accomplished.




MULTI-ENTERPRISE COLLABORATION

1.0 PROJECT SUMMARY

The objective of this project is to provide the initial set of methods and standards required for seamlessinteraction of model-based processes among supply chain members. The project will conclude with ademonstration of these capabilities involving a team of industry partners in a selected manufacturingsector.

2.0 CHALLENGE

Widespread use of model-based processes at every level of the supply chain, coupled with model-basedtechnical and business management tools, will enable future manufacturers of all sizes to efficiently man-age the intricacies of designing, producing, supplying, and supporting products in a highly dynamic andcompetitive global marketplace.

Model-based technical and business processes will drive the evolution of agile supply chains that usemodel-based techniques to quickly recognize and respond to opportunities and problems. Many of to-day’s walls between prime manufacturers and their suppliers will be dissolved through model-based col-laboration. Smaller manufacturers will serve as virtual “specialty departments” simultaneously for multi-ple primes, distinguished from in-house departments only by company nameplates and management re-porting chains.

Model-based product and process definition will support seamless operation of technical and businessfunctions to the lowest level of the supply network. Prime manufacturers’ business planning systems willinterface to comprehensive capability models maintained by potential suppliers. These models will elec-tronically mirror a supplier’s products and production capability, complete with high-fidelity models ofmanufacturing processes and equipment and including performance, availability, and capacity informa-tion that is kept continuously current. This will allow designers and procurement teams to quickly evalu-ate the ability of a supplier to support prime requirements, either singly or in combination with other sup-pliers.

Model-based processes will also enable engineers, planners, and managers at all levels of the supply chainto collaborate in virtual environments where designs and approaches are optimized for the best balance ofperformance, reliability, cost, schedule, and other factors. All product and process design data and sup-porting information will reside in shared repositories accessible by authorized users from anywhere in theworld via product data management (PDM) system interfaces. This will eliminate the time, cost, andcomplexity of maintaining multiple versions of the same data at different levels of the supply chain. Itwill also ensure that every team member’s engineering, planning, and management tools operate from thesame information.

While CAD-based PDM applications are enabling prime manufacturers to integrate unitary supply chainswith individual partners, current implementations are highly tailored and require significant investmentsby supply chain members to meet the specific requirements for compatibility (e.g., for product data ex-change and cost/schedule performance reporting) with a particular prime manufacturer. Even apparentlysimple requirements can be sources of significant problems. On the Army’s Future Combat Systems(FCS) procurement, lead systems integrator Boeing specified submittal of program schedules in MicrosoftProject. Raytheon, after being selected as the integrator for the FCS ground sensors, revised the sensorprogram requirements to specify use of OpenPlan for schedules. One prospective bidder, having already



prepared its integrated master schedule (IMS) in MS Project, was forced to fly in OpenPlan experts torecreate the IMS at great expense. When the IMS files were submitted, Raytheon admitted that theydidn’t have the capability in place to use OpenPlan, and what they really wanted was an MS Project file.

The cost of maintaining different applications and systems to support different chains often precludessmaller suppliers from supporting multiple primes, and the economics of such investments force smallersuppliers into business relationships where their options are greatly narrowed. The challenge for the fu-ture is to provide ways for partners to integrate their technical and business systems, instead of re-engineering them to support specific tools and practices.


The full solution to enabling secure information sharing throughout the supply chain will require anenormous amount of effort as outlined in the goals and requirements listed in Section 3.1 below. In thisproject we propose to accomplish the initial step of developing the first set of methods and standards forsharing information using model-based processes among supply chain partners. The results will illumi-nate key issues and provide validated data for investment decisions regarding further development.

3.1 GOALS AND REQUIREMENTS FOR MULTI-ENTERPRISE INTEGRATION

• Goal 1: Model-Based Teaming – Provide a modeling framework and tools for rapidly creating newteams and supply chains to pursue business opportunities. (M)24

– Prequalification Model Framework – Develop a web-based supplier qualification model or tem-plate appropriate for any manufacturing industry type. Include data elements for process capabili-ties, capacity, current utilization and commitments, certifications, past performance, financial as-sets, and cost history. (S)

– Industry-Specific Extensions – Extend the standard supplier qualification model to support theunique requirements of specific industries, including specific factors such as tolerances, purity,turnaround time, quantity, regulatory compliance, and similar criteria. (S)

– Extended Enterprise Modeling – Develop methods and tools for modeling extended enterprisesto pursue and execute defined business opportunities, including evaluation of team member roles,values, and capabilities, to support supplier selection and teaming/partnering decisions. (M)

– Multi-Enterprise Estimating & Planning – Develop models and associated tools that supportmulti-enterprise estimating and planning for joint bids, with appropriate protection of the sensitivedata of each team member. (M)

• Goal 2: Extended Enterprise Interoperability – Provide standards and methods enabling seamlessinterconnection of model-based processes among supply chain members. (M-L)

– Common Supply Chain Language – Define and develop a standardized language or method ofsharing model-based data, methods, and procedures within and between each member of a supplychain. (M)

– Shared, Secure Models – Develop information management methods enabling all members of thesupply chain to input to, access, and manipulate shared models in accordance with appropriatepermissions, with assured security of every data element. Include the capability to provide a con-tinuous audit trail of all actions and automatically communicate changes to affected partners andpersonnel. (M)





– Extended Business Infrastructure Management – Develop modeling tools and techniques foridentifying, monitoring, and responding to internal and external forces acting on the supply chain,including the capability to predict the different impacts of an event on each member of the supplychain. (M-L)

• Goal 3: Model-Based Extended Enterprise Management – Provide frameworks and standards thatenable interoperability of model-based technical and business systems to the lowest level of the supplychain. (L)

– Enterprise Multi-Model Collaboration – Develop a methodology to interface different compa-nies’ enterprise models within the framework of an extended enterprise architecture, providingpoint-to-point connectivity of interdependent operations including requirements management,product and process design, configuration management, manufacturing planning, cost estimating,scheduling, and performance management. (M)

– Extended Factory Modeling – Develop methods, tools, and techniques for creating accurate mod-els for simulations of the extended enterprise factory and linking to current resource status infor-mation from different companies and different sites including the capability to automatically querystatus, locate extra capacity, identify and analyze constraints, and forecast requirements throughoutthe extended enterprise. (L)

– Extended Enterprise Logistics & Life-Cycle Support Modeling – Develop tools to model logis-tics requirements across the supply chain and ensure materials, equipment, and human resourcesare delivered to point of need. This will facilitate product tracking, supply, support, maintenance,repair, and return for reprocessing and recycle/reuse. Include analytical capabilities for problemsolving, tradeoff analysis, and predicting the impacts of decisions (including plans for future tech-nology insertion) at different points in the product life cycle. (L)

– Inverse/Reverse Manufacturing Modeling Tools – Develop modeling and simulation tools to aidin reverse engineering of products or components that are no longer supported by the original sup-plier (or for which the original supplier no longer exists) to support product life extension programsand manage end-of-life concerns such as reprocessing and recycling. (M)


This project will pursue the development and implementation of methods and standards to share model-based data and information in interconnected processes across an enterprise supply chain. Specific tasksare as follows.

Task 1 – Project Organization & Planning: This task shall establish a team of industry, government,and academic participants to develop the detailed plan for project execution. This activity shall select amanufacturing sector for the technology pilot and engage members at each level of a selected product-type supply chain. The objective is to select an existing supply chain with well-established working rela-tionships and a high level of technical and business process automation, including a minimum of four lev-els with multiple participants at sub-tier: prime manufacturer (one), subsystem supplier (2 to 3 partici-pants), part/component supplier (4 to 6 participants), and material or commodity supplier (5 to 10 partici-pants).

Task 2 – Methods & Standards: This task shall develop the top-level process models that define thebusiness and technical interactions across the selected supply chain. Existing applications supportingthese functions shall be mapped to the model and requirements defined to cover gaps in functionality. Foreach gap, the team shall survey available tools, select the tools that provide the best functionality fit, andwork with the tool vendors to develop the required capabilities.



Functions to be addressed include product definition (design and configuration management), cost esti-mating and reporting, scheduling and schedule performance reporting, procurement/work order placementand status tracking, change management, distribution (e.g., shipping and transport) management, andmanufacturing execution.

Task 3 – Integration Pilot: This task shall implement and demonstrate the effectiveness of the model-based supply chain integration methods developed under Task 2. The project team shall document theresults of this effort in a project final report, quantifying the resulting improvements and documentingthose areas where further development is required and barriers remain. Recommendations for new or im-proved application functionality shall be documented and shared with the providers of the tools used inthe pilot in order to guide further development.

4.0 BENEFITS & BUSINESS CASE


Management of product data and associated technical and business processes across multiple levels of asupply chain is expected to realize the greatest benefits from this project. The ability of model-basedproduct definition, data management, and planning and reporting systems to transparently exchange in-formation between different brands and types of applications will eliminate the cost and time of transfer-ring or recreating design definitions shared among different members of the supply chain. For suppliers,this will eliminate the need to support multiple CAD/PDM and analytical tools. For primes and suppliers,this will eliminate a major source of errors and reduce the time and cost of moving new products andprocesses from design to production.

Other parts of the enterprise will also benefit. Program managers and production managers will havegreatly improved ability to objectively evaluate potential suppliers for capability, capacity, and ability tomeet schedule and cost targets, eliminating a large percentage of supplier performance issues. Productand process designers and planners will have access to the accurate, in-depth information they need toplan design and manufacturing efforts with a clear, high-fidelity understanding of cost and schedule risks.

4.2 BENEFITS TO DOD

DoD programs will realize the same benefits as discussed above but, given the far greater complexity ofthe military acquisition environment, the resulting improved efficiencies in supply chain relationships areanticipated to translate to multi-billion-dollar annual savings across the DoD contractor base. Moreseamless integration of team members’ technical and business systems will directly benefit large-scaleprograms such as Joint Strike Fighter, Future Combat Systems, and DD(X), which will ultimately involvehundreds of companies in the U.S. and abroad.

For future programs, prime contractors will be able to choose suppliers based solely on performance andcost factors, knowing that any qualified supplier will be able to quickly “plug in” to the program’s engi-neering and business environment. This will also provide more accurate and timely visibility of technicalprogress and earned value, since cost and schedule status will be extracted electronically each week oreach day instead of having to be manually processed. Administrative costs for subcontract managementcould be easily halved given the ability to fully integrate reporting mechanisms.


This project is estimated to require approximately 34 months to complete at a cost of $2.7 million. Theproject schedule is provided below.




The risk for this project is assessed as high, because the integration of a large number of applications pre-sents significant technical issues in addition to overcoming the resistance of application vendors to mak-ing their products more open to compatibility with competing applications. A major challenge will bebalancing the need to accept some point solutions to bridge gaps and mitigate risks that cannot be over-come within the project timeframe using truly open, model-based solutions.

Technology readiness for model-based multi-enterprise integration is assessed at MTRL 2-3. Although anumber of supply chains – particularly in the automotive industry – have achieved a high degree of auto-mation and interoperability, successes have been highly dependent on the use of a small set of commonapplications and performing point-to-point integration of specific processes between specific members ofthe supply chain. The ability for candidate suppliers to transparently “plug and play” in a new supplychain relationship remains at the vision stage.



NGMTI PROJECT MBE-8

MODEL-BASED PRODUCT LIFE-CYCLE MANAGEMENT

1.0 PROJECT SUMMARY

The objective of this project is to provide the capability to create and apply scaleable, high-fidelity prod-uct life-cycle models that support every phase of the product lifespan and through all tiers of the supplychain.

