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Creating a Smarter Future

Winner of the MLC’s 2019 Lifetime Achievement Award, Jim Davis believes a deeper grasp of data context, better ways to combat complexity, and a more broadly embraced industrial culture of collaboration and innovation are essential to manufacturing’s future.

“We’re just beginning to discover that we can do things that have not been thought of before.”

Jim Davis, Vice Provost IT & Chief Academic Technology Officer at UCLA and Principal Investigator and CIO Advisor of the Clean Energy Smart Manufacturing Innovation Institute (CESMII)

In June this year, Jim Davis, Vice Provost IT & Chief Academic Technology Officer at UCLA and Principal Investigator & CIO Advisor of the Clean Energy Smart Manufacturing Innovation Institute, won the Manufacturing Leadership Council’s prestigious 2019 Lifetime Achievement Award.

For over four decades, Davis has helped pioneer the practical application of advanced cyberinfrastructure and operational technologies across both academia and the manufacturing industry. His involvement spans the control, design, and diagnosis of industrial chemical processes in the early days of AI and machine learning in the 1980s; the development of a national network infrastructure in the 1990’s; co-founding the U.S.-based Smart Manufacturing Leadership Coalition in the 2000’s; and co-leading the formation of the Clean Energy Smart Manufacturing Innovation Institute (CESMII) in the 2010’s.

A lifelong advocate of advanced data and modelling technologies and better practices to create a smarter future for manufacturing, Davis has been instrumental in the formation and leadership of numerous collaborative industry and academic technology initiatives focused on advancing the cause of U.S. and global manufacturing.

In our latest Dialogue with a manufacturing industry thought-leader, Davis talks to Manufacturing Leadership Council Executive Editor Paul Tate about developing more effective approaches to how we make things, how more collaborative practices and the convergence of advanced data and modelling technologies can help drive industrial competitiveness, how a deeper understanding of the nature of data can help solve some of the world’s most pressing issues, and the critical role that manufacturing now needs to play in the global future.

Q: What excites you most about your current role today?
Smart manufacturing is now on the national table in terms of how well we make things and use resources, and how product and operational innovation need to go hand-in-hand. In the early days of Smart Manufacturing, this terminology was virtually non-existent. Competitiveness was then focused almost entirely on inventing the next great product or technology. How and where we make things are now key priorities, both nationally and globally. These priorities are of high value and drivers of innovation and dramatic change that can benefit manufacturing and the world generally. Reducing energy and materials, addressing climate change, protecting and improving the environment, and driving toward zero safety incidents are more in reach than ever before. I believe this cause is both exciting and necessary.

With my systems engineering hat on, the shear convergence of different technologies, changing values and cultures, changing drivers of economic potential, and different practices that are pushing and pulling on significant new opportunities are dramatic. There is a sense of being at a moment in time where there is practical alignment to take some very exciting steps at scale. Actually doing something to improve the future keeps me going more than ever before. It has been fifteen years to get to the starting line with Smart Manufacturing. It has been thirty years for the full value of data and knowledge in manufacturing to truly emerge. Even in the 80’s we were seeing remarkable operational possibilities with data and modelling, including AI and Machine Learning. The return of AI today, with far better tools and infrastructure to support it, now gives those possibilities far more traction.

“Moving forward can at times seem more like crisis management than opportunity management, although both are in play.”

Q: What challenges still keep you awake at night?
The existential challenge, I see, has to do with the increasing pace of technology change relative to the pace of human change. Is industry moving fast enough, for example, for opportunity, environment, competitiveness, and security? Are we aligning new technologies and practice with leadership readiness? Is there a critical mass of leaders really ready to move forward? Are we sufficiently distinguishing actual and perceived risks? We are surely not moving fast enough with needed workforce skills.

Sometimes it feels like industry is in front and aligned for next steps. At other times it feels like the pace is creating too much disruption and risk for industry to tolerate. This is the nature of disruption, but it is also a challenge. There is a high degree of sensitivity about being ready or not, but compared to a few years ago, I consider this a most positive sign of progress. Nevertheless, as I listen to discussions, moving forward can at times seem more like crisis management than opportunity management, although both are in play.

