A CEP Preprint Update © 2012 AIChE

Michelle Bryner Smart Manufacturing: The Next Revolution elcome to the chemical plant Moving to the next generation of production can help the Wof the future, where self-aware heat exchangers, distillation columns, U.S. keep its competitive edge. chemical reactors, motors, and pumps work in concert, wirelessly communi- what can be accomplished with next- ply chain teams to respond quicker to cating their status with each other to generation manufacturing, also called customer needs and disruptions in the produce the highest-quality product, smart manufacturing (Figure 1). market place,” Kotanchek says. “Part in the least amount of time, with near- “As smart manufacturing technol- of our challenge in the future will be zero incidents, emissions, and waste. ogy develops further, we will see an to design our manufacturing assets The plant is intimately connected explosion in the amount of informa- with the right capabilities and flex- with upstream and downstream tion available to our manufacturing ibility to take full advantage of these processes, such that it can respond in plants and extended supply chains,” emerging benefits,” she says. real time to market shifts, customer says Theresa Kotanchek, Vice Presi- The goal of the smart manu- demand, global economics, and politi- dent, Sustainable Technologies and facturing movement is to create cal and socioeconomic activities. Innovation Sourcing at Dow Chemi- knowledge-embedded manufacturing Constantly changing and adapting, cal. “This information will allow operations. Today’s operations will be the process learns and adjusts. our product designers, developers, transformed from reactive to proac- Glimpses of this vision exist. For purchasing, manufacturing, and sup- tive, response to prevention, compli- example, two years ago, Tata Motors made headlines when it introduced the Nano — the world’s cheapest sedan, with a sticker price in the Indian market of $2,500. This ridicu- lously low price was not the result of cheap labor and shoddy workman- ship, but instead could be attributed to the company’s “smart” factory in Gujarat and its out-of-the-box innovation. Tata’s Gujarat factory employs the latest automation technologies — sensors, microprocessors, and motor control devices — giving it the brains to predict bottlenecks and break- downs before they occur, and the ability to get parts from suppliers in real time to meet orders. The factory also houses smart technology that traces and tracks each component of a customer’s Nano back to its source. The traceability program minimizes the number of car recalls when a p Figure 1. Advanced automation and control is at the heart of smart manufacturing. This multi­ faulty product is identified. purpose system synchronizes and monitors processes in a bottling plant. Image courtesy of The Nano is just one example of Rockwell Automation.

4 www.aiche.org/cep October 2012 CEP ance to performance oriented, tactical for Information Technology and CTO and four high-performance comput- to strategic, and local to global. The at the Univ. of California, Los Angeles ing centers. It comprises companies pervasive application of networked (UCLA) and a professor of chemi- across a broad spectrum of indus- information and knowledge through cal and biomoleculer engineering at try, including: batch, discrete, and analytics, modeling, and simulation UCLA. “We are further interested in a continuous process manufacturers; dynamically integrates the demands high level of responsiveness to market chemical, pharmaceutical, and materi- of the customer throughout the entire shifts, customer demand, global als producers; automation providers; supply chain — enabling real-time economics, and political and socio­ and power generators, among others. response to changes in raw material economic factors.” “We sat down with a good cross- costs, fluctuations in volume demand, Over the past few years, several section of companies, and we identi- and custom orders, while minimizing initiatives aimed at modernizing fied common objectives that were energy and material usage and maxi- manufacturing in the U.S. have driving manufacturing in very new mizing environmental sustainability, materialized. Industry, academia, and directions,” says Davis, leader of the health and safety, and economic government entities have formed the SMLC steering committee. “We then competitiveness (Figure 2). Smart Manufacturing Leadership took a look at the roles of information “We are interested in the capabil- Coalition (SMLC), for instance, to technology and the rapidly expanding ity, tools, and infrastructure that ensure identify objectives, secure funding, capabilities of cyber infrastructure; that processes are seeking, at every and develop the IT platform that will that’s where smart manufacturing, the instant in time, the optimum delivery support smart manufacturing. The terminology, emerged,” he explains. of the best possible product without coalition consists of 25 global compa- “If you’re going to address all of interruption, incident, or cause for nies, eight manufacturing consortia, these in a comprehensive manner, you alarm,” says Jim Davis, Vice Provost six universities, one government lab, need to address something that we call manufacturing intelligence, which means having the right information wherever you need it, whenever you An agile, demand-driven supply need it, and in the form that is useful chain operates with higher product Business management is availability and minimal inventories. closely linked with supply and actionable,” Davis continues. The chains and factories to optimize performance. SMLC has identified actions needed Suppliers to achieve this vision (Figure 3). Smart interconnected SMLC is one of several U.S. systems create agile factories Business that can rapidly respond to Systems initiatives working on next-generation changes in products and manufacturing. Government-funded customer demands. entities, including the National Smart Institute of Standards and Technol- Factory ogy (NIST) and the National Sci- Plant operators use knowledge-based applications ence Foundation (NSF), are actively to make decisions that involved in smart (or advanced) Smart optimize production and Grid minimize costs. manufacturing. The President’s Council of Advisors on Science and Distribution Real-time information flows Centers between factories and Technology (PCAST) prepared a Smart factory systems take distribution centers to report entitled “Ensuring American advantage of smart grid streamline product technology to optimize electricity Leadership in Advanced Manufactur- movement. use. ing,” which provided an overarching Products are tailored to the strategy along with specific recom- Customers customer, traceable mendations to revitalize U.S. leader- throughout their service life, and recycled, reengineered, ship in advanced manufacturing. The or remanufactured. Advanced Manufacturing Partnership (AMP) was formed to work with the p Figure 2. The future manufacturing plant will be interconnected with suppliers, distributors, National Economic Council and the customers, and business systems via information technology. Source: SMLC. Office of Science and Technology