2.0 CHALLENGE

In the NGMTI vision, from inception of product development through the end of a product’s life, all en-gineering and business activities will apply and support a central, integrated product master model or“metamodel” that contains or is linked to the original requirements and specifications for the product;analytical simulation tools for design, engineering, and business decision support; and all processes, sys-tems, and participants in the productlife cycle (Figure 2-1) through manu-facturing and product support. Forsome products with long expected lifespans, this means ensuring availabil-ity of all modeled data related to theproduct and its support for decadesafter its creation. This requires astrong focus on archival of model-based information in addressing life-cycle issues.

Despite increasing awareness of theimportance of taking a life-cycle viewin managing complex product devel-opment – from inception throughmanufacture, operation, and support –to fully embrace all aspects of a prod-uct’s life cycle, current capabilities inthis area are inadequate to meet to-day’s business challenges.

It is widely accepted that 80% of thetotal ownership cost (TOC) of aproduct is locked in by decisions inthe early stages (the first 20%) of thelife cycle. However, major TOCfactors such as maintenance, reliabil-ity, training, upgrades, and end-of-lifedisposition (e.g., recycle and disposal) receive limited visibility in the development phase, since the over-riding emphasis is on product performance and acquisition cost. While progress has been made in betteraddressing life-cycle issues in both the commercial and defense sectors, most products remain compli-cated and costly to support in the operational environment. Compounding these problems is the limitedfeedback from the operations, maintenance, training, and customer support functions to improve design

Figure 2-1. An integrated model will drive, enable,and support all phases of the product life cycle.



decision processes for better life-cycle performance, and corrective action is typically initiated only whenit is externally forced.

Tools for understanding life-cycle factors – beyond basic spreadsheets to calculate costs and factors suchas reliability – do not exist to any meaningful degree. In the defense sector, techniques such as environ-mental testing, level-of-repair analysis, reliability growth engineering, and supply support analysis helptune weapon system designs for reliability and supportability in the field. Contractor depots and productsupport organizations collect failure data and repair information and perform failure analyses to identifyneeded improvements. However, in the development phase of programs, these disciplines – along withother downstream functions such as training – are among the first to have their budgets reduced whenprograms run into schedule and budget problems.

In the commercial sector, life-cycle aspects of product design receive far less attention. Most manufac-turers – particularly automotive and electronics – address reliability aggressively in product development,but the overriding goals are typically to ensure a product will perform for its warranty period and be on aperceived quality par with its competitors. Beyond that, provision of repair services and spare parts is avaluable revenue stream, so there is little incentive to optimize for long, reliable life. Below the level ofmajor appliances, today many consumers will throw a product away and buy a new one rather than dealwith the inconvenience of trying to get it repaired.

Model-based processes can deliver dramatic improvements in all these areas. By providing the capabilityto accurately model all aspects of the product life cycle from the earliest stages of design, manufacturerscan drastically reduce costs associated with creating and distributing the product, improve operationalperformance, and improve efficiency of maintenance/repair operations and training. The model can alsobe used to harvest the information gathered about downstream performance and repair trends in order toimprove product design and manufacturing approaches.

Life-cycle costs are difficult to understand adequately due to the enormous number of variables (and de-grees of uncertainty) involved in a complex product. Different kinds of data and information that factorinto life-cycle equations are not sharable across different models, and everyone uses different models,plus some of the information needed is archived in legacy data stores. In addition, in the defense sectordifferent life-cycle functions are typically funded from different budgets, making true costs very difficultto understand.

While cost is the most tangible concern with respectto the product life cycle, there is a pressing need tobe able to optimize products for all aspects of life-cycle performance – including reliability, support-ability, maintainability, supply of consumables andspares, repair, training, and other downstreamfunctions (Figure 2-2). The challenges in these ar-eas are daunting. We lack the ability to modelmany life-cycle factors in their operational contextwith mathematical rigor and completeness. Infor-mation about life-cycle requirements is not readilytraceable back to fundamental data, and we don’tcapture knowledge in readily usable/reusable forms. This is especially true of information captured inlegacy systems. We also lack the ability to quantify many life-cycle factors with sufficient rigor and cer-tainty to support accurate decision processes.

Current development in this area is focused largely on CAD-based product life-cycle management (PLM)tools and refinement of available models. Reliability, perhaps the most mature example, is currentlymodeled by adding together the cumulative reliabilities of all of the constituent parts and subsystems of aproduct to calculate factors such as mean time between failures (MTBF). This yields useful results for

Figure 2-2. The product model must support everyfunction across every phase of the life cycle.



planning purposes, but represents only a best-guess approximation based on engineering judgment andknown history of life-limiting components or materials. When the reality turns out to be very different,the impacts on operational performance and support costs can be huge. The AH-64 attack helicopter, forexample, was originally designed (as were many current front-line DoD systems) as a tank-killing plat-form to operate in the cold and wet weather of European theater. The different climate of the MiddleEast, where blowing sand, dust, and extreme heat are a constant threat to mechanical systems, has dra-matically impacted the operational availability of the AH-64 fleet and its major systems and has stressedmaintenance and repair resources to their limits.

Modeling and simulation of maintenance functions is one area where excellent progress is being made,improving the ability to design in life-cycle robustness and affordability from early stages in the productdesign phase. “Ergo man” and design for assembly (DFA) tools enable aircraft designers to simulatesupport functions such as stores loading and removal of parts for servicing, greatly improving support-ability aspects of the system design. Also, emergence of the “system-of-systems” concept has contributedsignificantly to improving the process of engineering products in ways that complement all of the otherproducts and systems with which a product must interoperate in its operational environment. This con-cept must be fully extended to the life-cycle support context.


The goal of this project is to develop the capabilities required to provide a single product master modelthat is linked to analytical simulation tools for design, systems engineering, and decision support; and toall processes, systems, and functions in the product life cycle.

The key to this concept is providing the ability to:

• Link all elements of the integrated product life-cycle model to accurate, complete, and current data,and maintain these linkages over the life of the product in order to continuously enrich the model andkeep it current.

• Accurately bound risk and uncertainty for each element (or sub-model) of the model, and propagateassociated variability across interrelated factors.

• Create models that are scaleable in terms of complexity and level of detail; extendibility over time;and extendibility across product/product family types.

3.1 GOALS AND REQUIREMENTS FOR MODEL-BASED PRODUCT LIFE-CYCLE MANAGEMENT

This project responds to three major goals defined in the NGMTI Roadmap for the Model-Based Enter-prise, as follows.

• Goal 1: Model-Driven Support Over Full Life Cycle – Provide the capability to manage all productlife-cycle support activities using model-based processes, by extending the product model includingtraceability through all configurations, adding life-cycle simulations, and capturing and applying in-formation from the product’s operational environment. (L)25

– Robust Requirements Modeling Tools – Develop modeling tools that integrate the entire chain oflife-cycle events for a product or process, including environmental, safety, health, and other regula-tory requirements. (M)

– Integrated Life-Cycle Modeling Capability – Develop integrated, plug-and-play tool sets andstandard database structures for modeling and simulation of all life-cycle factors for generic producttypes (e.g., mechanical, electrical, chemical). Include the capability for accurate modeling of all





product design factors relevant to product support, including reliability, availability, maintainability,and supportability, to optimize product designs for performance, cost-effectiveness, and customervalue. (M-L)

– Integration of Legacy Data – Provide the capability to integrate archived information from legacyapplications and databases into the current life-cycle model framework, and demonstrate the capa-bility for a selected product family that has long life-cycle use and support characteristics. (M)

– Integral Product Monitoring – Develop packaging and transportation systems and onboard sensorsthat monitor products from point of origin and record and report environmental conditions and han-dling history to enrich the product’s life-cycle knowledge base. (M)

– Modeling Tools for Systems-Based Life-Cycle Planning – Develop and pilot modeling capabilitiessupporting requirements definition, problem-solving, tradeoff analysis, and prediction of decisionimpacts anywhere in the product life cycle (including future technology insertions) in the context ofthe environment in which the product will operate. Include tools enabling product models to monitordata gathered concerning operational use and predict product condition at end of life, to support de-sign decisions about refurbishment, recycle, and disposal. (L)

– Model Linkages to MRO Management Systems – For a given set of products to be supported, ex-tend and integrate current design, manufacturing, PDM systems, and maintenance/repair operations(MRO) management systems to support forecasting to plan for expected repair operations, prioriti-zation of resources, conduct of work, resupply/reorder of spares and consumables, and similar MROfunctions. (M)

– Product-Driven Support Schedules – Develop modeling tools to analyze a specific product andquickly and accurately generate the projected need for repair and spare parts and optimal mainte-nance schedules based on the product design and its deployment schedule. (S-M)

– Technology Impact Forecasting – Develop the means to link knowledge and projections about fu-ture technology progressions (e.g., faster processors, new materials) to optimize a product design forits intended useful life, including technology refresh or product phaseout. (M)

• Goal 2: Life-Cycle Model Feedback to Design & Planning – Provide the ability to acquire and usecaptured information from users and maintenance/repair and final disposition operations to 1) enrichthe fidelity and depth of product life-cycle models, and 2) feed back and enhance the process and prod-uct design function. (M-L)

– Life-Cycle Model Connectivity to Operational Data – Identify and develop means for capturing,verifying, and delivering needed live data (including cumulative life-cycle history such as perform-ance over time and repair trends and spares demands) for different types and families of productsback to the enterprise and product models, enabling life-cycle system models and system-of-systemsmodels to be continuously updated to enhance their fidelity and value. Include capability to minedata for products with service problems, searching development and manufacturing data for off-normal conditions that might be associated and alerting management if sensitive associations arefound (e.g., concerning potential liability issues). (L)

– Life-Cycle Performance Feedback Tools – Develop tools and methods to automatically capturelife-cycle performance data (e.g., actual reliability and repair turnaround times) from the enterprise’sproduct support systems and update the master product knowledge base. (S-M)

– Model Database Interfaces to Life-Cycle Feedback – Establish formal interfaces with specificmanufacturer and customer databases enabling product models to link to actual life-cycle informa-tion such as spares and consumables drawdowns, frequency of maintenance and repair actions, fieldmodifications, and user feedback on performance and problems. (M)



– Real-Time Access to Maintenance Data – Develop the capability to capture and use real-timefeedback from maintenance activities in predictive maintenance/support models to improve planningand management of maintenance/repair operations, including supply logistics. (M)

• Goal 3: Model-Based Training – Provide the capability to use product and process models as the ba-sis for all training activities across the product life-cycle. Provide model-based training tools that: 1)are developed along with the product or process, 2) support different kinds of training for differentkinds of products and processes, 3) are available for training prior to release and use of the prod-uct/process, and 4 are automatically adaptable for all types and levels of user. (S-M)

– Model-Based Training Requirements Definition – Define levels and types of training needs (in-cluding both formal training and real-time job support for operation, maintenance, and product sup-port) for different classes of products, in cooperation with training community stakeholders (includ-ing universities). (S)

– Model-Based Embedded Training Concepts – Develop model-based embedded training conceptsand approaches for different classes of products and processes in collaboration with indus-try/government user communities, academia, and training technology vendors and service providers.(S-M)

– Embedded Training Pilots – Develop and demonstrate model-based embedded training technolo-gies and applications for selected products, for use by support/maintenance staff and custom-ers/users. (M)


Under the proposed project, a team of commercial and government-funded participants will develop, pi-lot, and demonstrate the benefits of scaleable life-cycle model-based technologies and associated life-cycle support processes. The project comprises four major tasks as follows.

