Optimizing Product Realization Costs Across the Value ChainBy leveraging various cost optimization enablers, global automotive, aerospace, discrete manufacturing and medical device companies can identify and optimize costs holistically across the product lifecycle, thereby improving safety, environmental and regulatory compliance, sustainability and quality of products.

Executive SummaryAutomotive, aerospace, discrete manufacturing and medical devices companies face numerous business challenges daily. Shrinking profit margins (due to unrelenting competition) and the movement of manufacturing to lower-cost regions are among the stiffest such challenges. Moreover, amid these escalating pressures, companies need to produce high-quality, differentiating products, while remaining cost-effective.

Each product development phase uniquely contributes to the company’s cost equation. Howeover, most product development companies apply cost optimization inititiaves to individual silos. To get more bang from these efforts, they need to embrace a holistic view of cost optimiza-tion across the product realization value chain.

This white paper identifies and addresses the various pain areas or factors contributing to cost overruns across the product realization lifecycle. The paper addresses the challenges within the

cognizant 20-20 insights | may 2016

• Cognizant 20-20 Insights

four major phases of product realization in the value chain:

• Requirements and feasibility study.

• Design, verify and validate.

• Approve, manufacture and launch.

• Post-market observation.

Apart from the enablers/best practices in each product development phase, we also highlight key industry trends. In our view, early adoption of emerging solutions would help optimize product costs and enhance growth prospects.

Product Cost Optimization ChallengesBased on our domain and engagement experience, and discussions with multiple stakeholders across industry functions, we discovered the full sweep of factors that contribute to cost overruns across the product development lifecycle, and corresponding best practices to avoid such cost overruns (see Figure 1, next page).

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Cost Overrun Factors and Avoidance Best Practices

• Inaccurate or Incomplete determination of VOC or VOB.

• Inadequate time spent on requirements management.

• Insufficient exploration of concepts and prototypes.

• Multiple iterations in design and testing.

• Inadequate attention to DFx requirements.

• Lack of focus on cost/value aspect of design.

• Initiating V&V efforts after design is nearly complete.

• Manual approval/wet signature for design and test artifact approvals.

• Test all for CSV/COTS validation.

• Single source or long lead items in BOM.

• Increase in raw materials costs.

• Poor data quality in device trails and on road testing.

• Insufficient post-production monitoring.

• Ineffective root cause analysis and incomplete issue resolution before releasing the product after a recall.

• Spend adequate time on VOC/VOB and requirements cascade.

• Complete requirements analysis before proceeding to design.

• Enable knowledge-based engineering to reduce iterations.

• Adopt DFx checklist for analysis.

• Risk-based CSV/COTS validation.

• Early involvement of V&V to identify requirements defects early.

• Adoption of electronic document management systems and test management systems.

• Avoid single source or long lead items; if unavoidable, plan for adequate inventory.

• Plan and approach manufacturing phase using optimization tools.

• Usage of quality control techniques like Six Sigma and Lean manufacturing.

• Working with contract manufacturers by outsourcing work.

• Integrated quality management system that stresses active vigilance.

• Use analytics to preempt failures.

• Thorough root-cause analysis before re-releasing the product to the field after a recall.

Requirements & Feasibility Study

Design, Verify & Validate Approval Manufacture & Launch

Post-Market Observation

Social Media PLM Integration

LatestThinking

CAE PLM Integration 3-D Shape Search

Optimize Product Cost Using Big Data Analytics

Connected Vehicles and Internet of Things

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Figure 1

Product Realization Requirements

Figure 2

Typical Product Realization Requirements

Design & Risk Management

Sourcing & Manufacturing

Interface PerformanceProduct & Data Security

Service & Installation

Labelling Documentation

Packaging & Shipping Usability Regulatory

Ideation: Requirements Management Stage

The successful realization and marketing of any product is dependent on capturing correct requirements as early as possible in the lifecyle. Faulty requirements during product development constitutes nearly 40% to 60% of total errors.

