Manufacturing

InsideBIGDATA Guide to

Big Data for Manufacturingby Daniel D. Gutierrez

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Guide to Big Data for Manufacturing

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Manufacturing concerns consistently have sought ways to reduce waste and variability in their production processes to dramatically improve product quality and yield (e.g. the amount of output per unit of input). Further, these companies need a granular approach toward recognizing and correcting manufacturing process flaws. Big data technology provides just such an approach and many high-tier manufacturers possess a significant degree of interest and motivation in adopting the big data technology stack. Big data analytics refers to the application of tools based on principles of computer science, statistics, data mining algorithms and mathematics to enterprise data assets with the fundamental goal to assess and improve business practices. In manufacturing, operations managers can use big data to drill down into historical process data, discover previously unidentified knowledge among discrete process steps and inputs, and then optimize the factors that are shown to have the greatest effect on yield. Many manufacturers across a broad range of industries now have an abundance of real-time shop floor data and the capability to conduct sophisticated statistical learning assessments. They are taking previously siloed data sets, aggregating and joining before analyzing them to reveal key insights.

Even when considering manufacturing operations that are thought to be best in class, the use of big data may reveal further opportunities to increase yield above industry benchmarks. In addition, companies can reduce their waste of raw materials, reduce energy costs and thus increase profitability by rigorously assessing production data, all without having to make additional capital investments or implementing major change initiatives.

The essential first step for manufacturers that want to use big data to increase yield is to consider how much data the enterprise has at its disposal. Most manufacturers collect vast troves of process data but typically use them only for tracking purposes, not as a basis for improving operations. The challenge is for these players to invest in the systems and skill sets that will allow them to enhance their use of existing process statistics. For example, it might be prudent to index information from multiple sources so it can be analyzed more easily and hire data scientists who are trained in identifying patterns and drawing actionable business insights from the data.

Big Data for Manufacturing – An Overview

ContentsBig Data for Manufacturing – An Overview . . . . . . . . . . . . . . . . . . . . . . . . . 2Common Big Data Pain Points for Manufacturers . . . . . . . . . . . . . . . . . . . . 5Big Data Technology for Manufacturing . . . 6

Analytics Layer . . . . . . . . . . . . . . . . . . . . . . 6Data Integration Layer. . . . . . . . . . . . . . . . 7Data Management Layer . . . . . . . . . . . . . 7

Infrastructure Layer . . . . . . . . . . . . . . . . . . 7Professional Services . . . . . . . . . . . . . . . . . 7Big Data Technology Stack for Manufacturers . . . . . . . . . . . . . . . . . . . 8

Adopting Big Data for Manufacturing . . . . 9

Case Study: Omneo . . . . . . . . . . . . . . . . . . 11

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Most manufacturers collect vast troves of process data but typically use them only for tracking purposes, not as a basis for improving operations.

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Some manufacturers, particularly those with lengthy production cycles, have too little data to be statistically meaningful when put under a data scientist’s magnifying glass. The challenge for thought leaders at these companies will be taking a long-term focus and investing in systems and practices to collect more data. They can invest incrementally, e.g. gathering information about one particularly important or particularly complex process step within the larger chain of events, and then applying sophisticated analytics to that part of the process.

Big data is becoming a strong competitive advantage in the manufacturing industry. In fact, big data savvy is nearly a necessity these days because many companies feel if they don’t adopt a data-centric business strategy it could affect their competitive landscape in the next 3-5 years. As a result, manufacturing businesses, across a wide range of categories and verticals, are exploring ways to adopt big data.

Here are just a few areas of interest for big data under the manufacturer’s prism:

• Manufacturers are seeing a higher degree of visibility into supplier quality levels, and improved accuracy in predicting supplier performance over time. Using big data, manufacturers are able to view product quality and delivery accuracy in real-time, making trade-offs on which suppliers receive the most time-sensitive orders.

• Selling only the most profitable customized or build-to-order configurations of products that impact production the least. For many complex manufacturers, customized or build-

to-order products deliver higher-than-average gross margins yet also costs exponentially more if production processes aren’t well planned. Using big data, manufacturers are discovering which of the myriad of build-to-order configurations they can sell with the most minimal impact to existing production schedules to the machine scheduling, staffing and shop floor level.

• Measuring compliance and traceability to the machine level becomes possible. Using sensors on all machinery in a production center provides operations managers with immediate visibility into how each is operating. Big data can also show quality, performance and training variances by each machine and its operators. This is invaluable in streamlining workflows in a production center, and is becoming increasingly commonplace.