Task 1 – Product Life-Cycle Model-Based Framework Concept: This task shall define and baseline atechnical framework for model-based product life-cycle management. The project team shall:

1. Conduct research and develop case studies to define and characterize life-cycle requirements fordifferent types and classes of products, and quantify the benefits of life-cycle modeling andsimulation applications to document a detailed business case for investment.

2. Define information needs for each life-cycle phase, process, and stakeholder, and develop logicalinformation models for an initial set of product types.

3. Identify available models and software tools, and define tool integration requirements and anenabling framework to support the Task 2 demonstration activity. This effort shall include a gapanalysis to identify required tool modifications/extensions, and disseminate requirements to thetool vendor community.

Task 2 – Phase I Demonstrations: This task shall pilot the model-based product life-cycle frameworkand enabling tools on a set of selected product types of interest to both DoD and commercial industry.Working with the product supply chain members and modeling and simulation tool vendors, develop anddemonstrate integrated life-cycle models for the selected product types. Key factors to be addressed in-clude life-cycle cost and supportability.

Task 3 – Phase I Framework Validation: This task shall validate the baseline product life-cycleframework through operational evaluation on a selected product with members of the product’s supplychain. The project team shall:



1. Develop a generic product-type life-cycle model and tailor it to the selected product example andits operational life-cycle processes (e.g., provision of spare parts and consumables, maintenanceand repair, recycle/disposal).

2. Disseminate the generic model to all members of the product supply chain and interested organi-zations for independent evaluation, and solicit feedback on utility, deficiencies, and considera-tions to optimize the model for use in business processes.

3. Conduct specific tests to exercise functionality, such as compatibility with analytical tools andability to automatically predict the “cascade” effects of changes on both small and large scales.

4. Based on the feedback of the supply chain members, enhance and extend the pilot model frame-work and identify required technology advances.

Task 4 – Phase II Development & Demonstration: This task shall conduct follow-on demonstrationsof improved and extended functionality and capabilities, targeting the gaps remaining from the Phase Iactivity and applying available model-based tools to a complex product as opposed to the simple productaddressed in Phase I. The project team shall:

1. Develop a generic life-cycle model for the complex product type and tailor it to the selectedproduct example and its life-cycle processes.

2. Work with the vendor community to extend the capabilities and functionality of the life-cyclemodel-based toolset to support the complex product case.

3. Work with product supply chain members to implement the technologies in wide-scale demon-strations and evaluations, enhance and extend the pilot framework, and identify required technol-ogy advances.

After conclusion of the project, it is expected that broad-based technology development and maturationwill continue with focused R&D projects to attack key technology gaps identified in the Phase I andPhase II demonstration activities. These requirements are anticipated to include the following:

1. Work with the model-based tools vendor community to extend the capabilities and functionalityof the life-cycle toolset in areas such as ability to accept real-time data feeds, plug-and-playinteroperability of different applications and model types, and data quality assurance.

2. Advance capabilities to capture and apply feedback from maintenance activities in predictivemodels to improve planning and management of maintenance/repair operations (including supplylogistics) and improve original product designs.

3. Develop technologies to monitor products from point of origin to point of use and report envi-ronmental conditions and usage/handling history to enrich the product’s life-cycle knowledgebase.

4. Work with the vendor community to promote needed standards, conduct demonstrations of tech-nology advances, and commercialize new tools that support integrated life-cycle modeling.


4.1 BENEFITS TO DOD

Use of integrated life-cycle modeling in all phases of the DoD product life cycle is key to improving costsand efficiency for maintenance, repair, training, supply, technology refresh, and other sustainment func-tions. With a global theater of operations involving multiple major force deployments (e.g., Iraq, Af-ghanistan, and South Korea) expected to continue for many years, improved ability to model and under-stand life-cycle support requirements at the systems and system-of-systems levels will help DoD deci-sion-makers anticipate maintenance and support bottlenecks and improve operational availability (Ao).



For weapon systems now in the development pipeline, such as the F-35 and the Future Combat Systems“fleet”, the capabilities delivered by this project will greatly enhance the ability of DoD program manag-ers and contractor teams to optimize evolving the system designs for reliability, maintainability, support-ability, training, and seamlessness of future block and spiral upgrades. Improved ability to model re-quirements, workload, and work processes for restoration of signature-control features after maintenanceand repair, for example, will give designers greater insight into optimizing these kinds of problematicprocesses. This will reduce the cost and time of reapplying treatments, enabling faster combat turns whileimproving abilities to protect signature integrity.

Systems in production or early in production, like the F/A-18E/F and AH-1S aircraft and the F-16 Ad-vanced Targeting Pod, will benefit in terms of improved ability to optimize life-cycle performance basedon feedback from the field in the early years of operational deployment. Systems well into their servicelife – such as the F-16, F-15, A-10, C-130/AC-130, and B-52 aircraft and ground systems such as theHMMWV, M-1 Abrams, and Patriot missile system – will benefit by improved abilities to design andimplement system upgrades and life extensions that deliver maximum value to the warfighter while re-ducing the associated impacts on support infrastructures.


Developing the ability to create and use product life-cycle models will yield the following benefits:

• Provide a toolset for modeling and understanding life-cycle cost and supportability impacts.

• Enable feedback from downstream experience to improve upstream functions.

• Improve the speed and accuracy of technical and business decisions over the life cycle.

• Improve the ability to analyze field information on “as-worn” parts to predict failures and improvedesigns.

Providing the capability to create and apply scaleable product life-cycle models will fundamentallychange the way we develop, produce, and support manufactured products. Much of the data needed todrive these processes will be openly shared, changing the basis of competition from one of “protectedknowledge” to one of proven capability and genuine best value. Other key benefits include:

• Higher fidelity of life-cycle cost and performance drivers early in the design process, enablingstronger focus on optimizing affordability, reliability, availability, supportability, and similar at-tributes.

• Ability to capture knowledge from manufacturing and repair/maintenance shop floors for use in allphases of life-cycle support, including product improvements, technology insertions, and futuredevelopment of similar products.

• Deeper integration and understanding across the supply chain, which is particularly critical asprime manufacturers continue to evolve to larger roles as system integrators.

• Ability to create shared life-cycle knowledge bases including validated design models, cost models,and process models that are plug-and-play compatible.


The proposed schedule for the 66-month project is provided below; cost is estimated at $13.5 million.




Risk for this project is assessed as moderate to high. Many of the capabilities desired can be implementedthrough extension of current tools to the extent needed to demonstrate clear value, but integration of allthe tools and incorporating feedback from real experience to modify the models will be more difficult.The interoperability issues raised by integrating archived legacy information also increases the risk.

Technology readiness is assessed at TRL 3. Although many of the technologies required are mature intheir current implementations, the ability to fully integrate model-based tools and processes to provide acomplete life-cycle capability remains an experimental concept.



NGMTI PROJECT MBE-9

MODEL-BASED, REAL-TIME FACTORY OPERATIONS

1.0 PROJECT SUMMARY

The objective of this project is to develop enabling technologies and conduct a proof-of-principle demon-stration of real-time, model-based control of factory operations, including production and maintenanceoperations as well as active interfaces with asset, inventory, and facility management systems. The pro-ject is focused on providing the models that establish the necessary operations control functions, and inte-grating these models with material, product, process, and control models to deliver an operational proto-type system.

2.0 CHALLENGE

Automation of processes and factory management is commonplace in the U.S. manufacturing industry,and has been key to improving productivity, quality, efficiency, and economic competitiveness over thepast 10 years in all sectors of manufacturing. Real-time control is a well-established capability in thecontinuous process industries (e.g., chemicals, pharmaceuticals), where the ability to monitor, sense, andadjust to observed conditions is critical to ensuring highly precise product quality. However, such capa-bilities are presently limited to unit process lines where key parameters such as feeds, speeds, tempera-tures, pressures, and dwell times can be closely monitored and controlled in accordance with well-developed chemical transformation process models that have been proven at laboratory, bench, and pilotscales. A similar situation exists in the discrete product sectors (e.g., automotive), although sensing andcontrol is more typically limited to manufacturing cells and individual units of equipment.

As a generalization, the most highly automated and controlled factory operations are those that have ex-tremely well-defined and non-varying processes producing extremely well-defined and non-varying prod-ucts. Process upsets can have problematic or occasionally catastrophic impacts, and product changeoversor significant changes in production requirements can take operations or facilities off line for days, weeks,or even months. Leading companies such as Procter & Gamble make extensive use of model-based tech-nologies to engineer out potential problems up front, optimizing systems and equipment for reliability andproductivity.

In the NGMTI vision, future factories will be highly autonomic entities that monitor their resources, as-sets, and activities and rely on robust models and high-performance simulations to continually tune allfactors for optimal performance – not only in steady-state operation, but in response to changes in re-quirements and to fluctuations in the performance and availability of every asset and function. Model-based, real-time executive control over factory operations will not only tighten control of both productionand support functions but will also offer substantial savings through reduced manpower, lower materialand spare parts inventories, and improved processes.

Models are required for both planning and control of factory operations. Discrete event models have beenavailable for more than a decade for simulation of processes and material flow, but significant work isrequired to provide control over the myriad of details associated with storage and movement of material,tools, and fixtures, as well as delivery of instructions to humans. These models must also incorporatematerials and other resources delivered through the factory’s external supply chains.



Omniscience, at least with respect to factory control, is the state sought to realize true model-based fac-tory control. Real-time information describing all facets of factory operation must be provided to the ex-ecutive models. These models must also be equipped with the logic required to generate decisions opti-mized over critical production variables. Significant challenges exist in both the scope of the project andin the difficulty of individual components. Proprietary enterprise resource planning (ERP) tools alreadyprovide many of the planning and work direction functions associated with factory control. They cur-rently lack integration with product and process models. Higher-level tools associated with scheduling,routing, etc. must be added. These tools must use the product and process models and apply valid logic tospecify the paths of material flow, process sequence, and specific equipment to be used as well as managethe many other details associated with factory control.

A significant effort must be invested in creating and/or integrating information-gathering and reportingtools if the administrative systems are to work as needed. If accurate factory simulation is to be realized,the underlying models must be continually updated with accurate information. Shop-level tools such asreactive schedulers may be employed to enable optimization in the presence of local upsets in availabilityof machines, tools, programs, fixtures, or materials. Such local tools must also draw from real-time in-formation and must provide updates to the factory-level simulation and control systems.

The executive-level systems must also control the distribution of the full array of information associatedwith factory control. Although much of the control programs, operator instructions, and quality require-ments will be stored near their applications, the timely availability of the right information is the respon-sibility of the overall control system.

Quality maintenance must also be addressed. In the long term, “zero defect” and “N Sigma” productquality will be assured by real-time control of critical process parameters. In the near term – certainlywithin the duration of this project – some post-process product inspection will be required, and such in-formation must be generated as with any other stage of operations. Quality analysis systems must there-fore be included among the required models.

Equipment and facility maintenance must also be addressed, as these functions occupy a significant frac-tion of the cost of factory operations. Over the past 20 years, these functions have seen major develop-ments in equipment condition monitoring and work planning and estimating, but models and new strate-



gies remain to be developed to make serious advances in avoidance of failures and in time spent on diag-nosis and repair. For example, a substantial effort is required to capture information related to failures,symptoms, repairs, and equipment history. The maintenance models of the future will capture this infor-mation automatically because the local controllers will routinely supply data on wear, variability, andother health indicators to higher-level monitoring systems that track trends in equipment performance andfunction. Again, the necessity of working with legacy equipment imposes constraints. Thus, mainte-nance and reliability functions, which are often overlooked in higher-level planning functions, will berepresented by models that contribute significantly to the efficient utilization of factory assets and to thesuccess of real-time factory operations.