In our experience, cost overrun factors at the ideation stage include the following:

• The inability to accurately identify the voice of customer (VoC) and voice of business (VoB) at

the start of the project, and inadequate time spent on requirements due to strict timeline commitments.

• Poor requirements and document-manage-ment practices.

• Neglecting process and documents to accelerate working product development.

• Lack of adequate dialogue with a regulatory authority at an earlier stage of product devel-opment – such as with the Federal Drug Admin-istration (FDA) in cases concerning medical device development.

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Ideation Cost Optimization Enablers

Maintaining complete, correct, feasible and pri-oritized requirements within a requirements management system can help optimize costs (see sidebar below). Product lifecycle management (PLM) systems, for example, typically come equipped with requirements management capability, which helps to:

• Improve product quality and provide better traceability and risk management.

• Improve compliance to regulatory standards.

• Avoid rework and recalls, and ensure product is on time to market.

A PLM accelerator for socio-interactive automotive product development helps car makers acquire and apply social media feedback in a structured and meaningful way in their PLM systems during car platform development.1

Integrating systems-engineering capabili-ties into PLM improves collaboration between various departments of product development (mechanical, electrical and software design).2

Design/Verify/Prototype StageDesign is another critical phase in the product realization value chain. Approximately 70% of the product costs are fixed during its design.3

Typical cost overrun factors faced by product development companies during the design, verify and validation phase include:

• Time wasted in iterations done during design, design prototyping and prototype testing.

• Inadequate attention to design for x or DFx (where x = manufacturing, testing, assembly, etc.) requirements.

• Designers unaware of how to focus on costs or their value attributes.

• Consideration for verification and validation activities after the requirements and design activities are completed.

Design Stage Cost Optimization Enablers

Product companies can adopt our knowledge-based engineering and VA-VE framework (see Figure 5, page 5) to effectively:

• Improve integration between computer-aided design (CAD), computer-aided manufacturing (CAM) and computer-aided engineering (CAE) systems.

• Optimize products using “what-if” scenarios.

• Improve time to market.

• Reduce, rework and reuse design knowledge.

Quick Take Integrated Requirements Management for a Global Automotive Major

• Business situation: A global automotive major was using legacy systems to manage require-ments. As a result, its engineers were spending an inordinate amount of time fetching data and managing requirements.

• Solution: A bidirectional integration of the PLM tool with the legacy system was developed. Earlier, the company used two-plus Java-based applications for requirements gathering, supplier management, manufacturing bill of materials control and CAD management. Using Teamcenter PLM, we were able to integrate all of those functions into a single tool and reduce the number of applications and approvals/signoffs, with improved visibility and transparency.

• Benefits: As a result of the integration, the automotive major could:

> Easily maintain, manage and fetch the re-quired information with regard to standards and regulations.

> Conduct root-cause analysis with reduced lead time.

> Reduce the number of applications.

> Increase operational visibility and transpar-ency.

> Manage regulatory and standard-process-re-lated information in a centralized repository.

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Quick Take Automated Design Verification and Validation at a Global Automotive Major• Business situation: A global automotive major

was using a traditional and manual process for design verification and validation. As a result of this traditional process:

> Time was wasted in repeatable work during design and prototyping.

> Design knowledge was lost when employees left the organization.

> There was an absence of design verification and validation standards.

• Solution: A proven, knowledge-based engi-neering framework was implemented that automated the CAD-based verification and validation process.

• Benefits: As a result of automating the verifi-cation and validation process, the automotive major has:

> Increased efficiency and productivity, and improved quality via repeatability and re-producibility.

> Re-stratified and effectively used engineer-ing manpower.

> Effectively integrated its CAD and enter-prise information systems.

> Separated decision support from decision-making activity.

> Made product creation process automation the primary driver.

• Create design, validation and verification standards.

• Improve functional focus and value.

