The need for leadership in smart manufacturing cannot be overstated: Making revolutionary changes can be arduous. But the leaders who have emerged in this industry show us it can also be exhilarating. To assemble this group of luminaries, Smart Manufacturing took into account the fact that big change is happening inside large corporations, startups, public-private partnerships, and standards organizations. An example of each, respectively:
When it comes to smart manufacturing, Oteka and his company are obviously in it for the long haul. “We have adopted several automation and robotics technologies in our machining, injection molding, assembly, inspection and packaging operations across our global manufacturing footprint,” he said. The company also has implemented Industry 4.0 solutions associated with connectivity, real-time production-performance monitoring, data analytics, simulation and additive manufacturing. “These technologies help drive manufacturing excellence, meet and exceed both internal and external customers’ expectations and drive process standardization,” he said. What is next? Artificial intelligence is an area of interest for expanding Stanley Black & Decker’s data analytics capabilities, he said. While Oteka has an interest in exploring and deploying cutting-edge technology solutions aimed at providing flexible manufacturing processes, improving productivity and lowering cost, he also has the support of the leadership team, which is willing to invest in technology and talent. “The collaboration with and adoption rates from our operations teams have been instrumental, as well,” he said.
“Andersen’s smart manufacturing journey began out of necessity in the 1990s as we began to transform our business to made-to-order,” Schneider said. The company offers custom-size products with nearly unlimited combinations of glass, color and hardware. “To ensure success, we needed to integrate details of customer orders into the information systems that direct raw material delivery and manage our manufacturing processes,” she said. “More recently, our smart manufacturing journey has led us to integrate supply chain processes to provide real-time communication from the shop floor to our manufacturing support teams.” Andersen’s new, greenfield manufacturing site in Arizona has integrated materials and processes from raw material through to the final-mile delivery trucks, to unlock unique ways to optimize its supply chain. “When I think about the future of smart manufacturing, I see an acceleration of the current path,” she said. “Integrated and connected manufacturing will extend to remote monitoring and predictive diagnostics across material supply, part quality and end-to-end optimization.” She volunteers with an SME committee and is a commissioner for the organization that accredits post-secondary engineering programs.
Lukens was in the midst of post-doctoral research, after finishing her doctorate at Tulane University, when she was hired into industrial software amid “the mad scramble to hire data scientists,” she recalled. “Once there, I promptly started working with some amazing mentors with deep domain experience and big visions, and I was hooked.” Her area of focus is technical language processing. In her view, it is one of the most underutilized sources of data in manufacturing—and also contains some of the richest data. “Decades of knowledge and experience are sitting unused in unstructured text,” she said. “But due to the technical nature of such text, out-of-the-box tools from natural language processing don’t often cut it for extracting business value.” Specifically, she has worked with different data sources—including maintenance work orders, operator logs, inspection reports and downtime events—to develop not only scalable tools that formally leverage knowledge and experience for extracting structured information from the unstructured text but also end-to-end work processes augmented by the AI-extracted information. She has also developed software infrastructure supporting the deployment and execution of such AI-augmented work processes.
Verre created a new approach to machine vision based on neuromorphic computing and AI. Called “event-based vision,” it significantly improves the performance, power efficiency and dynamic range of cameras used in industrial automation. “It can be applied to a range of smart manufacturing functions, such as high-speed counting, vibration monitoring for predictive maintenance and spatter monitoring for live quality control,” Verre said from his office in Paris. He has encountered some pushback to his technology. “We are a disruptor of sorts, and it can be easy to get discouraged by people’s resistance to change,” he said. “We have overcome that by showing very real, tangible improvements that can be realized with our technology. Looking forward, we want to continue to build an entire ecosystem around event-based vision.” He anticipates more intelligence embedded into every aspect of manufacturing. “Great strides have been made in making AI and machine learning more accessible, and we envision more machines and processes that can perform better by using characteristics of the human brain and eye to improve safety and productivity in manufacturing,” he said.
Helu has been building things since he was a child. But he only began considering a career in manufacturing when he was in college. His first professional experiences were internships on the production floor in automotive factories. “The experience motivated me to seek out research opportunities in manufacturing, and I was fortunate enough to find incredible mentors in the field who helped nurture my interests,” he said. In graduate school, while working in the laboratory, he encountered a group of smart manufacturing leaders who were meeting to create the MTConnect standard. “By virtue of being in the right place at the right time, I was given the chance to work on an early implementation of MTConnect, and I was hooked,” he said. At NIST, Helu co-led the development of the Smart Manufacturing Systems Test Bed, a model factory that facilitates creation of a digital thread. “Through demonstration test cases and best-practice and guidance documents based on our experience, we hope to provide a blueprint for manufacturers to develop their own appropriate smart manufacturing solutions,” he told Smart Manufacturing.