Many of the needed capabilities defined under the Goals and Requirements given below are medium- tolong-range objectives. The proposed project takes a prototyping approach in order to demonstrate thevalue of model-based operations control while beginning to deliver tangible benefits to industry. A num-ber of components – both within this project and external to it – will not be available during the projecttimeframe, so a number of “placeholders” will be required.

After the project team is established, the technical effort will begin with selection of the target manufac-turing environment and creation of a functional map of operations components that describes all the re-quired models and their relationships. Some components, such as scheduling, ERP, quality, and mainte-nance systems, already provide useful capabilities and will be tailored to function in the model-based en-vironment.

Both high- and low-level models must be created or modified for this project.26 Some specific lower-level models will be required for process/machine control along with material, tooling, and fixturingmodels. Process/machine control units can be built in the near term that couple real-time sensing of proc-ess information for local control with the status reporting and self-diagnosis features required for high-level operations management. All of these control models must connect automatically to the factory net-work in plug-and-play fashion. As the equipment controllers become more sophisticated, the detailedprograms will be generated within the controllers rather than by higher-level engineering functions.Controls vendors already supply some machine parameters for high-level simulation and control of ma-chine functions. However, a considerable effort will have to be invested in legacy equipment and net-works to realize similar capability over the entire enterprise.

3.1 GOALS AND REQUIREMENTS FOR MODEL-BASED, REAL-TIME FACTORY OPERATIONS

This project addresses the following goals and requirements found in the NGMTI Roadmap for theModel-Based Enterprise, Part 3: Resource Management.

• Goal 1: Product Model-Driven Manufacturing – Provide the capability to execute manufacturingoperations directly from the product model. (L)27

– Executable Product Models – Develop the capability to integrate process knowledge into productmodels sufficient for the product model to provide all information necessary to execute a manufac-turing process, including material routing/flow, actuator commands, assembly steps, and qualitymonitoring. (M)

26 Some specific material, product, and process models will be developed under separate projects (e.g., Product Driven Product and Process

Design), and are not included in the scope of this project. Placeholder models may be required in place of these models in the early stages ofthe project.

27 The S-M-L designations identify a nominal timeframe for delivery of the specified capability, where S (Short) = 0 to 3 years,M (Medium) = 3 to 5 years, and L (Long) = 5 to 10 years.



– Automated Verification of Production Readiness – Develop the capability for product models toverify that all information required to execute manufacturing is complete and accurate based onknowledge available from the enterprise’s manufacturing information system. Include the capabilityto flag areas where information is missing or where the model cannot verify that information is cor-rect (e.g., that a special part or fixture on order will deliver on time). (M-L)

– Complete Process Control Models – Develop techniques to incorporate process effector and sensordesign information and product-specific parameter data into a complete process model, to enable theintegrated product/process model to operate as a real-time process controller at the unit process, line,and shop floor levels. (M)

– Model-Based Direct Manufacturing – Develop transformation processes that synthesize and manu-facture materials to produce final parts directly from the product model, based on a scientific under-standing of materials, process interactions, and performance criteria. (L)

• Goal 2: Comprehensive Material Flow Modeling – Provide tools to develop and manage enterprise-wide material flow models to support planning and execution of complex manufacturing operations.(M-L)

– Material Flow Modeling Capability – Develop applications for creation of material and productflow models that enable plug-and-play integration of all flow models for a product down to the low-est tier of the supply chain, and enable material flows to be optimized both locally and supply-chainwide. (S)

– Real-Time Material Flow Modeling Capability – Provide links to enterprise information systemsto support rapid re-optimization of material flow models when factors change. (M-L)

– Outgoing Material Characterization – Establish standards and requirements for material suppliersto provide a complete and computer-sensible material characterization as a deliverable item witheach lot of material (e.g., for bar and sheet stock, chemical formulations, raw materials, and manu-factured commodity items such as fasteners and bulk electronic components). (M)

– Incoming Material Disposition – Develop supplier material disposition models that automaticallydirect the appropriate action (e.g., route to production with special processing instructions) based onincoming lot characterization results. (M)

– Material Variability Management – Develop material monitoring and flow management controlmodels that interface with receiving inspection, manufacturing process sensors, and the enterprisematerials knowledge base to monitor the state of incoming/in-process materials and direct adjust-ment of processing parameters to accommodate for material variability. Include the capability topredict the impacts (e.g., cost, schedule, quality) of material disposition options. (L)

• Goal 3: Self-Configuring Manufacturing Execution Models – Provide self-organizing manufactur-ing execution models able to integrate all applications, systems, equipment, and process instructions toensure readiness to satisfy all requirements for producing correct product, and which have the capa-bility to automatically adapt to changes in requirements. (L)

– Manufacturing Planning Model Templates – Develop a series of model-based templates, for ma-jor classes of products in different sectors, that can integrate “sub-models” of processing equipment,unit processes, line operations, and material flows to create an end-to-end model of a given manu-facturing process. (S)




– Equipment Characterization Models – Establish standards and requirements for integration of per-formance characterizations into existing or vendor-supplied models and simulations of processequipment (machine tools, valves, process sensors, material handling devices, etc.). (M)

– Machine-Specific Equipment Models – Develop tools to extend generic or vendor-supplied equip-ment performance models to reflect the as-installed configuration and use real-time sensor informa-tion to accurately represent specific equipment system performance. Include the capability to cap-ture the baseline signatures for each production machine in its supporting model. (M-L)

– Intelligent Manufacturing Execution Models – Develop methods to automatically update manu-facturing execution models by recognizing and responding to approved changes in underlying mate-rial/process/product models, or in response to direction from the shop floor control system. (L)

• Goal 4: Flexible, Reconfigurable Manufacturing Facility Modeling – Develop technologies andmethods for creating reconfigurable production lines able to use capacity, demand, and unit processmodels to quickly adapt to changing product and process requirements. (M-L)

– Manufacturing Facility Modeling System – Develop generic manufacturing facility modelingsystems for different industry sectors (e.g., mechanical, electrical/electronic, chemical) with the ca-pability to “plug in” unit process and equipment models and systematically develop and refine thegeneric model into a facility-specific model. (M-L)

– Scaleable Process Models – Prioritize unit processes and manufacturing equipment of interest anddevelop scaleable process models capable of going from one to many, or from small to large, or ac-commodate a defined wide range of input material variability, quickly and autonomously in responseto changes in production demand. (M)

– Robust Model-Based Control – Develop extensions to current manufacturing process control mod-els to add the capability to adapt dynamically to changes in basic process parameters, to remain reli-able and robust within the defined operating envelope, and to automatically respond to failures orprocess upsets with the appropriate action. (M)

• Goal 5: Operations Element Modeling – Provide the capability to create accurate models and simu-lations of manufacturing operations for an entire facility, and to integrate and modify constituent mod-els to mirror the real-world facility and all of its assets and processes. (M)

– Operations Modeling Framework – Develop standards and conventions for creating high-fidelitymodels and simulations of manufacturing operations, including equipment, tools, fixtures, unit proc-esses, facility attributes (structure, utilities, etc.), material flows and transport systems, work flows,monitoring and control functions, safety systems, and other attributes of interest. (M)

– Generic Integratable Process Models – Develop generic models for different kinds of processesand facilities associated with manufacturing operations in different business sectors. Include inter-face definitions and hooks that enable integration of equipment models, unit process models, and fa-cility models into higher-level operations models with accurate linking of inputs and outputs betweenand among each element of the system. (M)

• Goal 6: Model-Based Operations Control – Provide the capability to integrate model-based controlfunctions for equipment and unit processes to enable model-based control at the shop floor and factorylevels. (L)

– Process Control Linkage – Develop methods for linking individual equipment and process per-formance monitoring and control functions to facility operations models to support model-based



control and optimization of operations performance. Include the capability to monitor external fac-tors that affect operational performance, such as supplier production schedules. (M)

– Performance Reporting Modules – Develop generic reporting modules that can be plugged intooperations models to deliver defined performance status information sets for different types andclasses of processes and equipment. Include the capability to deliver predefined reports, respond tospecific queries, and calculate the impacts of simulated changes in operating parameters. (M)

– Equipment & Material Status – Develop equipment/material status systems that interface withother enterprise planning and management systems to continuously update operations models withequipment and material resource availability and utilization information. Include the ability to pre-dict the impact of running equipment at 100% capacity/utilization for sustained periods. (M-L)

– Model-Based Performance Management – Develop model-based tools and methods to monitorand evaluate the performance of factory operations including resource staging and application, mate-rial and work-in-process flows, and off-line activities. Include the ability to simulate problems andchanges in selected functions to support troubleshooting, tradeoffs and optimization, and planning tomeet new requirements. (L)

• Goal 7: Dynamic Asset Modeling – Provide asset management systems that integrate high-fidelitymodels to manage capital equipment and facilities across their useful life and support evaluation ofappropriate responses for addition, modification, replacement, and retirement of equipment and facili-ties. (L)

– Asset Definition Modeling Standards – Develop standards for creation of object and life-cyclemodels for different types and classes of capital equipment and facilities that support integration intobusiness and facility planning models. Provide the capability to include key factors such as capacity(throughput, sizing, tolerances, etc.), life expectancy and life-limiting factors, and growth capabilityto support higher performance levels, expanded functionality, or changeover to support new re-quirements. (S)

– Automated Asset Monitoring & Condition Prediction – Develop techniques for predicting the lifeexpectancy of capital assets based on feedback from sensing systems that monitor wear, frequency ofmaintenance/repair, and changes in performance over time. Include the capability to simulate the ef-fects of stressing conditions (e.g., extended operation at limits of capacity) to support contingencyplanning. (M-L)

– Asset Alternative Modeling – Develop methods to integrate asset information and knowledgeacross the enterprise, including its suppliers and partners, to enable rapid evaluation of solution op-tions for fulfilling a capital asset requirement. Include the capability to define the margins of capa-bility and financial impacts for each option for a given time span, including factors such as availabil-ity of capital funds, return on capital, funds flow, and ability to meet surges in demand. (L)

– Regulatory Impact Modeling – Develop techniques for modeling the impact of changes in regula-tory requirements on capital equipment and facilities. Include the capability to automatically moni-tor information sources for issues relative to process and facility emissions (air and water discharges,noise), safety standards, and similar factors that potentially dictate modification, replacement, orshutdown of capital assets. (L)

• Goal 8: Intelligent Inventory Modeling – Provide modeling tools that monitor sources of inventoryrequirements change and aid users in defining and implementing optimal responses to change. (L)

– Adaptive Inventory Modeling Applications – Develop generic inventory modeling applicationsthat can be readily adapted to specific industry sectors and different supply chain roles (i.e., OEM,major subcontractor, supplier). Include the ability to integrate products having widely varying in-



ventory characteristics, easily add new products to the model, and interface with distribution plan-ning and management systems. (M)



– Automated Demand Prediction – Develop modeling tools able to evaluate variables that impactproduct demand over time, and accurately forecast inventory requirements for all makes, models,and styles of product. Include the capability to extrapolate demand trends for new product introduc-tions based on initial orders and sales, and the ability to model the demand impacts of disruptiveevents such as strikes, natural disasters, political upheaval, or introduction of competing products.(L)


A manufacturing sector will be chosen that represents a significant element of the U.S. manufacturingbase. The effort will begin with selection of the target manufacturing environment and specification ofthe multiple system layers required for the factory control architecture ranging from individual machinemonitoring and control to the executive control level. This stage will require close coordination with thesources that will be providing models for use in this project.