With my CIO hat on, the other obvious challenge is cybersecurity. Cybersecurity is a challenge that is worsening because the manufacturing industry is managing greater and greater complexity. Smart Manufacturing is not likely to succeed using the cyber technology practices and structures of the past thirty years. On the plus side, there are new, advanced, and far better ways to address the cybersecurity of interconnectedness. But the industry will need to restructure siloed operating practices to address the issues of cybersecurity and competitiveness for the future.

Q: How would you characterize the current state and driving forces behind M4.0 transformation?
I am seeing considerably greater industry clarity on business and economic opportunity drivers with Smart Manufacturing, or M4.0, compared to just a few years ago. The opportunities, while significant, point back to the necessity of true collaborative manufacturing practices, shared infrastructure, and radically increased use of data that can be carefully and securely shared for business and process interoperability. These opportunities involve structural changes to industry business practice and circle back to fundamental people questions about culture, risk, change, and market.

A good readiness test for me has been with the recent use of the term democratization. Democratization means having advanced technology, practice, and capability in the hands of everyone who can add value – small, medium, and large companies, solution providers, machine builders, universities, their supply chains, and the people throughout these communities. Even though there are long-recognized and growing gaps with technology and practice in small and medium companies compared to large companies, democratization was a vocabulary that just didn’t fly a few years ago. John Dyck, the CESMII CEO, and Haresh Malkani, the CESMII CTO, are among a number of national manufacturing leaders who have recently been using democratization to articulate more clearly than ever how closing the gap in capability and broadly supporting innovation helps the entire industry. It has been a full decade of coming to grips with the idea that collaboration and the need for democratized capability are practical necessities. We are just now at the doorstep of the heavy lift with changing current market drivers, how risk is viewed, and the operational culture for collaboration throughout industry. But the change is clearly underway.

I also think we’re at the very early stages of really understanding the nature of data and what does it really take to transform data into information, knowledge and insight that can be applied confidently at scale and at the right time. In the early days of AI, a great deal of effort went into understanding knowledge and data in context. It was shown over and over again how crucial operational and physical contexts are to using data. Today, data is a word that’s so heavily used, and to be sure there are many new tools now available for working with large quantities of data. But much of the current discussion is not about data in context, how to get at that context, or how to apply context at scale. And of course, an aspect that’s really important about manufacturing data, both locally used and at enterprise scale, is that what you do with the data has real physical implications in terms of products and materials.

Q: Taking a step back, how did you first get involved in the early Smart Manufacturing initiatives?
For me, Smart Manufacturing was a convergence of several key experiences. There was control and operation of a chemical plant at Amoco Chemicals, working with early digital data acquisition, controllers and PLCs. As a faculty member at Ohio State University, I became involved with industry in the early days of AI and machine learning and was among the first group of engineering researchers applying AI to diagnostics and operational management of process and machine systems. I was also an early member of the Honeywell Abnormal Situation Management Consortium, which had received a NIST award to build a platform to integrate AI, control, and human-centered technologies to detect and manage abnormal situations in process operations. In parallel, I became involved with IT at scale as Associate Director of Research Computing with the Ohio Supercomputer Center and as the Director of University Technology Services at Ohio State University (when CIO was a new job title). This placed me in the early days of supercomputing, the ramp up of the world wide web, the start up of a national network called Internet2, an early ERP implementation, and the early days of dealing with security. As CIO at UCLA, I became further involved in national networking, university-industry partnerships, security, and operational data systems.

The actual start of Smart Manufacturing was a National Science Foundation (NSF) industry-university workshop in 2005/2006 that I had been asked to organize to address the role of cyber infrastructure in chemical and biological processes. It was at this workshop that the term Smart Manufacturing was coined. More recently, UCLA provided the flexibility for me to take on a Chief Academic Technology Officer role to focus on digitalization and public-private partnerships at scale.