• Quantify how daily production impacts financial performance with visibility to the machine level. Big data is delivering the missing link that can unify daily production activity to the financial performance of a manufacturer. Being able to know to the machine level if the factory floor is running efficiently, production planners and senior management know how best to scale operations. By unifying daily production to financial metrics, manufacturers have a greater chance of profitably scaling their operations.

Big data savvy is nearly a necessity these days because many companies feel if they don’t adopt a data-centric business strategy it could affect their competitive landscape in the next 3-5 years.

Big data can also show quality, performance and training variances by each machine and its operators. This is invaluable in streamlining workflows in a production center, and is becoming increasingly commonplace.





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For an important perspective on this new found awareness, the “Industrial Internet Insights Report for 2015” sponsored by GE and Accenture considers the rise of the “Industrial Internet,” the combination of big data analytics with the Internet of Things (IoT), and how it is producing huge opportunities for manufacturers. The report includes a number of telling highlights for how manufacturers see big data affecting their bottom lines:

1. 67% of manufacturers report strong board-level support as primary influencer for big data initiatives.

2. 87% of manufacturers report big data is one of the top three priorities.

3. By introducing big data analytics and more flexible production techniques, manu-facturers could boost their productivity by as much as 30%.

4. 70% of manufacturers report interest in using big data to “gain insights into customer behaviors, preferences and trends”—more than any other industry.

The goal for this Guide is to provide strategic direction for enterprise thought leaders in the manufacturing sector for ways of leveraging the big data technology stack in support of analytics proficiencies designed to work more independently and effectively in today’s climate of striving to increase the value of corporate data assets.

Source: Accenture

Top business priorities by industry shows manufacturing sector’s desire to gain insights.

70% of manufacturers report interest in using big data to “gain insights into customer behaviors, preferences and trends”—more than any other industry.





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With the rise of big data, global manufacturers recognize the importance of data as the new currency and as a competitive differentiator. Data is being created and consumed at rates never before seen and this effect especially is prevalent in the manufacturing sector. With this data explosion, manufacturers are recognizing the need for more than traditional, structured systems to take control of their data. But there are significant pain points in getting up to speed to take advantage of the data deluge. For instance, many manufacturers don’t have access to the tools that they need for deploying big data solutions—to get results faster, to perform required calculations—regardless of what they’re manufacturing.

At a high-level, many manufacturers share common pain points when undergoing a big data initiative. Here are a few of most frequently mentioned:

• Needing to crunch more data in less time Keep your organization ahead of the data deluge so that your decisions are based on information from the analytics. Even the most advanced big data solution will not benefit an organization if it takes too long to get insights.

• Ensuring the right people have access to big data results – If the right people do not have access to the right tools to deliver insights, it will not matter how much data you have.

• Effectively handling data quality and performance – The goal must be to build a big data infrastructure that aligns with your business goals and delivers actionable, real-time business insights that you can trust.

• Needing big data solutions that scale to fit your business – To get the right insights into the right hands at the right time, you must have a flexible, scalable big data infrastructure that can reliably integrate front-end systems with back-end systems—and keep your business up and running.

• Being able to expand your company’s data handling strategy by having the ability to analyze various types of data including traditional structured data, but also newly available unstructured and semi-structured data sources.

If a manufacturer wants to get started with big data to address their own set of pain points and build a use case in their environment, one good path toward success is the Dell QuickStart for Cloudera Hadoop program which offers an effective point-of-entry for enterprises to begin managing and analyzing data. An all-in-one system designed to reduce the complexity of deploying, configuring, and managing Hadoop systems, includes the hardware, software and services needed to deliver a Hadoop cluster that will start organizations on a proof-of-concept to begin working with big data. With this program, Dell is building on its deep expertise and relationships in working with Cloudera® and Intel®. The solution represents a unique collaboration in the big data ecosystem to collectively deliver an easy and affordable way to get started with Hadoop.

Dell QuickStart for Cloudera Hadoop enables organizations to quickly engage in Hadoop testing, development and proof of concept work. Through the combination of Dell PowerEdge servers with Intel® Xeon® processors, Cloudera Enterprise Basic Edition and Dell Professional Services, organizations can quickly deploy Hadoop and enable development and application teams to test business processes, data analysis methodologies and operational needs against a fully functioning Hadoop cluster.