Task 1 – Project Planning: This task shall establish the project management and technical team, selectthe manufacturing environment to be addressed, select one or more testbed sites, and establish the de-tailed project plan with task assignments.

Task 2 – Requirements Definition: This task shall create a functional map of all components and rela-tionships in the target manufacturing environment, identify required models, and define integration re-quirements at each level of operation (i.e., equipment, unit process, line, and factory). Standards for thevarious types of models required shall be defined, making maximum use of existing standards. Thedocumented requirements shall be circulated to team participants and interested industry and governmentstakeholders for review, comment, and finalization prior to start of the development tasks.

Task 3 – System Development: This task shall create, test, and refine the required models. Althoughthe models are prototypes, they shall be fully functional for the planned demonstration. This task alsoincludes the parallel effort of establishing standards for the model structures that will be extended to ap-plications and industry sectors beyond the bounds defined for the project’s demonstration activities.

Task 4 – System Demonstration: This task shall demonstrate successful functionality of model-basedfactory operations in an industry setting. Because of the importance of the demonstration, system testingwill begin before some of the models are complete; design of the demonstration will likewise begin earlyin the project.28 Expectations for the demonstration include:

• Successful performance of all models

• Seamless integration of all models such that no human intervention is required

28 Many problems associated with seemingly minor details in individual models (data types, dependencies, lack of data, etc.) only become evi-

dent when forced to function in the full system. Logic that appears to produce credible results in lower-level testing may produce faulty resultswhen operating in the full factory control system.



• All information relevant to factory control is delivered automatically to the point of use (machinesand/or human operators)

• All machine status and condition information is acquired and communicated as designed

• Information links to ERP and other external systems operate seamlessly in real time

• The factory control system accesses all information and knowledge sources and directs targetedfactory operations as designed

• Quality information is collected, analyzed, reported, and made a part of the product and processknowledge base.

The demonstration task will conclude with delivery of a project final report that documents the capabili-ties delivered and requirements for further development. All software will be documented and madeavailable as a deliverable.


4.1 BENEFITS TO DOD

This project will not benefit any specific DoD weapon system since its focus is on the much lower levelof basic manufacturing operations management; however, all of the military services will benefit fromhaving a more efficient, responsive, flexible, and capable manufacturing base. One key benefit to DoDwill be the greatly improved ability to optimize manufacturing performance early in the production tran-sition process, reducing the time required to qualify production capability for a new weapon system, sen-sor system, or other item of military hardware. The greater intelligence and automation afforded bymodel-based operation control is expected to enable a better than 10% reduction in manufacturing touchlabor and supervisory workload, with complex, large-scope programs such as Joint Strike Fighter realiz-ing the greatest benefit.

The factory system also will be able to capture a far richer and deeper base of information about its proc-esses and equipment, which will reduce the time and cost of uncovering and analyzing manufacturingproblems and getting production back on track. This is particularly important at the major subsystemsupplier level, where manufacturing problems experienced by multiple suppliers can have severe impacton the prime contractor responsible for final system integration and test.

The capabilities delivered by this project will also greatly enhance the ability of the supply base to re-spond to surge and mobilization requirements, by shortening the timelines required to ramp up productionof product improvements and special-purpose variants, re-start “cold” or “warm” lines, or change over toproducts experiencing spikes in demand due to unanticipated inventory draw downs. This would mini-mize the challenge of feeding DoD spares pipelines for items such as batteries and repair parts, which hasbeen a persistent issue in Iraq and Afghanistan.


The primary benefit derived from this project is the demonstrated functionality of model-based real-timeoperations in a realistic, representative manufacturing environment. This will be a significant step for-ward in real-time factory operations control. The system and its constituent technologies (models, controlsoftware, etc.) should be usable elsewhere immediately within the assumptions made in developing theprototype system. Second, the specification of functional map and the definition of system componentmodels, together with the methodology used in this stage, will facilitate implementation of similar capa-bilities for other types of manufacturing. The models themselves and the integrating architecture can bereplicated, refined, and/or modified for use in similar or other sectors. The availability of these provenmodels will shorten substantially the effort required to bring similar functionality to use in other manu-facturing enterprises.



Performance in all areas of factory operations will be radically improved. The ability to continuouslymonitor factory status and health and model the impact of planned or potential changes will greatly re-duce the time required to respond to new requirements, including large-scale changeovers as well as rou-tine shifts in product quantities, styles, etc. Improved control will also reduce all types of inefficiency andwaste, including scrap, rework, and spoilage, and enhance safety through close monitoring and control ofhazards throughout the factory.

Other key benefits expected from this project include:

• Accurate information will be delivered reliably and on time to all functional components of thefactory. Success in these communications should significantly reduce current levels of staffing as-sociated with this function while improving the quality of the information.

• Lower-level control components will not only control their equipment, but also automatically gatherand report status and performance information. Timely delivery of control programs and instruc-tions will reduce the human requirement for such tasks. Availability of equipment status data willimprove the ability to anticipate equipment failures and shorten timelines for diagnosis and repair.

• The real-time knowledge of the availability of all equipment provides greater flexibility in assigningresources to address production requirements. Accurate simulations of the factory operations willenable optimized use of facilities and equipment and accurate, precise forecasting of upcoming re-quirements.

• Direction of maintenance activities will benefit from the continuous availability of equipment statusinformation as well as from accurate equipment history and knowledge of production requirements.Thus, appropriate preventive maintenance, repairs, upgrades, etc. can be scheduled and conductedwith minimal impact on production operations. Major reductions in maintenance costs are expectedthrough labor reductions associated with both maintenance planning and maintenance repair. Somereduction in cost of spares should also result.

• Automatically gathered quality information permits automatic analysis and documentation as wellas rapid recognition for process anomalies and human intervention. This information will also beused in the product/process design models as well as in cost estimating and other business areas.


The proposed project schedule and estimated resource requirements are provided below. The estimatedcost for the 36-month effort is $4.0 million, which includes the required development and conduct of onemajor prototype demonstration.




The risk associated with this project is considered medium. Some of the functionality needed in the pro-ject has already been demonstrated in proprietary software. Demonstrations of high-level direction overfactory operations already exist but are not model-based and cannot be easily re-mapped to other envi-ronments or industry sectors. Some difficulties associated with seamlessly interfacing with a wide varietyof other models will be encountered but these difficulties should be manageable within the project period.An overall MTRL of 5 is appropriate for this project.



NGMTI PROJECT MBE-2

SHARED MODEL LIBRARIES

1.0 PROJECT SUMMARY

The objective of this project is to establish a necessary, common, and robust framework for managingrepositories of collaborative models that, when assembled, can accurately simulate materials, products,and enterprise functions across different industry sectors. This will enable significant reductions in thetime and cost of translating product concepts to delivered products, and enable accurate prediction of theimpacts of engineering and business decision. In its final phase, the project will establish an initial libraryof such models to validate the technical feasibility and business value of the shared model library concept.

2.0 CHALLENGE

The model-based enterprise will use complex models and simulations based on modular assemblages ofelectronically compatible models to design, develop, manage, and support products, processes, and op-erations. Doing so will require engineering and business applications to quickly locate and integrate theappropriate models and data (either generic or proprietary) needed to develop a response to user com-mands and queries. For product designers, this includes the ability to rapidly access different materialmodels to evaluate the benefits and impacts of various material options for a part, an assembly, or achemical formulation; and to quickly locate and “plug in” existing design components. For process andfacility engineers, it includes the ability to quickly access different equipment options to arrive at the op-timal production execution solution based on product requirements and enterprise capabilities and re-sources. For product support engineers, it includes the capability to quickly explore available supportequipment and tools to create the most effective solution for maintenance and repair. For program man-agers, it includes the capability to rapidly evaluate different sourcing options to provide the best balanceof low risk and assured ability to deliver.

In today’s environment, there is no systematic approach to managing models and associated data. Con-figuration management systems and product data management (PDM) tools only manage models and as-sociated data for specific configuration-controlled products. Leading CAD tools include libraries of stan-dard designs and features, and some companies have established proprietary libraries of designs and datato facilitate reuse and life-cycle support across product families. However, current model-based librariesin general do not support multiple application types (e.g., product design and process planning tools), ordifferent applications of a similar type (e.g., ProE vs. Intergraph vs. CATIA). Accessing the libraries thatare available is a manual function highly dependent on the expertise of the user. Models that are madeavailable for reuse come in many formats, with varying degrees of fidelity, and with limited confidence intheir accuracy.

In the NGMTI vision of the future manufacturing enterprise, model-based applications will perform allthe work of locating and integrating constituent models and supporting data based on the user’s specifica-tion of requirements and parameters. Manufacturers and suppliers of commodity items, parts, compo-nents, material stock, tools, equipment, and consumables will provide validated, high-fidelity models andsimulations of their products for inclusion in model repositories shared across industry sectors.

Using the shared library concept, product, process, and facility designers will specify a design element(e.g., pump) and designate required performance parameters and other attributes (e.g., 5 gpm and 10 psimax and <10 pounds). The design application will access the model library and present a list of availablecandidates for the designer to evaluate. When the designer makes a selection, the specific model will be



pulled into the design while carrying with it (or linking to) all available supporting data – reliability, sup-plier options, order lead time, price, availability, maintenance requirements, usage instructions, toler-ances, failure modes, optional features, and the like.

The shared model library concept requires manufacturers to provide validated, robust models of theirproducts along with supporting data in standard formats that are compatible with mainstream applications.The library function will provide the infrastructure for managing and maintaining the shareable models,providing search and notification functions for users and security functions to ensure model integrity andprotect proprietary data.


In the NGMTI vision, designers in the future manufacturing enterprise will draw on a comprehensive li-brary of validated, thoroughly characterized models and simulations of common materials, components,parts, subsystems, unit processes, and manufacturing equipment to create, produce, and support theirproducts. Reusable, scaleable models that autonomously search for information needed to execute theirown function will be standard tools for product and process engineering and manufacturing execution.

This project will establish the shared model library function required for realizing this vision and providethe capability to easily reuse and share models in accordance with the following requirements defined inthe NGMTI Roadmap for the Model-Based Enterprise.

3.1 GOALS AND REQUIREMENTS FOR SHARED MODEL LIBRARIES

• Goal 1: Shared Model Libraries – Establish an industry-wide network of shared libraries of vali-dated, well-characterized models that support plug-and-play simulation, proprietary tailoring, and op-timization of designs for products, processes, and operations. (M)29

– Framework for Model Library – Develop a broad-based framework to provide validated, inter-operable models that support multiple enterprise applications (design, manufacturing, product sup-port, etc.). Establish standards for secure shared access and for validation and characterization ofmodels prior to release to the library. (S)

– Model Library Management Approach – Develop a methodology for populating, updating,maintaining, extending, and ensuring the data quality/security of the shared model libraries, in-cluding the user interface and support tools. Include methods enabling all members of a supplychain to input to, access, and manipulate shared models in accordance with appropriate permis-sions, with assured security of every data element. Include the capability to provide a continuousaudit trail of all actions and automatically communicate changes to affected partners and personnel.(S-M)

– Component Libraries – Establish library segments for standard mechanical and electrical partsand components used in design and production of complex products. Areas to be addressed in-clude fasteners, connectors, lubricants and sealants, valves, pumps, piping and tubing, circuitboards, cables, and similar commodity items. (S)

– Science-Based Materials Model Repository – Establish an industry-wide shared repository ofvalidated, well-characterized models and simulations for materials database to support product andprocess modeling and analytical simulation. Define and establish linkages to certified/certifiableindustry, academic, and government sources to populate and update the database. (M)


M (Medium) = 3 to 5 years, and L (Long) = 5 to 10 years



– Validated Process & Equipment Model Repository – Establish an industry-wide shared reposi-tory of validated, well-characterized models and simulations for processes and equipment based onindustry priorities and value to multiple industry sectors. (M)

– Process Labor Standards Knowledge Base – Develop and establish a database of labor standards(i.e., time standards and skill/certification requirements) for all direct and indirect manufacturingprocesses and functions that interface to process design, simulation, planning, and resource man-agement systems. (S)


This project will provide the initial framework for shared libraries of compatible models and an initial setof models able to support manufacturing enterprise operations. The project plan contains five tasks:

Task 1 – Management Structure: This initial task shall establish the ground rules by which the commu-nity of contributors and users will govern themselves in the collaborative venture.