Q: As a founding member of the Smart Manufacturing Leadership Coalition (SMLC), can you explain how the organization began?
The NSF workshop in 2006 produced a critical mass of industry interest about Smart Manufacturing. A small group of us, Jim Porter, then with DuPont, and Tom Edgar at the University of Texas in Austin, continued to explore and develop this interest. At about this same time, the U.S. discussions about manufacturing competitiveness started. Our group expanded and became more involved in those national discussions. Not only Jim and Tom, but also John Bernaden at Rockwell, Denise Swink, then the Vice Chair of National Materials and Manufacturing Board at the National Academies, Jim Wetzel at General Mills, Michelle Pastel at Corning, and MLC Board member Dean Bartles, then with General Dynamics, coalesced into a core group. There were many others that should be recognized but this was the core group that energized Smart Manufacturing in the early days. Early efforts produced additional workshops on Smart Manufacturing, with financial support from the DOE and NSF. It was also in this time period that national discussions had opened about the prospects of a National Network of Manufacturing Innovation (NNMI) Institutes. A particularly ground-breaking workshop occurred in 2011 in which industrial partners highlighted the value of a shared enterprise infrastructure for Smart Manufacturing. The concept came together around the economic value of radically increased productivity, with data and modelling combined, with radically lowered infrastructure and deployment costs. The SMLC was instrumental in supporting a successful DOE R&D project in which an industry-university team prototyped a Smart Manufacturing Platform and evaluated how it would work with two distinctly different industrial operations.

Q: What were the SMLC’s initial goals? Have they changed?
The goals of the SMLC did evolve and change shape, especially as the national discussions progressed with what NNMII’s should be formed, and as the DOE project produced industrial scale experiences. The SMLC was originally created to provide a trusted place for companies to come together and for people with like-minded thinking to share company experiences. It also provided a forum to advocate for Smart Manufacturing in terms of U.S. competitiveness in a global environment. As the national discussions expanded, the SMLC provided an important voice in the debates about control, management execution, data and modelling, operations and products, the digital thread, digital manufacturing, process intensification, and smart manufacturing. An outcome of the national process was a DOE-sponsored funding opportunity for a Manufacturing Innovation Institute for Smart Manufacturing: Advance Sensors, Controls, Platforms and Modelling for Manufacturing. The SMLC successfully competed for, and was awarded, the cooperative agreement to set up the Clean Energy Smart Manufacturing Innovation Institute (CESMII). CESMII was recently moved under UCLA’s much larger administrative structure. The SMLC is continuing its Smart Manufacturing advocacy and is building toward new complementary roles.

“Reducing energy and materials, addressing climate change, protecting and improving the environment, and driving toward zero safety incidents, are more in reach than ever before.”

Q: What are the key benefits of creating such a shared infrastructure?
The industry consideration for shared infrastructure stems from how to get at substantially greater supply chain productivity, operational precision, and process performance opportunities that are derived from business collaboration, data interoperability, and the democratization of capability and innovation throughout the industry. When one looks at the siloed infrastructure prevalent today, the cost of configuring and creating the interconnections between individual products, platforms and/or systems in a line operation, plant or supply chain, is already high. Combine this with increasing numbers of devices and sensors, radically increased use of data of which some is within control and some not, and the security required for the interconnections and flow of data. Complexity, as a result, is increasing dramatically and working against objectives. The high cost of data and modelling infrastructure and the resource-intensive requirements to get data contextualized and in a form that the data are usable, further add dramatically to complexity and cost. Add into that, the current views of isolating IP need to change for collaboration. But there are not easy, trusted, and secure ways to manage data-centered business partnerships. Taken together, the complexity of putting data and modelling systems into manufacturing supply chains, ecosystems, and value chains at scale, is overwhelming, let alone taking advantage of new sensor, data, and modelling system capabilities. The industry has to address this increasing complexity by changing how to do systems at scale. If not, Smart Manufacturing, or M4.0, will fall far short of its potential. CESMII is focused not only on new data and modelling technologies and practices, but also on the industry specification, use, and practice with shared platform infrastructure at scale.