Common Big Data Pain Points for Manufacturers





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In order to consider big data solutions for manufacturing in a holistic manner, the following diagram divides up big data into four primary components—analytics, data integration, data management, and infrastructure. In addition, Dell provides state-of-the-art big data solutions in part with the help of strategic partners like Cloudera, Intel, Microsoft, Oracle, and SAP along with its professional services arm to ensure a quality big data deployment that can address enterprise data processing requirements at scale.

Analytics LayerWith analytics, the strategy is to turn enterprise data assets into better and faster actionable insights through managing data, processing data, storing data, and eventually analyzing data.

For analytics, Dell’s Statistica for Big Data Analytics platform is a good option as it extends the Statistica portfolio with advanced natural language processing (NLP), entity extraction, interactive visualizations and dashboards, and distributed advanced analytic models across Hadoop, databases and database appliances. The unique aspect of Statistica is that it has over 4,000 different models created for different vertical markets, including some for manufacturing. The attractiveness of Statistica is that it has these models created so manufacturers will not have to start from the ground up to build models to analyze their data, whether it’s a model for manufacturing efficiency, or a model to look at failure points within a product.

Big Data Technology for Manufacturing





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Data Integration LayerIn building a solution focused on data analytics, a manufacturer must first take the steps necessary to implement the end-to-end solution. One key driver is how to integrate the data into a suite of big data tools in order to manage it, process it, store it, and analyze it. For data integration, Dell solutions like Boomi and Shareplex resonate in a significant way. Boomi is a data integration platform and is unique in that it allows you to integrate any type of data source (e.g. log data, social media data, sensor data, machine data, etc.) into an application, plus you can integrate that data source on premise or in the cloud. The Shareplex Connector for Hadoop is another important tool which is key here because most of the time the first step enterprises take with big data is to integrate their existing data assets from a relational database or enterprise data warehouse. Shareplex enables you to easily load and continuously replicate changes from an Oracle database to a Hadoop cluster. The manner in which the data transfer takes place is not simply a big download, but rather with Shareplex you replicate the Oracle data directly into Hive or HBase and HDFS environments, essentially streamlining this cumbersome task.

Data Management LayerWhen building your big data solution, at an early point in the journey you must determine where you will put the data so that you can then manage the various types of data that will be a part of your eventual analysis. When dealing with diverse types of data, one option for the management layer is to deploy Hadoop as your data management platform. Hadoop allows you to collect, manage, analyze and store data in a scalable, flexible and cost effective solution. The reason this is key and the reason Hadoop is recommended is because Hadoop allows you to store the data in its native format. One of the biggest problems today with a relational database is before you can actually store data in the database you must clean it, parse it, and make it fit in a table, row, and field within the database. With Hadoop, you will not have to do any of that initial work. You can deliver the data as is and eventually when you want to start analyzing it that’s when you begin the cleaning process. This is why enterprises like it, where organizations find

the value in Hadoop, because whether that data is structured, unstructured, or semi-structured, you can put it in Hadoop today. Hadoop is the tool of choice for the data management layer.

Infrastructure LayerSuccessful big data deployments depend on reliable hardware infrastructures. The Dell PowerEdge R730xd, based on Intel® Xeon® processor tech-nology, offers an exceptionally flexible and scalable, two-socket 2U rack server that delivers high performance processing and a broad range of workload-optimized local storage possibilities, including hybrid tiering. This is a hardware solution ideal to run the Hadoop distributed computing platform for a solution to big data problems.

Additionally, Dell, together with Cloudera and Intel, provides a turnkey, purpose built in-memory advanced analytics data platform. The Dell In-Memory Appliance for Cloudera Enterprise represents a unique collaboration of partners within the big data ecosystem. Together Dell, Cloudera and Intel deliver both the platform and the software to help manufacturers capitalize on high-performance data analysis by leveraging the Cloudera Enterprise in-memory features (Apache Spark) for interactive analytics and multiple types of workloads. Cloudera Enterprise also features Impala for fast query and Cloudera Search for interactive search. The result—one tool for both processing and analytics.

Professional ServicesIf a manufacturing firm needs professional services to come in and build the entire solution, deploy and integrate, essentially build a solution from the ground up, Dell has the big data services to deliver that to you. In addition, there are the Dell Solution Centers— if you’re new to this technology and you don’t know how it will act in your environment, you can go to a Dell Solution Center to do a proof of concept without making a big investment—Dell offers this as a free service to their customers. Finally, Dell Financial Services can package up new and creative ways to finance these types of solutions so you don’t have to take it out of your capital expenditures but instead make it an operating expense.