Task 2 – Library Structure: This task shall develop the scheme and methods for organizing the collec-tion of models within the library, providing traceability, information security, and search and accessfunctions for users. Compatibility with leading design and PDM applications shall be addressed to ensurethat library modules will interface with current model-based product definition management systems.

Task 3 – Certification Requirements: This task shall define requirements for validating, verifying, pro-viding configuration management, and assuring application compatibility (and inter-model collaboration)of the models in the library. Application interoperability requirements (e.g., ProE, CATIA, and Inter-graph) will be documented and addressed with the application vendors, but no further effort to resolvethese issues shall be funded under this project.

Task 4 – Initial Library Population: This task shall acquire, through development or submission fromthird parties, initial sets of models to support collaborative product and process design, estimating, pro-duction, resource management, product support, and related activities. The project team shall work withpotential contributors to define what assets can be acquired and made available as "off-the-shelf" re-sources, prioritize the gaps, and focus further effort on building up the initial resources in areas that offerthe maximum benefit in the pilots to be conducted under Task 5.

Task 5 – Library Pilots: The initial shared model library shall be made available for testing, evaluation,and pilot usage. The project team shall work with the pilot participants to define the pilot activities, es-tablish metrics and mechanisms for collecting feedback, lay out schedules for the pilots, collect feedbackto assess the effectiveness of the library structure and content, and document requirements for further de-velopment. These results shall be documented in a project final report. This report shall also address op-portunities and issues related to establishment of common standards for different types of models (e.g.,material models, factory equipment models) to support transparent sharing in industry-wide repositories.

4.0 BENEFITS & BUSINESS CASE


A central model library function is essential to realizing the MBE vision of enabling product and processdesigners, product support engineers, factory planners, business managers, and other enterprise functionsto share and reuse knowledge, data, and designs captured in model form. The ability to simply "plug in"validated models of high-fidelity material, product, or process components with performance propertiesand supply-chain sourcing data will reduce design cycle time and cost by an estimated 10 to 40% de-pending on design complexity, and will eliminate the need to manually capture supporting data. Use ofvalidated models will also reduce the need for acceptance testing while ensuring the ability of the speci-fied item to perform to the defined specification, and ensuring that designers spec the right item for therequired use and function.



For suppliers, the shared library function will greatly reduce overhead associated with marketing, sales,and responding to routine queries, since OEM customers will be able to select products directly from thelibrary, and electronically transfer all procurement requirements directly to the enterprise’s purchasingfunction for order placement. Suppliers will be able to update any attribute of their models on demand(e.g., price reduction, spec change), ensuring that existing and potential customers always have the latestinformation to guide their design and procurement choices.

4.2 BENEFITS TO DOD

The DoD is already a leading practitioner of shared model usage, with major programs such as FutureCombat Systems (FCS) and Joint Strike Fighter (JSF) using leading-edge workgroup environments toshare product definition models and performance simulations (e.g., NVTherm and MATLAB models)across multi-company teams. Current-generation simulator systems such as the Army’s Close CombatTactical Trainer and the Navy’s Generic Reconfigurable Training System make extensive use of sharedJoint Semi-Automated Forces (JSAF) models, allowing instructors to quickly set up pilots and gunnerswith training scenarios that call up terrains and targets from a ready library of modeled assets.

This project builds from that concept and extends it to new levels in the product design and developmentarena. The ability for distributed teams to operate from a shared materials library, for example, will en-able rapid integration and virtual testing of complex weapon systems incorporating contributions fromdozens of subcontractors and suppliers, with each contributor’s model carrying the exact same fidelityand accuracy for the shared, common aspects of the system model.

In the materials example, the structural model provided by a warhead section supplier will be seamlesslycompatible with the structural models provided by the propulsion section supplier and the guidance sec-tion supplier. This will reduce the time and difficulty of system-level structural analysis; but of greaterimportance, it would help ensure that the total structural design is right before the first part is cast – elimi-nating a potentially significant source of redesign requirements. Contractors could likewise share themanufacturing process and equipment models developed under this project, enabling end-to-end optimi-zation of manufacturing execution strategies for low-cost, high-quality, and on-time delivery.

System-of-systems modeling capabilities would also be greatly advanced using shared library techniques,enabling DoD planners to build complex multi-system scenarios in a fraction of current timelines whiledelivering far greater accuracy in wargaming simulations and mission rehearsals. The system model pro-vided by a missile system supplier, for example, would come “equipped” with all of its pertinent attrib-utes – speed, range, Pacq, Phit, Pk, aero envelope, sensor modes, RCS profiles, etc. – and would transpar-ently plug in to the model of its launch platform, enabling extremely high fidelity in operational simula-tions. The level of models being developed for leading-edge programs such as FCS, JSF, and JointCommon Missile (JCM) provide an excellent starting point for development of system-level shared modelrepositories.


The proposed project schedule is provided on the following page. The estimated cost for the 39-montheffort is $9.1 million.


Risk for this project is low to moderate. A large base of models can be harvested for use on the project todemonstrate the feasibility and value of shared model libraries, although validating and verifying theseassets presents a technical challenge – as does ensuring compatibility of the models with multiple appli-cations. Technology readiness is assessed at MTRL 2-3 since although the concept of model libraries iswell-established, current applications are limited to single tools and much of the base of available modelsis in proprietary formats that may not be readily useable by other tools.



Project Plan for Shared Model Libraries




MODEL-BASED RESOURCE MANAGEMENT

1.0 PROJECT SUMMARY

The objective of this project is to develop enabling technologies to create a foundational, model-basedmanufacturing enterprise resource management system framework that is modular, scaleable, and built onopen software standards. The project will deliver a baseline capability for modeling, simulating, and di-recting control over all manufacturing enterprise resources, and will enable expansion to deal with in-creasing size, complexity, and functionality of organizational processes. The system will also have aplug-and-play capability that minimizes the cost of deployment so that the costs of acquisition and im-plementation will be affordable for companies of all sizes.

2.0 CHALLENGE

Current enterprise resource planning/management (ERP/ERM) systems provide good capability to simu-late asset scenarios and control resources ranging from people and materials to equipment to facilities.The cost and complexity of these systems, however, make them affordable only to larger companies withthe financial resources and the technical staff needed to implement and support them. Many large ERPprojects have been abandoned because of technical, cost, and schedule problems that made implementa-tion untenable. Moreover, despite widespread migration to web-type and PC interfaces, the current gen-eration of systems and associated applications are proprietary, and hence force dependence on the vendorfor upgrades and support. Additionally, it is difficult to integrate these “business management” systemswith the wide variety of other software systems used in a manufacturing enterprise. Legacy systems areintegrated only at significant time and expense, and typically with limited success.

Even for enterprises that have the resources to install a fully capable ERP/ERM system, the process isexpensive and time-consuming, and the cost of deployment typically dwarfs the cost of buying the soft-ware. For most small companies the cost of these tools is prohibitive. Another problem is loss of flexi-bility; once an organization has gone through the trouble and cost of customization to establish a properlyrunning system, it is very difficult to implement changes to meet new business requirements.

Another problematic aspect of current ERP/ERM systems is their rigidity of design, which generally re-quires companies to change their business processes to fit the software rather than adapting the softwareto fit their business processes. Consequently, changes in corporate culture are required, dictating signifi-cant training efforts for users and write-off of previous outlays for other systems and processes. Whilethe new systems may ultimately provide significant improvements in quality and productivity, the disrup-tive impacts on corporate culture can be significant.

These problems are not easily solved, and the challenges addressed by this project are daunting. The in-tent is to create a model-based framework for ERM systems that yields the performance benefits of cur-rent major proprietary ERP/ERM systems, while being more flexible and affordable for all sizes of com-pany. The ability to link resource information directly to related models, and enable the models to auto-matically manipulate resource requirements, will eliminate much of the time, cost, and error currentlyassociated with capturing (and recapturing) data for use within the resource management system.

In the NGMTI vision, the core elements of the enterprise (including its business rules and strategies aswell as its processes and systems) will be modeled so accurately and thoroughly that routine allocation ofresources will be handled autonomously by enterprise resource management (ERM) systems. These sys-tems will have total connectivity to all enterprise processes and assets – including product/process capa-



bilities, manpower and skills, facilities and equipment, raw material and product inventories, supply chaincapabilities, and working capital and budgets (Figure 2-1).

Figure 2-1. Future ERM systems will provide total connectivity of all enterprise processes to all enterprise re-sources, with powerful modeling and simulation capabilities that enable fast, accurate decisions.

This seamless connectivity will extend to every tier of the enterprise’s supply chains. An open businesssystems architecture based on well-defined standards for modeling and managing different types of re-sources will enable different companies to quickly “plug together” to exploit new opportunities. Whileallocation of resources will always be at the discretion of the enterprise’s managers, the ability to accesscurrent resource information anywhere in the supply chain – with appropriate security – will eliminatemuch of the inefficiency inherent to managing complex supply chain relationships.

Science-based models of the inputs, outputs, demand factors, and dependencies of every enterprise proc-ess, coupled with continuous access to all sources of information that affect these processes, will provideclear definition of what resources need to be where and when, and when they will be available again forreallocation. These models will control the systems that execute the enterprise’s technical and businessprocesses. Managers at all levels will interact with the system to develop plans, monitor performance,analyze issues, evaluate opportunities, and efficiently direct resources to point of need.

The greatest benefits of model-based resource management will come from radically improved ability toprepare for new requirements, and to respond to problems, throughout the supply chain. Future productand process models will provide precise definitions of the resources they require for their execution – in-cluding raw materials, parts, and components; manufacturing labor and skills; facility space, equipment,tooling, and fixtures; handling and transport; and product support, including training and documentation.These requirements will be “uptaken” by the ERM system and fed to functional planning systems for im-plementation. Managers will use desktop modeling and simulation tools, connected to the enterprise’sknowledge bases, to evaluate options for meeting the requirements with those resources in ways that offerthe best balance of performance, speed, cost, risk, and profitability.

To achieve this vision, the model-based resource management systems framework must be built on a trulyopen architecture that permits ready extension for new applications and new business demands. This ar-



chitecture must serve a broad range of resource management functions (manpower and skills, equipment,materials, etc.) that are each tied to high-fidelity models and simulation tools that are managed by mod-ules that can be added or deleted according to business needs. A common generic model structure isneeded to house the information required for any business function and enable real-time, seamless ex-change of information among all of the models and their “owner” systems. The capability must be devel-oped to automatically associate resource information to product and process models and their constituentfeatures.

Certain specialty model-based applications will be required. These include an advisory function able torecognize requirement changes that impact a particular resource model, implement the needed changes,and verify the accuracy and completeness of the transaction. A real-time data integration model is alsorequired to monitor data flows and ensure that the right models are receiving and sharing the right datathey need to perform their own functions as well as support the models that are “upstream” or “down-stream” from the resource node. The ability to perform real-time data quality assurance checks on re-source information entering the system – particularly from vendors and suppliers – and flag potentialproblems is also critical. Other requirements will emerge as the system architecture is developed.