CEP October 2012 www.aiche.org/cep 5 Update

Policy to implement the recommen- that describe such parameters as Achieving the smart manufactur- dations in the PCAST report. sustainability, economic competitive- ing vision will be a significant under- Simply put, smart manufacturing ness, energy consumption, and level taking and will require the build-out can be dissected into four main ele- of health and safety (unlike traditional of a new IT infrastructure, Davis says. ments: automation and control, models performance metrics, which are based Consensus is building around the need and simulation, a skilled workforce, on output/input productivity). for a standard, pre-competitive infra- and key performance indicators. structure for deploying multiple, smart The automation and control piece Automation and control manufacturing systems. Providing this involves sensors and microprocessors Automation and control technolo- infrastructure as a shared platform will on each piece of equipment, comput- gies are not new to manufacturing lower the barriers (e.g., cost, complex- ing hardware and software to record industries. Sensors, computer hard- ity, ease of use) for implementing and analyze data, and technology to ware and software, and actuator tech- sensor-measurement-driven modeling connect sensors to one another as well nologies have been used on individual and simulation, which in turn makes as to connect one plant floor to other pieces of equipment and isolated these technologies accessible to small- plant floors within a supply chain, all portions of processes for decades. To and medium-sized companies. in real-time. Modeling and simulation reach the smart manufacturing vision, In the past, different control sys- refers to operating models that interpret however, information technology (e.g., tems were used for different applica- and make sense of the collected data, data management and modeling) must tions. For instance, discrete processes and provide useful and actionable be pervasive and integrated through- such as those used in the automotive information for design, planning, out the multiple layers of operation industry relied on programmable logic production, etc. The human element and decision-making (e.g., sensors controllers (PLCs). PLCs work well (i.e., skilled workforce) refers to an and actuators, operators running the for discrete processes but do not have operational workforce (those running process, supply chain, etc.), and be the capability to handle continuous the processes) with advanced training extended across multiple process units processes. Instead, distributed control and skills. And the key performance within a factory, as well as across an systems (DCSs) are typically used for indicators (KPIs) are measurements entire factory and supply chain. continuous processes. The develop-

Industrial Community Modeling and Simulation Platforms for Smart Manufacturing

1. Create community platforms 2. Develop next-generation 3. Integrate human factors and 4. Expand availability of (networks, software) for the toolbox of software and decisions into plant optimization energy-management decision virtual plant enterprise computing architectures for software and user interfaces tools (energy dashboards, manufacturing decision-making automated data feedback systems, energy apps for mobile devices) for multiple industries and diverse skill levels

Affordable Industrial Data-Collection and Data-Management Systems

5. Establish consistent, efficient data methods for all industries 6. Develop robust data-collection frameworks (sensors/data fusion, (data protocols and interfaces, communication standards) machine and user interfaces, data recording and retrieval tools)