When a manager from Fusion OEM’s production floor brought the idea of using a robot to tend its CNC mills and lathes, Zoberis was open to the idea. He bought a collaborative robot (cobot) from Universal Robots in 2018 and his high-mix, low-volume machine shop became the customer for Fusion’s engineering team that was tasked with integrating it. The company learned so much and saw so much value added by the cobot that Fusion became a certified integrator for Universal Robots. “We were challenged by the skills gap that is in the world of CNC machining,” he said of integrating more automation. “We were looking at all options to attract new talent and train our current workforce and determined that automating our CNCs with robots was the best route.” Maybe so in one instance, but Zoberis has another solution: He hires for attitude, then offers in-house training and financial incentives for learning. Looking to the future of smart manufacturing, Zoberis sees user-friendly software interfaces for automation in CNC machines like his that are graphics-rich and intuitive, he said.
Oppedisano describes his tech as an “industrial-strength” predictive analytics platform, built to handle the most unconventional, unwieldy, large and complex datasets. “We make it easy for firms to corral data from multiple sources, find hidden correlations and create predictive models that drive impactful financial outcomes,” he said. “We specialize in reducing the cost of poor quality, unplanned downtime and scrap.” His firm has helped Rolls-Royce predict failures across multiple engine types, saving millions. “When I say ‘payoff,’ it’s not just the ROI of solving a million-dollar problem but the relationship and credibility we build with a customer who’s been trying to do this on their own for years,” he said. He has leveraged his SME membership to create a community of people trying to solve similar issues. “Several of these folks participate in a monthly ‘virtual cocktail’ call, sharing lessons learned,” he said. “We’ve been able to educate others and learn a bunch, too.”
Hopkins and his collaborators at the Institute for Advanced Composites Manufacturing Innovation could have set up a booth at IMTS 2018 with informational literature about membership. Instead, they set up a small factory in the convention center. There, they demonstrated the use of equipment at different exhibits to 3D-print a metal die for a motorcycle part, machine it on a multi-axis CNC, use the tool to compression-mold the component and finally laser scan the part for dimensional consistency. “The demo of additive manufacturing for a rapid path to actual fiber-reinforced polymer part production at IMTS highlighted several key benefits provided by smart manufacturing: connectivity across processes, shared data, flexible allowance for different material needs and closing the loop for qualifying physical part dimensions,” Hopkins said. The demonstration is one of several key accomplishments at IACMI, the Manufacturing USA institute that focuses on cost-competitive fiber-reinforced polymers that can be produced at high rate or volume for vehicles, wind turbines and compressed gas storage, along with the use of full-field imaging for non-destructive evaluation of in-line process control of carbon fiber manufacturing and physics-enabled design optimization.
In a way, Praemo’s analytics engine, Razor, mimics Henderson’s work in previous jobs. “I would design and perform tests on pre-production products while using sensors and data-acquisition systems to monitor as many aspects of the product as possible,” he recalled of his first job. “I would then apply what I had learned from my mechanical engineering undergraduate education to identify product issues and work with development teams to correct them before products were mass-produced and sold to customers.” At a subsequent job, “a lot of my time was spent manually gathering data that could have been available to us if we were tapping into the control systems on the floor,” he said. Eventually he saw a different, automated solution. “I concluded that technologies associated with the general field of AI were going to dramatically improve manufacturing’s ability to utilize data, but those technologies had not been developed into a product that was usable by the average manufacturer,” he said. “So, I was connected with some like-minded individuals and we set about building it.”
Stand on any busy street corner in the U.S., and within minutes you’ll likely spot one of the vehicles Hightower had a leading role in creating. They include the BMW 6 Series, Lincoln Navigator, Buick Enclave and Chevy Blazer. Leveraging his executive experience at BMW, Ford Motor and General Motors, he now has his sights set on facilitating value-added manufacturing and industrialization on the African continent, with a specific focus on the automotive industry. “Local manufacturing of motor vehicles and automotive components will create opportunities for entrepreneurs and grow local jobs and skills,” he said. “This is essential as the population of the African continent is forecast to double over the next 30 years, and Africa will soon have the largest workforce in the world.” One of his clients is Kiira Motors, a late-stage maker of electric buses in Uganda. His 2018 book, “Motoring Africa,” builds the case and sets a roadmap for establishing sustainable manufacturing in six African nations.