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Big Data Technology Stack for ManufacturersThe available technology stack for applying a big data methodology to manufacturing applications is growing by leaps and bounds. Here are the top level components of a big data initiative.

• Big Data Software – the early focus is on end-to-end solutions for big data starting with integral software applications like Statistica for Big Data Analytics. With this content mining and analytics solution, you’ll transform complex and time-consuming manipulation of web-scale data resources into a fast and intuitive process. You can harvest sentiments from Twitter feeds, blogs, news reports, CRM systems, and other sources, and combine them with demographic and regional data to better understand market traction and opportunities.

• Hadoop – In order for a manufacturing firm to extract value from an ever-growing onslaught of data, the organization needs next-generation data management, integration, storage and processing systems that allow the company to collect, manage, store and analyze data quickly, efficiently and cost-effectively. The Hadoop distributed computing platform provides end-to-end scalable infrastructure, leveraging open source technologies, to allow you to simultaneously store and process large datasets in a distributed environment for data mining and analysis, on both structured and unstructured data.

Hadoop is scalable, fault-tolerant and distributed. The open source software was originally developed by the world’s largest Internet companies to capture and analyze the massive amounts of data they generate. Now, manufacturing companies are climbing aboard the big data bandwagon with Hadoop as their chosen architecture. Unlike earlier platforms, Hadoop can store any kind of data in its native format and be used to perform a wide variety of analyses and transformations on that data.

Using Dell™ | Cloudera® Apache™ Hadoop® solutions for big data, Dell offers three ways to initiate your journey: deployment of the Dell QuickStart for Cloudera Hadoop packaged solution, exploration of Hadoop software via a Dell Solution Center and on-premises work with a fully functioning Hadoop environment via the Dell Hadoop Pod Loaner Program.

• Security for Big Data – Security is a key component for all big data projects. All solution designs must encompass performance, access, compliance and security. Security should be defined at all levels of the system implementation and account for both at-rest and in-flight data. Big data systems introduce new challenges for security that must be accounted for including data plus data policies and the handling of documents that may contain multiple levels of security. Big data projects are unique and should be carefully crafted and designed beginning with a use case definition and then allowing teams to work with low risk data as the focal point to enable organizations to become comfortable with new technologies as well as to determine how best to ensure the solution can be implemented to conform to corporate security policies.

Compliance is an imperative part of the security of data. Strong tools must be deployed as part of any big data solution to ensure that all data access and use can be reported on, and alerts generated for inappropriate data access. As data sets become more complex and more disparate data sets are integrated, ensuring compliance will become more difficult, but can be managed if data is integrated in steps, rather than all at once.

One good option for a manufacturer to employ for securing their big data solution is Dell SecureWorks, an information security service organization. Dell SecureWorks helps organizations worldwide protect their IT assets, comply with regulations and reduce security costs. These managed security services clients range from small local manufacturers to global industry leaders.





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The big data revolution is dramatically changing the manufacturing industry. The following driving factors have created opportunities for growth and have motivated the need for manufacturers to collect, store and analyze massive volumes of data—leading to the adoption of big data technology:

• Product quality/defect tracking

• Supply planning

• Manufacturing process defect tracking

• Supplier/supplier components/parts defect tracking

• Collecting supplier performance data to inform contract negotiations

• Forecasting of manufacturing output

• Increasing energy efficiency

• Simulation and testing of new manufacturing processes

• Enabling mass-customization in manufacturing

In realizing the above benefits, many manufacturers have implemented practices using big data including: log aggregation, monitoring, analysis and reporting; integrating sensors or embedding a log inventory system with machines on the manufacturing floor; this has enabled them to gain an understanding for what the machines are doing —allowing the data-driven organizations to grow, protect and bring added value to their business.

The “Maturity Pyramid” diagram below is a good way to visualize the big data adoption process. We can see that every new big data adoptee starts at a different spot, i.e. every business has different tools, every business has a unique set of skills. With the maturity pyramid you need to take an executive approach to this realization, e.g. a consultative approach to the process as it is important to have clarity for where the company is positioned. The maturity pyramid is a good ground point for manufacturers to understand how to get to analytics.