• Create sustainability and reduce costs:

> Improve safety, ergonomics, usability and process efficiency.

> Reduce the total number of parts in an assembly.

Traditional and Knowledge-Based Engineering (KBE) Design

Figure 3

Design Inputs

Design Outputs

Design Verify, Validate & Launch

Production

Redesign Loops

Post-Production Maintenance

Design Inputs

Empirical & Analytical Knowledge About the System

Design Outputs

Design Verify, Validate & Launch

Production

Source of Knowledge

Knowledge- Based Design

Aid

Post-Production Maintenance

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ProblemStatement

Measure Analyze

SMART

Reengineer Test &Validate

Our VA/VE Framework: SMART

Figure 5

� Understand customer needs.

� Define “as is” and “to be” condition in measurable terms.

� Project charter.

� Define the defects or issues.

� Gather baseline information.

� Critical to quality customer requirements.

� Process map.

� Analyze info/data collected for evaluation.

� Identify top contributors for defects or issues.

� Set goal for improvements.

� Develop concepts/ideas for better design.

� Finalize ideas and perform detailed design engineering.

� Perform cost analysis.

� Validate design using CAE analysis.

� Build functional prototypes.

� Measure performance of new design.

Our Approach to KBE-Design Automation: CFAD

Figure 4

» »

KBE System for Design Automation

Input• Customer • Specification

Product Data

• Knowledge acquisition through reports, design, manufacturing, performance and maintenance functions.

• Captured knowledge from business to maintenance activities & modeling techniques.

• Use of spreadsheet for capturing knowledge by implementing equations or rules that enable knowledge recycling

• Interpretation of acquired knowledge into rules and formalization to a computer-implement-ation-friendly format.

• Formalization using a partner-company approach. During this activity the implementation structure – i.e., a hierarchical class structure – takes shape.

• Implementation captured process, rules & activities to develop a KBE tool.

• Follow iterative process between the capturing of engineering knowledge and the automation of engineering activities.

• Testing of tool and/or application by allowing engineers to design the lifecycle properties of the product.

• Packaging & deployment of the tool.

Product Model• Geometry• Configuration• Product Knowledge• Engineering

Knowledge

External Data• Catalogues• Tables• Materials• Analysis

Output• Reports• Drawings • Costs• BOM• Manufacturing Plans• CAD Models/Assemblies

Capture of Engineering Knowledge

Knowledge Formalization

Automation of Engineering Activities

Deploy Quality Control of Engineering Activities

cognizant 20-20 insights 6

PLM integrated 3-D shape search helps designers in identifying parts of similar shape in the enterprise databases. As a result, companies can save time by avoiding rework.4 In the medical device industry, 3-D printing is another instant prototyping technology that has proven to be rev-olutionary. With the use of 3-D printing, surgeons can design implants and instrumentation specific to each patient. When doctors need to create a new device on demand for rare, unpredictable conditions, 3-D printing is applied.

ManufactureManufacture and launch is another critical phase in the device lifecycle. While requirements and design set the foundation for a successful product launch, costs incurred on manufacturing and the supply chain can make or break a product launch. Hence, cost optimization in manufacturing is of prime importance. The factors that contribute to cost overruns in this phase inclde:

• Increase in raw materials cost.

• Selecting single source for long-lead items.

• Poor data quality in device trials and on road testing.

Manufacturing Stage Cost Optimization Enablers

Three enablers can be implemented for cost opti-mization at the manufacturing stage.

• Optimize: Planning and approaching the man-ufacturing phase using optimization tools for raw materials optimization as well as costing and supply chain optimization.

• Control: Use of quality control techniques such as Six Sigma and Lean manufacturing to effectively reduce quality-related issues of the devices produced.

• Produce: Working with contract manufac-turers by sourcing design, manufacturing or delivery to OEMs.

The integration of manufacturing with product development using analytics and big data helps designers to take corrective and preventive actions based on inputs from manufacturing engineers.