Herway’s work focuses on creating a clean and more sustainable energy value chain and closing the gap between scientific discovery and industry adaptation. “I came to INL because I saw energy and climate change as one of the great challenges of our generation and one that would have huge ripple effects for future generations,” he said. “With over a third of the energy consumption in the U.S. from the industrial sector, manufacturing—in all its forms and intermediate steps—is one of the more impactful places to realize the transformation to a clean and sustainable energy value chain.” He recognizes the value of smart manufacturing in minimizing cost to optimize revenue, but “the part of smart manufacturing I’m most excited about is the quality and creativity of the output,” he said. What he sees in the future are stronger, more dynamic materials and innovative ways to recycle, preserve precious resources and minimize waste. Better data and better insights for improvement through more accurate analysis and relevant conclusions from that data will bring robustness and scalability, he said.
Naterwalla has a vision to help the Oregon Manufacturing & Innovation Center (OMIC) become internationally recognized as a state-of-the-art research center with the unique distinction of fostering data-driven decisions that provide value-added solutions to meet true production needs. With the participation of its members, world-class machinery and equipment, an on-site factory of tomorrow and a new training center due to open this year in collaboration with Portland Community College, the center is certainly moving the needle in advanced metals manufacturing technologies and processes in the Portland area. Next up for Naterwalla and his R&D team are temperature-predicting in machining, implanting sensors in additive parts, vision systems for finish measurements and defect analysis and rapid moldmaking by additive manufacturing. He also teaches advanced manufacturing master classes at the center’s OMIC Academy. On a personal note, he recently published a textbook, “Machining Solutions–The Art & Science of Metal Cutting.” “It is my sincerest hope that this text contributes to enhancement of professional careers, or helps students learn our trade the right way,” he said.
Gaur has helped Hitachi develop wide-ranging smart manufacturing solutions to help its customers. “I’m proud of the work we are doing in automation to augment workers, utilizing key pieces of emerging technology, such as AI/machine learning, 5G, robotics and time-sensitive networking,” he said. “Besides that, we have had great success with providing solutions to improve shop-floor operations using camera/LiDAR analytics and vibration/ultrasonic analysis for equipment monitoring.” At Hitachi, it is not at all the case that the cobbler’s children have no shoes; smart manufacturing begins in-house. “Most of Hitachi Astemo’s plants had ‘IoT connectivity’ before the term was coined,” he said. “We have been collecting data from PLCs and rendering visualization of production operations to help supervisors and plant managers conduct their jobs efficiently. Since we have grown considerably in the last couple of years, with 140 manufacturing sites worldwide, we conducted a systematic study to re-evaluate our smart manufacturing operations. Now our emphasis is on scaling our repository of solutions to all plants with minimal customization.”
The stakes could not be much higher for Francis and his organization, which is partnered with the Department of Defense and a part of Manufacturing USA. “Smarter manufacturing—which at LIFT we describe as the connection between materials, processes and systems—is critical for our nation because we believe the nation or region that masters it first will be the next global industrial and economic powerhouse,” said Francis, who is at the center in the photo below. Many large manufacturers have already adopted smarter manufacturing principles, so the challenge lies in educating and supporting the small and medium-sized companies that are LIFT’s focus, he noted. “They need to know what smarter manufacturing entails, but they also need to know and implement the digital twin and distributed manufacturing and, most importantly, what smart technologies and processes can impact their specific situation and how they work together to impact their bottom line,” he said. To accomplish that, LIFT has developed an ecosystem to connect government, industry and academia to drive American manufacturing into the future with technology and talent, including “mom and pop” shops that could otherwise be left behind.
“Men and women across the planet go to work every day in complex environments,” Glynn said. “The more we can understand about those environments, the more we can improve the outcomes for workers.” The data-gathering, arm-band device from MakuSafe—which feeds environmental, human motion and spatial information to an AI platform—makes workers part of Industry 4.0. Unlike data gathered from machine tools, though, MakuSafe enhances a worker’s production through his or her safety. A few years ago, MakuSafe was “two guys working on an idea.” Today, the firm employs 23 people, and Glynn and his colleagues plan to double that number this year. When this CEO envisions smart manufacturing in the future, “I see a beautifully choreographed dance between worker, environment and equipment beginning to emerge,” he said. “Automation will continue to grow at a rapid pace, and the role of human workers will continue to evolve. Industry 4.0 will find new ways to improve workflows, production processes, output, building design, product design, worker safety and so much more.”