Adopting Big Data for Manufacturing

An example of a successful big data adoption in manufacturing is Koehler Paper Group. The company streamlined their data warehouse systems, reducing costs by 30%, giving managers and executives up to the minute monitoring of manufacturing processes, allowing for much faster and smarter decisions to be made. The new system cuts data loading times from five minutes to five seconds. Koehler worked with Dell to deploy a new SAP HANA database solution on Dell PowerEdge servers with Intel® Xeon® processors in just three days.

Koehler Paper Group





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Recent findings from a survey completed by LNS Research and MESA International—Attitudes on How Big Data will Affect Manufacturing Performance—include where big data is delivering the greatest manufacturing performance improve-ments today. The highlights from this survey include the following top three areas that big data can improve manufacturing performance: better forecasts of product demand and production (46%), understanding plant performance across multiple metrics (45%) and providing service and support to customers faster (39%).

As a clear indication for how solidly Dell is behind the big data adoption of big data solutions, Dell boasts several significant results! Dell, as a large manufacturing company, is taking this big data journey as well, and the company can speak to how it has achieved its goals, how the company built its big data solutions, where it failed, and what it will implement next time.

Dell is adopting big data solutions together with internal IT teams in order to enact the following enterprise initiatives:

• ETL offload• SAP HANA deployment

• SAS to Statistica migration• Dell works with customers in its Solution

Centers to help them leverage Dell’s own experience with building big data solutions

• Dell’s own metrics demonstrates success when adopting big data (see chart below)

Dell has a great deal of expertise in building big data reference architectures, in that the company built the first custom designs for big data platforms as early as 2009. Dell has been in the big data space for that length of time, since 2009 with the platform and the first reference architecture in 2011.





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For a compelling example that illustrates how big data is affecting the manufacturing sector, we can consider Omneo, a provider of supply chain management software for manufacturing com-panies. The business need was to enable global manufacturers to efficiently manage product quality/performance and customer experience. Consequently, Omneo needed to collect, manage, search and analyze vast amounts of diverse data types, and it sought the right software and hardware infrastructure to support this effort. The organization worked with Dell and Cloudera to build a software solution on top of the Cloudera® of Distribution Hadoop® (CDH) platform running on a cluster of Dell PowerEdge C8220 servers with Intel® Xeon® pro-cessors, giving customers total product data visibility throughout their entire supply chain.

The benefits that the Omneo solution offers to manufacturers are:

• Enables global-brand owners to manage product performance and customer experience

• Delivers a 360-degree view of supply chain data

• Searches billions of data records in less than three seconds

• Scales to support 300 million records every month

• Allows customers to quickly search, analyze and mine all their data in a single place so that they can identify and resolve emerging supply chain issues

• Helps manufacturers and suppliers detect emerging issues

• Searches billions of data records in less than three seconds

• Scales to support 300 million records every month

• Saves millions of dollars and boosts productivity

• Improves product quality, performance, customer experience and compliance

“We are able to help customers search billions of records in seconds with the Dell infrastructure and support, Cloudera’s Hadoop solution, and our knowledge of supply chain and quality issues,” says Karim Lokas, senior vice president of marketing and product strategy for Omneo, a division of the global enterprise manufacturing software firm Camstar Systems, now a wholly-owned subsidiary of Siemens. “With the visibility provided by this solution, manufacturers can put out more consistent, better products and have less suspect product go out the door.”

Case Study: Omneo A division of global enterprise manufacturing software firm Camstar Systems, now a wholly-owned subsidiary of Siemens





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SummaryIn this Guide we have delivered the case for the benefits of big data technology applied to the needs of the manufacturing industry. In demonstrating the value of big data, we included:

• An overview of how manufacturing can benefit from the big data technology stack

• A high-level view of common big data pain points for manufacturers

• A detailed analysis of big data technology for manufacturers

• A view as to how manufacturers are going about big data adoption

• A proven case study with: Omneo

Dell Statistica offers an extensive portfolio of solutions including a complimentary online resource and textbook on statistics that me be found at http://www.statsoft.com/textbook.

Additionally for manufacturing we offer 15 subcategories of scenarios and solutions outlined at: http://www.statsoft.com/Solutions/Manufacturing

Intel, the Intel logo, Xeon, and Xeon Inside are trademarks or registered trademarks of Intel Corporation in the U.S. and/or other countries.



http://www.statsoft.com/textbook

http://www.statsoft.com/Solutions/Manufacturing

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