Enterprise-Wide Integration: Business Systems, Manufacturing Plants, and Suppliers

7. Optimize supply-chain performance 8. Develop open-platform software and 9. Integrate product and manufacturing through common reporting and rating hardware to integrate and transfer data process models (software, networks, virtual methods (dashboard reports, metrics, between small and medium enterprises and real-time simulations, data transfer common data architecture and language) (SMEs) and original equipment manufactur- systems) ers (OEMs) (data sharing systems and standards, common reference architectures)

Education and Training in Smart Manufacturing

10. Enhance education and training to build a workforce for smart manufacturing (training modules, curricula, design standards, learner interfaces) p Figure 3. The SMLC has identified 10 priority actions required to move smart manufacturing into the mainstream industrial sector.

6 www.aiche.org/cep October 2012 CEP ment of the programmable automation Modeling and simulation allows the assembly line to go as fast controller (PAC) changes this. Just as automation and control as possible and has the aerodynamics “Today, we have programmable technologies are not new to manufac- to keep the chip hugging the assem- automation controllers, which are turing, modeling and simulation are bly line,” Bernaden says. Then they able to do both — you can use the also not new. While much progress used these same aerodynamic models same general-purpose computer has been made in this area, modeling to determine the best method to apply system for applications in the process and simulation are still not ubiqui- the flavor dust to the chip while it was world that you can for an assembly tous throughout manufacturing, and moving at such high speeds. “With line,” says John Bernaden, corporate they are not often used for business $36,000 of computer time over about director at Rockwell Automation and and technical decision-making. In two weeks, they created a virtual vice chair of the SMLC. addition, modeling is rarely used next-generation assembly line,” Ber- In addition to the controllers during plant operation. The smart naden says. themselves, the automation and manufacturing vision incorporates Instead of one-off models, in control system will require a common modeling and simulation into the which a model is developed and used networking technology to enable sen- actual operation of the manufacturing for only one isolated project or pro- sors to talk with one another across a process. cess, the smart manufacturing vision plant floor and for one plant floor to “Model-based techniques, simu- calls for models to be accessible communicate with others in the sup- lation, and smart tools to manage across supply chains and industries, ply chain. While wireless networks information and process knowledge in the form of open-source soft- are extensively used for consumer are key elements in smart manufac- ware. The application of models to electronics, they were not designed turing,” says Yiannis Dimitratos, every aspect of manufacturing, from for the manufacturing environment. Technology Manager of the Process planning, design, and development “The factory floor is a pretty Dynamics and Control organization through operation, will be pervasive, demanding environment, both in at DuPont Engineering Research and coordinated, consistent, and man- terms of the noise — mechanical and Technology. aged. The objective of the models is electrical noise that interferes with the A plant that made Pringles potato to optimize a set of predetermined measurements that these sensors are chips (which at the time was owned performance metrics. making — and the need for real-time by Procter & Gamble) realized the deterministic operation,” says Fred benefits of modeling and simulation Skilled workforce Proctor, leader of the control systems during manufacturing when it built an In his book, Make It In America group at NIST. “For example, unlike additional process line to manufac- — The Case for Re-Inventing the video streaming for your laptop, where ture chips in a new flavor. Typically, Economy, Dow Chemical CEO an occasional glitch is fine, control this endeavor would take about three Andrew Liveris discusses the need operations on a factory floor require months and about $250,000, says for a skilled workforce to revitalize dead-on timing,” Proctor says. “In Rockwell Automation’s Bernaden. the U.S. manufacturing sector. “A the factory, if that timing is delayed a Because the company’s Pringles plants properly educated workforce is an little bit, or if on average it’s okay but were equipped with state-of-the-art issue of deep concern to business, there are periods where information automation and control technology, especially to manufacturers,” Liveris comes in really fast or really slow, that a lot of data on the existing Pringles writes. “Our worry isn’t just that our wreaks havoc on controls.” lines was available. The research and children are being educated poorly; In support of a standardized IT development team at P&G ran the data it’s that they’re being educated infrastructure, the SMLC is creating a through aerodynamics models and poorly in the subjects most relevant smart manufacturing platform (SMP) simulations using a high-performance to our economic well-being. There — a standardized toolkit of hardware, computer system. isn’t enough focus on science, tech- software, and network technologies “They obtained aerodynamic nology, engineering, or math in our that are accessible and adaptable models because these Pringles are schools.” across industries and processes. The literally flying down the assembly SMP can be thought of as a common line,” Bernaden says. (P&G made Key performance indicators operating system, allowing users to about a half-billion chips per hour.) Key performance indicators will download and use the same apps, “Using the big computer model, they allow companies to compare, share, compare and share data, and commu- discovered that a 47-degree angle and understand one another’s per- nicate easily with one another. [between the chip and the conveyor] formance metrics. Examples include