Protolabs pioneered digital manufacturing in 1999 when founder Larry Lukis wrote more than 1 million lines of software code to automate injection molding. The company created the first complete digital thread, touching every stage of the manufacturing process, including design analysis, pricing, production, quality control and shipping. Since then, Protolabs has expanded to include CNC machining, 3D printing and sheet metal fabrication. The company’s digitization and capabilities uniquely positioned it to help with coronavirus pandemic efforts: “Since March 2020, I’m proud to say that we’ve produced 20 million mission-critical, COVID-related components, which is very inspiring,” Bodor said. While digits and machine tools keep Protolabs humming, none of it would happen without one additional element. “Smart manufacturing isn’t possible without the smart people making it happen,” he said. “When you work in digital manufacturing, change is constant. It has to be in order to support the on-demand culture we live in. So, I continue to be amazed at how well our employees adapt and thrive on change.”
When Waddell, an economist by training, envisions smart manufacturing of the future, he sees work-from-home machinists making six-figure salaries to keep five to 10 machines running profitably. His role at the Association For Manufacturing Technology puts him in a position to have an opinion worth paying attention to. He manages a 300-company, volunteer organization that developed and maintains MTConnect, a free, open standard that defines a domain vocabulary for manufacturing. “I didn’t create it,” he said. “My role is primarily to ensure that nothing is in the way of those volunteers.” Some of his favorite things related to his job include Excel spreadsheets, touring factories, minimalist PowerPoint slides, getting to help assemble a car, industrial lasers, visiting NASCAR engine shops, working with startups and user interface design for shop-floor applications. What inspires him? “First, there’s still low-hanging fruit in smart manufacturing as far as the technology itself goes,” he said. “Second, there’s also a ton of untapped personnel potential in Gen Z, particularly women and girls. Half the talent pool is basically missing from manufacturing.”
Akella is all in on smart manufacturing, but he thinks Industry 4.0 has a blind spot. What’s missing is augmenting and digitizing human tasks, which represent over 70 percent of production. His solution is a company he founded in 2016 to provide an AI-powered, computer-vision solution that, for the first time in history, digitizes manual assembly activities on a massive scale. “By recording every activity at every station on the assembly line, Drishti equips manufacturers with more data than has ever before existed on manual activities,” he asserted. “Better data drives better decisions, and Drishti’s data helps line supervisors, industrial engineers, plant managers and line associates digitally transform the plant floor and extend the value of human workers.” Being first is not new for Akella: In the 1990s, he was an engineer at General Motors and led a team that built some of the world’s first collaborative robots. Somewhat ironically, the experience taught him that people are manufacturing’s superpower. “But 112 years after Henry Ford, there’s a massive lack of understanding about manual assembly activities in manufacturing,” he said.
“It wasn’t a super AI that brought humanity to its knees last year. It was a virus,” Aparicio said. Siemens responded with robots and other smart manufacturing technology for testing, contagion control, production and supply-chain improvements. The virus highlighted domestic manufacturing’s importance. “Manufacturing was overlooked for a long time,” he said. “It was regarded as something that was done somewhere else, across the ocean. Now it is one of the hottest areas to work on, and traditional approaches are ripe for disruption.” The company’s greatest successes are incorporating AI into the control layer for robotic systems and enlisting “some of the best roboticists in the world” to do so, he said. What’s next? “Just enough human-like dexterity so that robots can tackle tedious, dangerous and error-prone tasks, unlocking the human potential for tasks where ingenuity is needed,” he said. “And making robotic applications so easy to operate and interoperate that there is no barrier to entry for advanced robotics applications.”
*Aparicio joined Ready Robotics in March
Georgetti, who hired in at Eaton as a manufacturing engineer in Brazil 23 years ago and worked his way up, is just as methodical in his thinking about Industry 4.0. At the start of its smart manufacturing strategy, Eaton Vehicle Group chose 10 “i4.0 solutions” to select from—individually or in combination—to address a business need or issue, along with a governance model for their deployment. “We have been deploying those in a pragmatic, integrated and structured approach,” he said. The group is now working on a plan for metal additive manufacturing production. “Eaton Vehicle Group chose the smart manufacturing journey because we really believe all of the 10 solutions working in an integrated approach can help us to achieve our ultimate goal: to improve operational performance in productivity, flexibility, quality and speed to market,” he said. “By achieving it, we will be addressing the needs of all of our stakeholders, from our employees to our customers.” Ultimately, he envisions a digital ecosystem that’s fully integrated among the entire value stream chain, including Eaton’s suppliers and customers.