Post-Market StageProduct development companies spend billions of dollars5,6 on product recalls (warranty- or compliance-based). The cost of poor quality affects the image of companies and erases profit

earned - due to litigation costs. Poor quality also endangers the lives of customers.

Companies face huge warranty-, compliance- and safety-related issues, while automotive companies regularly face warranty and safety issues with regard to various components. The causes:

• Inefficiency and a lack of proactive vigilance.

• Ineffective root-cause analysis.

• Incomplete addressing of issues.

Figure 6

Cost Optimization Enablers: Manufacturing Phase

� Raw Material Optimization

� Real-time Should-costing

� Supply Chain Optimization

Optimize Control Produce

� Six Sigma

� Poka Yoke

� Lean

� SPC

� ContractManufacturing

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• Aging complaints and corrective actions preventive actions (CAPAs).

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Integrated QMS and PDLC

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ProductRealizationValue Chain

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(between the design and quality departments, for example). As a result, quality issues at the design level can be addressed very early in the process.

Other benefits of IQMS include improved organi-zational effectiveness, successful new product implementation (NPI), increased compliance, reduced internal and external failure costs, and reduced quality prevention and quality assurance costs.

Automotive companies are now looking at an integrated approach to collecting vehicle infor-mation using Internet of Things (IoT) technolo-gies and connected vehicles to actively monitor and advise customers on likely quality issues.8

Going BeyondAny cost optimization inititiative without the ability to view cost metrics is of little use. Enter-prises typically do not visualize the benefits and ROI of a cost optimization drive in a holistic way. A single source of truth is necessary for analyzing or viewing product costs, which can be obtained through a cost analytics and reporting tool that can communicate insights holistically across the organization or enterprise.

An enterprise cost reporting system should be able to:

• Interact with all enterprise systems and departments.

Post-Market Cost Optimization Enablers

In our view, an integrated quality management system (IQMS) can bring additional organiza-tional effectiveness. Companies that have imple-mented IQMS achieve better on-time shipment rates.7 Moreover, integration adds quality control from conception through post market, and brings more effective cross-functional colloboration

Quick Take Product Cost Management at a Global Automotive Major

• Business situation: A global automotive major was using a traditional system of product costing:

> CAD data moved between various depart-ments such as design, sourcing and costing through e-mails.

> Engineers did not have the most recent CAD data for costing purposes.

> Delays in design changes due to cost over-runs led to product rollout delays.

• Solution: A CAD-based costing tool was developed that automatically generates tool and manufacturing costs based on recognition of CAD features.

• Benefits: As a result of an integrated PLM/CAD costing tool, the automotive major now can:

> Get real-time cost information at every stage of the product lifecycle.

> Broadcast a single source of truth with re-gard to cost.

> Negotiate better with vendors with regard to parts costs.

> Help designers know the cost implications of their designs, instantly.

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• Track and analyze cost for various product configurations.

• Report cost data across the enterprise.

• Search and identify various cost components with ease.

Looking ForwardAny effective product cost optimization program is an amalgamation of effective people using efficient tools to drive effective processes. Product cost optimization is a by-product of efficiency and a never-say-die attitude embedded in an organization’s culture.

Post-Market-Activity Categories

Figure 8

Proactive Vigilance & Reporting Reactive Vigilance

Competitor-Initiated Product Recall,MAUDE Database Regulator-Initiated Recall

Competitor-Related Quality Incidents Quality Incidents

Recall Report Analysis Adverse Event

Today’s product teams focus not only on products’ features and their performance, but also on cost, safety, environmental and regulatory compliance, sustainability, quality and other factors that impact the design process.

Continuous monitoring of cost across the product realization value chain can help identify key cost drivers. With this knowledge, a product design team can deploy control measures to optimize costs through multiple tools and methodologies, as discussed in the paper. Enterprise product cost optimization brings the focus on creating a win-win outcome for product development companies and their suppliers, at an optimum cost for customers.