CEP October 2012 www.aiche.org/cep 7 Update

elements of cost, productivity, quality, energy, environment, sustainability, A Call to Action and other factors. While there are he Society of Manufacturing Engineers (SME) released a white paper last thousands of such indicators, their Tmonth, “Workforce Imperative: A Manufacturing Education Strategy,” that pro- meaning varies across companies and vides recommendations to increase the number of skilled manufacturing workers industries. in the U.S. “With an estimated 600,000 unfilled manufacturing positions across the “Performance is difficult to country, the shortage of advanced manufacturing engineers, technologists, and quantify,” NIST’s Proctor says. “We technicians is already impacting most industries,” SME says. “With the retirement have a good handle on measurements of much of the core workforce over the next 10 years, and new generations not like length and time; those are the prepared to — or even interested in — taking their place, the manufacturing indus- fundamental units that we have tools try is facing a crisis.” to measure. The harder thing is how The plan calls for academia and industry to work together to: to measure performance.” • attract more students into manufacturing • articulate a standard core of manufacturing knowledge NIST has initiated the Factory • improve the consistency and quality of manufacturing education Equipment Network Testing Frame- • integrate manufacturing topics into science, technology, engineering, and work project to address this need. The mathematics (STEM) education aim of the project is to develop a uni- • strategically deploy resources to accomplish these goals. fied testing framework that will enable “It is imperative that manufacturing is working hand-in-hand with education the development of new performance to properly train and educate both our current and future workforce,” says Mark measurement methodologies — bet- Tomlinson, SME executive director and CEO. “Importantly, we must communicate to our young people the tremendous opportunities that exist in manufacturing and ter ways to define and measure key then provide them with the educational foundation necessary to succeed,” performance indicators — and apply he says. them to factory equipment.

The smart ecosystem dynamic models for process optimi- Extending the technologies and Many of the smart manufacturing zation,” Kotanchek says. “The value concepts currently being applied at the technologies and concepts are being to this process automation is clear in plant level to the entire supply chain applied at the plant level and/or on terms of process safety, cost, yield, and multiple industries will require a isolated production lines. Most chemi- efficiency, quality, and reliability. But standardized infrastructure. In such an cal plants are equipped with automa- we realize there is still a lot of value ecosystem, small, medium, and large tion and control technologies that alert to be mined.” companies will use hardware and operators when the process is operat- In the smart manufacturing ecosys- software that are interoperable. They ing outside of preset limits. tem, this intelligence will be extended will all rely on compatible computers Dow Chemical, for example, uses beyond the automation and control and operating systems. The model- a proprietary process control system systems. “When the smart manufactur- ing and simulation software will be that it developed 30 years ago. Chem- ing vision is achieved, intelligence will accessible to all companies, which ical engineers at Dow developed the be not only in the automation system, can then customize it accordingly. In Manufacturing Operating Discipline but it will be in every computing essence, companies will have access (MOD) because the commercial system that we are using at the plant. to the equivalent of Apple’s App Store products available at the time did not It will be in field instrumentation. It for the iPhone and iPad. And cloud meet the company’s needs. In the will be in our safety systems. And it technology will allow for the manage- 1980s, Dow implemented the MOD-5 will even be in our unit operations,” ment and sharing of data across the system (the latest version) globally, Dimitratos of DuPont says. “Think supply chain. CEP which allowed every plant and facil- of an intelligent reactor or intelligent ity running on the MOD-5 hardware distillation column … It will have the and software to communicate with self-awareness of what it’s supposed one another. to do and its current state; it will have “Today, our manufacturing assets the capability of learning and adapt- are highly automated, including auto- ing; and when it’s running at the risk matic startups, shutdowns, deviation of, e.g., violating some constraint, it responses, and formulation changes, will be able to take some corrective and many of our plants utilize online action,” he says.

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