Moskowitz believes the United States can’t maintain its leadership in innovation by continuing to offshore production. “In many cases, the knowledge that we gain by manufacturing the products leads us to develop the next generation of innovations—because we learn so much about the products by making them ourselves,” he said. “Maintaining a strong, smart manufacturing base domestically is important to our nation’s economy, its competitiveness, and its security.” In addition, the coronavirus pandemic has made it clear to Moskowitz, and many others, that a smart manufacturing capability is critical to our nation’s resilience in the face of unexpected supply-chain disruptions. Moskowitz, who spent 30 years in the semiconductor manufacturing industry, is staking his career on smart manufacturing. For almost a year, he has led the robotics-focused institute for Manufacturing USA. “Through this national consortium, we have accomplished dozens of projects to make robotics, autonomy and AI more accessible to U.S. manufacturers large and small, train and empower a smart manufacturing workforce, strengthen our economy and global competitiveness and elevate our national security and resilience,” he said.
As a tech futurist and evangelist, Mathur thrives on and specializes in navigating transformational programs, identifying and enabling end-customer and business-driven outcomes via new technologies and partner ecosystems. Mathur is a true believer in pushing “out-of-the-box” execution strategies and driving digital (and self) transformation by joining hands with domain experts in a partner ecosystem. All of that may be what makes him so good at leading the strategic partnerships and global program management function for Hexagon’s smart manufacturing business unit. In his global role, Zurich-based Mathur fosters industry and strategic partnerships that deliver a host of innovations, benefits and added value to the end customer. For example, Hexagon partnered with Ericsson, the telecommunications company and developer of 5G technology designed for manufacturing, to develop new 5G technology geared toward industry and automation. As a result, Ericsson opened a 5G-connected, smart manufacturing facility in Plano, Texas, and Hexagon integrated Ericsson’s 5G capabilities in a new facility in China designed around cableless shop-floor operations and manufacturing and an autonomous future.
Damiani and people like him, with expertise in IT, have become crucial to progress in life sciences companies like Moderna, maker of a coronavirus vaccine. “I think today we’re at a fortuitous time where the advances in life sciences technologies like proteomics, genomics and next-generation sequencing, are fueled by the revolution in IT,” he said at the eyeforpharma event in 2018. For example, at the end of the day, scientists at Moderna’s drug-design studio send their data to the company’s fully automated and digitized lab. Overnight, the lab brings the design to life, applies analytics to the design data, and sends everything back to the scientist. The use of cloud computing, AI, advanced algorithmics and analytics are all used to get insights into the data, he said. Prior to joining Moderna in 2015, Damiani helped conceive and build creative IT solutions to help solve business problems at, for example, the French diagnostics company bioMérieux. He is known for employing his digitization skills to transform a company’s information flow—and for ultimately improving products and processes.
Eschbach believes the human element—specifically person-to-person communication—has been overlooked in smart manufacturing. This is especially important in the process manufacturing industry, where there is a vital link between communication and safety, he said. “I found it especially interesting in the process manufacturing industry, with its multifaceted and complex operations and units located around the globe—along with huge safety risks if important information doesn’t get to all personnel,” he said. “It was obvious that this challenge can’t be solved with spreadsheets or office collaboration. Those manufacturers need a robust and reliable solution with a tamper-proof foundation”—and one that complies with regulatory authorities. His solution is software called Shiftconnector. It captures and connects team-to-team, or shift-to-shift, communication to a repository of real-time manufacturing data. Almost every module was designed and developed with a customer’s input. He’s getting feedback from customers like Roche Pharma, which acknowledged Shiftconnector helped it cope with the pandemic by facilitating non-contact communication in its cancer medicine production. “We want to make a difference!” Eschbach said.
The adage “you can’t improve what you can’t measure” is certainly true, Thompson said. “However, it omits one important fact: You can’t measure what you can’t see.” At Process Intel, a global provider of mobile quality-verification solutions, inspection software and mobile process auditing solutions, he helps fill manufacturers’ need for quality control. “Process Intel stops damaging quality defects through automated data correlation and direct input solutions,” he said. “Our smart manufacturing platform improves data management, visual monitoring and analytics for an expansive snapshot and contextualized approach to product quality excellence.” The detailed visibility into operations data coupled with defect-related quality data alerts manufacturers to issues before product delivery, when they can act on them. “We improve overall quality in industrial operations by automatically correlating and contextualizing key operational data,” he said. “Our Industry 4.0 smart monitoring solutions enable real-time, Industrial IoT data, autonomous decisions, automated workflow assignments, predictive analytics and machine learning/AI data-science insights.” Thompson said he relies on his SME membership to continue learning and to build relationships with like-minded innovators.
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