The baseline system must address an initial set of business functions in a given enterprise, and supportlinking of business functions with external entities (e.g., customers and subcontractors). While differentcompanies will not be willing to fully open their “books” and systems, the ability to share knowledge andseamlessly exchange essential data for a shared endeavor is vital. Thus, tools must be provided that rec-ognize the data structures of incoming resource information or external system interfaces and extract andformat the data according to the defined needs of the receiving system and the affected models. For ex-ample, an enterprise serving as a part of a supply chain may wish to share knowledge of its equipmentcapabilities while not revealing knowledge initially about capacity, schedule, or cost. As the businessrelationship develops, additional information may be revealed with the consent of the owner. Specialmodels and information management tools will be required to support such data negotiation needs.


This project will construct and demonstrate an open, modular, model-based enterprise resource manage-ment system that emulates the basic functionalities of current proprietary systems while greatly reducingacquisition and operation costs and providing a new generation of model-based planning and managementcapabilities. Project goals and requirements are summarized below.

3.1 GOALS AND REQUIREMENTS FOR MODEL-BASED RESOURCE MANAGEMENT

• Goal 1: Model-Based Resource Management – Provide model-based tools and techniques to facili-tate management of all resources across all components of the manufacturing enterprise. (M)30

– Model-Based Enterprise Architecture – Develop an open, model-based business systems archi-tecture that enables the necessary interconnections and resource-related information flows betweenand among different enterprise processes. Include the capability to support different sizes andtypes of manufacturing enterprises, including small suppliers as well as OEMs and complex supplychains. (M)

– Generic Resource Models – Develop a generic set of models and modeling standards for commonresource types which can be customized to meet the specific needs of any manufacturing enter-prise. Include materials, manpower, skills, process equipment, unit processes, facilities, capi-tal/cash, and other common forms of resource. (M)





– Resource Data Linking – Develop methods, tools, and techniques for linking model-based re-source management applications to current resource status information from different processes,functions, sites, and organizational entities. (S)

– Resource Change Management – Develop a computer-based advisory tool, compatible with cur-rent MRP/ERP/ERM and operations management software applications, to alert resource ownersand users when a requirement changes, so as to enable quick negotiation and implementation of theproper response. Include the capability to automatically communicate changes in resource re-quirements and availability to all affected organizations, systems, and applications. (M)

• Goal 2: Multi-Enterprise ERP/ERM Integration – Provide mechanisms and methods for rapidlyinterconnecting the systems of different enterprise partners to integrate ERP/ERM functionality to thelowest tier of the supply chain. (M)

– ERP/ERM Interface Frameworks – Develop interface frameworks and standards for quickly andseamlessly integrating different resource management systems across different companies. Includethe capability for ERP/ERM systems to automatically negotiate full or limited interfaces dependingon the capabilities of the systems being interfaced. (M)

– Linkages to External Resource Sources – Develop tools for linking ERM systems to external re-source information sources to enable continuous update of resource information to support plan-ning and decision processes. (S)

– Distributed Resource Status Tracking – Develop model-based tools for continuously trackingand forecasting resource status throughout the supply chain, enabling real-time updating of activityschedules based on internal and external resource constraints. (M)


The project will develop and demonstrate a framework and component capabilities for model-based re-source management systems, and build a pilot system for a chosen manufacturing sector. Specific tasksare as follows.

Task 1 – Model-Based Resource Management Infrastructure: This task shall develop open-architecture tools and techniques to facilitate model-based management of all resources across all compo-nents of the manufacturing enterprise. Subtasks include:

1. Resource Management Architecture: Develop a model-based business systems architecturethat enables the necessary interconnections and resource-related information flows between andamong different enterprise process models. Include the capability to support different sizes andtypes of manufacturing enterprises (e.g., OEM and small supplier, consumer products, and de-fense).

2. Generic Resource Models: Develop a generic, modular set of resource models and modelingdevelopment specifications (structures and information interchanges) for common resource types,which can be customized to meet the unique needs of any manufacturing enterprise. Include ma-terials, manpower, labor skills, process equipment, unit processes, capital/cash, and other com-mon forms of manufacturing resource.

3. Resource Data Linking: Develop methods, tools, and techniques for linking model-based re-source management applications to current resource status information (e.g., availability, capac-ity/capability, and fitness for use) from different processes, functions, sites, and organizationalentities.

4. Resource Management System Demonstration: Demonstrate the functionality of model-basedenterprise resource management in a representative factory setting. The demonstration shall in-



clude full documentation of the system and recommendations for improvements and extensions.Standards will be established that facilitate interface with other business systems and applicationsused by the enterprise.

5. Resource Change Management: Develop an advisory module, compatible with currentMRP/ERP/ERM and operations management applications, to alert resource owners and userswhen a requirement changes, so as to enable quick negotiation and implementation of the properresponse. Include the capability to automatically communicate changes in resource requirements,status, and availability to all affected organizations, systems, and applications.

Task 2 – Multi-Enterprise Resource Management: This task shall develop mechanisms and methodsfor interconnecting the systems of different enterprise partners to integrate model-based ERM functional-ity to the lowest tier of the supply chain.

1. ERP/ERM Interface Frameworks: Develop interface frameworks and standards for quicklyand seamlessly integrating disparate resource management systems across different companies.Include the capability for ERP/ERM systems to automatically negotiate interfaces depending onthe capabilities of the systems being interfaced and the information permissions established bythe data owner.

2. Linkages to External Resource Sources: Develop tools for linking the model-based resourcemanagement system to external resource information sources to enable continuous update of re-source information to support planning and decision processes.

3. Distributed Resource Status Tracking: Develop model-based tools for continuously trackingresource status throughout the supply chain, enabling real-time updating of activity schedulesbased on internal and external resource constraints.


4.1 BENEFITS TO DOD

Model-based resource management capabilities are expected to provide notable benefit to DoD primarilythrough improving resource management efficiency in the defense manufacturing supply base, reducingadministrative overheads, and improving the quality of planning for production. The ability to better pre-dict, understand, and accommodate the resource impacts of procurement problems (e.g., delivery delaysby lower-tier suppliers) will reduce program management costs and provide better visibility of problems,enabling corrective actions to be taken earlier – thus improving the ability of government program man-agers to keep acquisition and deployment schedules intact.

Success in this program will also provide the technology validation needed to lay the foundation for mi-grating the technologies and concepts of model-based resource management to the DoD materiel andmanpower management arena. This offers the potential to streamline logistics chains; better anticipatethe impacts of drawdowns in consumables and expendables (e.g., ammunition, batteries, spares, and re-pair parts); shorten timelines for fielding of systems and deployment of units; and improve efficiency inscheduling and delivery of training (both initial and refresher). The approach in this area would leverageDoD’s investments in autonomic logistics for programs, such as Joint Strike Fighter. It would couplemodels with real-time feedback from diagnostic and prognostic systems to ensure the right resources arein hand at the right place at the right time to enable fast execution of maintenance, repair, and resupplyactions for all assets across an entire theater of operations.


The primary benefit expected from this project is the provision of model-based resource management ca-pabilities that: 1) greatly reduce the cost of acquiring, deploying, and maintaining a resource managementsystem, thereby making the capabilities affordable by enterprises of all sizes; and 2) enable far greater



accuracy and efficiency in managing resources by enabling product and process models to manage re-source information automatically and, in many applications, autonomously. The open, modular construc-tion of these systems will enable low-cost addition of new resource system components as they areneeded. Instead of being dependent on costly and inflexible proprietary systems, the open structure of thesystem and its interfaces will reduce the cost of changing the business systems infrastructure and willgreatly simplify the process of integrating new resource functions into the system.

Perhaps the greatest benefit of this project will be the flexibility for smaller companies to choose resourcemanagement tools, and be able to interface their systems to those of their prime manufacturer customersor project partners without having to buy the prime’s system. This is an insoluble problem today forsmall manufacturers that support multiple supply chains, particularly in the automotive and defense sec-tors.


The proposed project schedule is provided below; estimated cost for the 40-month effort is $4.7 million.


The proposed project is assessed as medium to high risk, due primarily to the significant challenge asso-ciated with developing an integrating framework that can support all of the required functionality while atthe same time allowing integration of the huge base of existing tools, databases, and applications cur-rently in use throughout different industry sectors. Engaging the support of resource management toolvendors operating in this market is also critical, since success hinges in part on the ability of the project toleverage the community’s technology development resources. It is also clear that the project must be per-ceived as an opportunity, not a threat, for this market.

Technology readiness for this area is assessed at TRL 3-4. The basic concepts are clearly valid and ele-ments of capability are commercially available. The challenge is to establish a framework that enablesintegrated functionality and robust utility – particularly for the generic resource models – then to developthe tools and mechanisms to fill in the gaps.


A-1

APPENDIX

MBE PROJECT PLAN

This section presents a compiled schedule of the scopes of work outlined in the MBE project plan whitepapers. The schedule is intended as an input to the NGMTI implementation process, and therefore repre-sents a starting point for more detailed planning.

ID Task Name Duration Start Finish

1 Enterprise-Wide Cost Modeling 660 d 10/2/06 4/10/092 1.0 Detail Project Plan 300 d 10/2/06 11/23/073 1.1 Team Development 6 mo 10/2/06 3/16/074 1.2 Plan and Estimate 3 mo 3/19/07 6/8/075 1.3 Current Practice Estimate 6 mo 6/11/07 11/23/076 1.4 New Element ID 6 mo 6/11/07 11/23/077 2.0 Model-Based Cost Architecture 240 d 11/26/07 10/24/088 2.1 Available Cost Model Survey 2 mo 11/26/07 1/18/089 2.2 New Element Incorporation 12 mo 11/26/07 10/24/08

10 3.0 Applications Development 480 d 10/2/06 8/1/0811 3.1 Integrating Protocol 12 mo 10/2/06 8/31/0712 3.2 Existing Cost Model Enhancement 12 mo 9/3/07 8/1/0813 4.0 Data Linking 300 d 9/3/07 10/24/0814 4.1 Collaborating Software Development 12 mo 9/3/07 8/1/0815 4.2 Elements Integration 6 mo 5/12/08 10/24/0816 5.0 Demonstrate Cost Estimating Improvements 3 mo 10/27/08 1/16/0917 6.0 Final Report 3 mo 1/19/09 4/10/092 Shared Model Libraries 780 d 10/2/06 9/25/092 1.0 Management Structure 120 d 10/2/06 3/16/073 1.1 Working Group 2 mo 10/2/06 11/24/064 1.2 Workshop development and publication 4 mo 11/27/06 3/16/075 2.0 Library Structure 120 d 3/19/07 8/31/076 Information Requirements 4 mo 3/19/07 7/6/077 Software Hooks 6 mo 3/19/07 8/31/078 3.0 Certification Requirements 240 d 9/3/07 8/1/089 3.1 Certification Requirements 6 mo 9/3/07 2/15/08

10 3.2 Document and Socialize Requirements 6 mo 2/18/08 8/1/0811 4.0 Initial Library Population 160 d 8/4/08 3/13/0912 4.1 Cost model 8 mo 8/4/08 3/13/0913 4.2 Product model 8 mo 8/4/08 3/13/0914 4.3 Process model 8 mo 8/4/08 3/13/09