Enterprise Cost Reporting

Figure 9

Source: http://resources.apriori.com/h/

Product LifecycleManagement (PLM)

Enterprise ResourcePlanning (ERP)

Supply ChainManagement (SCM)

Material RequirementsPlanning (MRP)

PRODUCT COSTMANAGEMENT

ENGINEERING

COST REPORTING

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KIN

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COST ANALYSIS

MANUFACTURING

RESEARCH &DEVELOPMENT

SOURCING

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Footnotes1 http://www.cognizant.com/InsightsWhitepapers/a-PLM-accelerator-for-socio-interactive-automotive-

product-development-codex1204.pdf.

2 http://www.plm.automation.siemens.com/en_us/products/teamcenter/systems-engineering-soft-ware/.

3 “Design determines 70% of cost? A review of implications for design evaluation.” J.A. Barton, D.M. Love and G.D. Taylor.

4 http://www.plm.automation.siemens.com/en_us/products/open/geolus/.

5 http://www.catlin.com/flipbook/product-recall/files/inc/382055422.pdf.

6 “The Business Case for Medical Device Quality,” by Ted Fuhr, Katy George, Janice Pai, www.mckinsey.com.

7 “Network Polymers and IQMS: A Mixture of Success,” http://www.iqms.com/company/network-polymers/.

8 “Design for Manufacturing’s Internet of Things,” https://www1.cognizant.com/content/dam/Cognizant_Dotcom/article_content/Services/Designing-for-Manufacturings-Internet-of-Things.pdf.

Reference

• Requirements Management: The Interface Between Requirements Development and All Other Systems Engineering Processes, by Colin Hood, Simon Wiedemann, Stefan Fichtinger, Urte Pautz; Springer, 2008.

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Quick Take Proactive Vigilance in Medical Device Quality Management Systems

A working QMS is part of reactive vigilance which triggers a chain of events back to the PDLC only when quality issues are discovered in the product.

Medical device companies should be encouraged to go a step further and be proactive in following

product recalls or incidents of other competitors in the same product category.

Such vigilance would provide valuable learning that would ensure quality issues are addressed proactively.

About CognizantCognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process outsourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satis-faction, technology innovation, deep industry and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 100 development and delivery centers worldwide and approximately 221,700 employees as of December 31, 2015, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world.

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About the AuthorsMadan Unde is a Senior Manager of Projects within the Product Engineering Practice of Cognizant’s Engineering and Manufacturing Solutions business unit. He has over 13 years of engineering industry experience, working in both the engineering OEM as well as engineering services spaces. Madan’s work experience spans industries, from life sciences (medical devices), high-technology, and industrial automation, through machine tools. In these roles, he has provided solutions across various technical areas such as product design and development, innovation (front end), continuous improvement, complaints investigation and resolution, CAPA management and product sustenance engineering. Madan is well acquainted with medical devices standards such as ISO13485 and ISO14971, and practices such methods as Six Sigma, DFx, risk management, and value engineering/value analysis and problem solving. Madan holds a mechanical engineering and industrial engineering post graduate degree from the University of Pune and is a certified Six Sigma Black Belt Professional (MSME India) and Value Engi-neering Associate Value Specialist (SAVE & INVEST). He can be reached at Madan.Unde@cognizant.com.

Murali Krishna is a Manager of Projects within Cognizant’s Engineering and Manufacturing Solutions business unit. He has 11-plus years of experience in building CAD-to-CAE and CAD-to-CAM data interop-erability solutions, product cost management, geometric search and knowledge-based engineering solutions. Murali has vast experience in creating frameworks for automotive design verification and validation. He has worked extensively with automotive, high technology and manufacturing companies to provide the best solutions for their data interoperability needs. Murali has authored technical papers in the fields of CAD, CAM and CAE. He has a bachelor’s degree in mechanical engineering from College of Engineering, Guindy. Murali can be reached at Muralikrishna.AV@cognizant.com.