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ID Task Name Duration Start Finish15 4.4 Resouce model 8 mo 8/4/08 3/13/0916 5.0 Library Pilots 7 mo 3/16/09 9/25/093 Flexible Representation of Complex Models 1740 d 10/2/06 5/31/132 1.0 System Requirements & Architecture 1200 d 10/2/06 5/6/113 1.1 Common Modeling Terminology 12 mo 10/2/06 8/31/074 1.2 Technical Specs and Interim Plan 12 mo 10/2/06 8/31/075 1.3 Product Model Framework 36 mo 9/3/07 6/4/106 1.4 Product/Process Model Integration Standards 36 mo 8/4/08 5/6/117 1.5 Hierarchical, Composable, Shareable Models 24 mo 7/6/09 5/6/118 2.0 User Interface 960 d 8/4/08 4/6/129 2.1 Automated Abstraction 48 mo 8/4/08 4/6/12

10 2.2 Natural Language Interaction 36 mo 8/4/08 5/6/1111 2.3 Multi-Sensory Representation 36 mo 8/4/08 5/6/1112 3.0 Model Functionality 1440 d 9/3/07 3/8/1313 3.1 Unit Process Models 36 mo 9/3/07 6/4/1014 3.2 Plug-and-Play Vendor Models 24 mo 7/6/09 5/6/1115 3.3 Process Performance Models 36 mo 7/6/09 4/6/1216 3.4 Self-Monitoring Product & Process Models 36 mo 7/6/09 4/6/1217 3.5 Self-Composing Models 36 mo 6/7/10 3/8/1318 Final report 3 mo 3/11/13 5/31/134 Multi-Enterprise Integration 720 d 10/2/06 7/3/092 1.0 Project Organization & Planning 80 d 10/2/06 1/19/073 1.1 Project Team & Plan 3 mo 10/2/06 12/22/064 1.2 Supply Chain Selection 1 mo 12/25/06 1/19/075 2.0 Methods & Standards 480 d 1/22/07 11/21/086 2.1 Top-Level Process Models 6 mo 1/22/07 7/6/077 2.2 Application mapping & Gap Analysis 4 mo 5/14/07 8/31/078 2.3 Tool Selection & Development 16 mo 9/3/07 11/21/089 3.0 Integration Pilot 160 d 11/24/08 7/3/09

10 3.1 Demonstration Prep 2 mo 11/24/08 1/16/0911 3.2 Pilot 4 mo 1/19/09 5/8/0912 3.3 Final Report & Recommendations 2 mo 5/11/09 7/3/09

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ID Task Name Duration Start Finish5 Configuration Management for the Model-Based Enterprise 1300 d 10/2/06 9/23/112 1.0 CM System Requirements & Architecture 300 d 10/2/06 11/23/073 1.1 Standard Data/Knwldg Representation & Mgmt 130 d 10/2/06 3/30/074 1.2 Model Management Lexicon 130 d 1/29/07 7/27/075 1.3 Life-Cycle CM Requirements Definition 198 d 10/30/06 8/1/076 1.4 Model-Based CM Frameworks 260 d 11/27/06 11/23/077 2.0 CM System Components 524 d 10/1/07 10/1/098 2.1 Extraction of Product Configuration Data 180 d 10/1/07 6/6/089 2.2 Automated Model Information Delivery 220 d 11/26/07 9/26/08

10 2.3 Automated Change Management 264 d 1/21/08 1/22/0911 2.4 Automated Change Propagation 264 d 9/29/08 10/1/0912 3.0 Associated Systems 780 d 9/29/08 9/23/1113 3.1 MB Product Requirements Mgt Envmt 528 d 9/29/08 10/6/1014 3.2 Data Management & Auditing System 528 d 3/16/09 3/23/1115 3.3 Real-Time product support Linkage 440 d 1/18/10 9/23/116 Intelligent Models 756 d 10/2/06 8/24/092 1.0 Project Planning 3.15 mo 10/2/06 12/27/063 2.0 Intelligent Models Requirements 13.65 mo 12/28/06 1/14/084 3.0 Functionality Development 22.05 mo 7/23/07 3/30/095 4.0 Technology Demonstrations 14.7 mo 7/9/08 8/24/097 Model-Based, Real-Time Factory Operations 740 d 10/1/06 7/31/092 Project Planning 160 d 10/2/06 5/11/073 Form project management team 3 mo 10/2/06 12/22/064 Define project plan 3 mo 12/25/06 3/16/075 Form technical team 2 mo 3/19/07 5/11/076 Recruit external experts for review panel 2 mo 3/19/07 5/11/077 Requirements Definition 240 d 5/14/07 4/11/088 Create functional map of all components and relationships 12 mo 5/14/07 4/11/089 Define integration requirements 12 mo 5/14/07 4/11/08

10 Define structures of models 12 mo 5/14/07 4/11/0811 Systems Develoment 240 d 4/14/08 3/13/0912 Create, test and refine prototypes of models 12 mo 4/14/08 3/13/09

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ID Task Name Duration Start Finish13 Create model standards for further development. 12 mo 4/14/08 3/13/0914 System Demonstration 2 mo 3/16/09 5/8/0915 Report 3 mo 5/11/09 7/31/098 Model-Based Product Life Cycle Management 1320 d 10/2/06 10/21/112 1.0 Product LC Model-Based Framework 120 d 10/2/06 3/16/073 1.1 Case Studies 2 mo 10/2/06 11/24/064 1.2 Information Models 2 mo 10/2/06 11/24/065 1.3 Requirements Definition & Gap Analysis 4 mo 11/27/06 3/16/076 2.0 Phase I Demonstrations 12 mo 3/19/07 2/15/087 3.0 Phase I Framework Validation 480 d 2/18/08 12/18/098 3.1 Generic LC Model (Simple Product) 6 mo 2/18/08 8/1/089 3.2 Independent Evaluation 6 mo 8/4/08 1/16/09

10 3.3 Functionality Testing 6 mo 1/19/09 7/3/0911 3.4 Enhancements & Extensions 6 mo 7/6/09 12/18/0912 4.0 Phase II Demonstration & Validation 480 d 12/21/09 10/21/1113 4.1 Generic LC Model (Complex Product) 6 mo 12/21/09 6/4/1014 4.2 Tool Extensions 6 mo 6/7/10 11/19/1015 4.3 Implementation & Testing 12 mo 11/22/10 10/21/119 Model-Based Resource Management 836 d 10/2/06 12/14/092 1.0 Model-Based Resource Mgmt Infrastructure 836 d 10/2/06 12/14/093 1.1 Resource Mgmt Architecture 1.1 mo 10/2/06 10/31/064 1.2 Generic Resource Models 22 mo 11/1/06 7/8/085 1.3 Resource Data Linking 8.8 mo 5/8/08 1/8/096 1.4 Resource Mgmt System Demo 1.1 mo 1/9/09 2/9/097 1.5 Resource Change Mgmt 17.6 mo 8/8/08 12/14/098 2.0 Multi-Enterprise Resource Management 682 d 4/4/07 11/12/099 2.1 ERP/ERM Interface Frameworks 18.7 mo 4/4/07 9/8/08

10 2.2 Linkages to External Resource Sources 14.3 mo 9/9/08 10/13/0911 2.3 Distributed Resource Status Tracking 1.1 mo 10/14/09 11/12/0910 Product Driven Product & Process Design 968 d 10/2/06 6/16/102 1.0 Project Organization 6.6 mo 10/2/06 4/3/073 2.0 Automated Comprehensive Product & Design 792 d 4/4/07 4/15/10

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ID Task Name Duration Start Finish4 2.1 Common Product & Process Specifications Standards 26.4 mo 4/4/07 4/10/095 2.2 Design Knowledge Base 26.4 mo 4/4/07 4/10/096 2.3 Automated Design for Assembly 26.4 mo 4/8/08 4/15/107 2.4 Accurate Process Simulations Tools 26.4 mo 4/8/08 4/15/108 3.0 Automated Process Planning 924 d 10/2/06 4/15/109 3.1 Process Model Repository 13.2 mo 10/2/06 10/4/07

10 3.2 Process & Resource Capability Models 13.2 mo 10/5/07 10/8/0811 3.3 Multi-Level Interoperable Process Models 19.8 mo 10/9/08 4/15/1012 4.0 Product/Process Development Uncertainty & Risk 39.6 mo 4/4/07 4/15/1013 5.0 Demonstrate Initial Product Driven Environment 2.2 mo 4/16/10 6/16/1011 Model-Based Distribution 836 d 10/2/06 12/14/092 1.0 Real-Time, Responsive Distribution Mgmt 792 d 10/2/06 10/13/093 1.1 System Rqmts Definition 6.6 mo 10/2/06 4/3/074 1.2 Design for Distribution 13.2 mo 5/4/07 5/7/085 1.3 Integrated Distribution Modeling 16.5 mo 5/4/07 8/7/086 1.4 Model-Based Product Tracking 9.9 mo 1/2/07 10/4/077 1.5 Pull-Based Distribution 19.8 mo 4/8/08 10/13/098 1.6 Special Materials Mgmt 11 mo 6/5/07 4/7/089 2.0 Intelligent Asset/Inventory Modeling 682 d 5/4/07 12/14/09

10 2.1 Adaptive Inventory Modeling Apps 18.7 mo 5/4/07 10/8/0811 2.2 Inventory Modeling Info Interface 13.2 mo 6/9/08 6/11/0912 2.3 Automated Demand Prediction 13.2 mo 10/9/08 10/13/0913 2.4 Capacity Mgmt System Interface 11 mo 2/10/09 12/14/0912 Information Delivery to Point of Use 1584 d 10/2/06 10/25/122 1.0 Comprehensive Planning and MFG Execution System 528 d 10/2/06 10/8/083 1.1 Requirements for MB Control of Manufacturing Execution 6.6 mo 10/2/06 4/3/074 1.2 Evaluate Existing Modeling Tools 6.6 mo 4/4/07 10/4/075 1.3 Create Integrated Planning System 6.6 mo 10/5/07 4/7/086 1.4 Create Intelligent Manufacturing Execution Model 6.6 mo 4/8/08 10/8/087 2.0 Determine Information Viewing Requirements 132 d 10/2/06 4/3/078 2.1 Develop Requirements for Execution Domains 6.6 mo 10/2/06 4/3/079 2.2 Prepare Planning Processes to Provide the Info 6.6 mo 10/2/06 4/3/07

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ID Task Name Duration Start Finish10 2.3 Assess Current Viewing Technologies 6.6 mo 10/2/06 4/3/0711 2.4 Evaluate Recent Pilot Info Delivery Efforts 6.6 mo 10/2/06 4/3/0712 3.0 Develop Architecture and Standards 264 d 4/4/07 4/7/0813 3.1 Functional Architecture for Information Delivery 6.6 mo 4/4/07 10/4/0714 3.2 Recognized Standards for Information Delivery 6.6 mo 4/4/07 10/4/0715 3.3 Technical Architecture for Information Delivery 6.6 mo 10/5/07 4/7/0816 4.0 Short-Term Information Delivery Capability to Pt of Use 528 d 10/5/07 10/13/0917 4.1 Provide Information Delivery Toolset 13.2 mo 10/5/07 10/8/0818 4.2 Provide Solution Products 13.2 mo 10/9/08 10/13/0919 5.0 Extend Model-Based Support to Other Execution Systems 528 d 4/4/07 4/10/0920 5.1 MB Support of Maintenance & Repair 13.2 mo 4/4/07 4/7/0821 5.2 MB Support of Training 13.2 mo 4/8/08 4/10/0922 6.0 Next Generation Information Delivery Capabilities 792 d 10/14/09 10/25/1223 6.1 Develop New Info Delivery Capabilities 39.6 mo 10/14/09 10/25/1224 6.2 Develop New Communication Capabilities 39.6 mo 10/14/09 10/25/12

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