Q: At a corporate level, do companies now need to transform their internal structures to put these new collaborative approaches into practice and deliver real value in a Smart Manufacturing world?
The key word is practice — putting the technology, the market, the organization, the culture, and the training together into comprehensive alignment so that new smart manufacturing capabilities can actually be implemented and drive value. Industry should anticipate the need to rethink existing structures about where things are made, how they’re made, and which business and operational partnerships add value. There are examples now where data have exposed new business-to-business opportunities or changes to interoperation practices that had not been previously thought about. Simplifying infrastructure practice is just as critical. But deriving value does depend on shifting long held mindsets such as market drivers, how infrastructure is valued, and how the benefits and risks of deeper collaborations are traded off.

“The complexity of putting data and modelling systems into manufacturing supply chains, ecosystems, and value chains at scale is overwhelming.”

In general, we are talking about new, more valuable ways of defining and using IP, and new ways of dealing with cybersecurity and infrastructure risk and opportunity together. New, substantially different ways to engineer data and process modelling systems are on the horizon and will require new skill sets. Value and economic drivers are certainly changing while small and medium companies need capacity and capability to engage much more aggressively. There is importance in starting the Smart Manufacturing/M4.0 journey and embracing change, but, I am the first to say, get ready for a heavy lift and long-haul proposition. I don’t see that the fundamental changes in store have been acknowledged strongly enough yet to be addressed. Reassuringly, I’ve seen the willingness of the industry to talk strategically and work together in public-private partnerships increase dramatically over the past four or five years. The power of the collective manufacturing voice to identify, understand, and shape the journey and address the risks together has, I believe, been proven.

Q: What are the implications of this transformation on corporate culture and the workforce?
I think workforce training is talked about too narrowly and needs to be embraced as a full cultural change. We need to think about broad communities and how the culture of working with data, security, privacy, interoperability, and business collaboration all need to come into play in a new data culture. Of course, individual workforce training activities on the ground need to happen. But boot camps work better than classes, as do new forms of mentorships and apprenticeships. I also think that we are spending too much time being defensive and trying to explain why manufacturing is not like it used to be. We should be explaining why manufacturing is exciting and change the vocabulary to be more about making things, dealing with climate, dealing with energy, and dealing with doing things better.

Q: What kind of leadership skills do you think the next generation of manufacturing leaders will need in the digital era?
I actually think back over fifteen years about many manufacturing leaders with whom I have had the remarkable opportunity to interact. There is a pattern. They talk about not being risky, but about a willingness to engage opportunity and manage the risk. They are not afraid of change and are open to collaboration. They are willing to provide a manufacturing voice and they talk about the way this is going to happen and how to embrace the digital future. They see potential with investing without falling into an ROI trap. They are also looking for value investment with new models that align with the new technologies and practices. There is an openness to think about intellectual property differently. They surround themselves with open-minded people and they get started with real projects and build successful experiences. A previous ML Award winner, Peter Holicki at Dow, exemplified this sort of leadership in his talk at the recent ML Summit.

Q: Finally, if you had to focus on one thing as a watchword for the future of manufacturing, what would that be?
Making the right products the best way for the future. I think manufacturing has truly crossed that threshold where operational and information data and modelling technology practice can fold into Smart Manufacturing, or M4.0, or Industry 4.0, and start to live up to its promise. I expect that promise to first be in ways that were not possible a few years ago but are now achievable. We’re just beginning to discover that we can do things that have not been thought of before. Importantly, we have seen the value of being tactical and incremental at pace, while always keeping the eye on the strategic prize. M

UCLA Campus, Los Angeles, California

FACT FILE: Clean Energy Smart Manufacturing Innovation Institute (CESMII)

Location: Los Angeles, California
Business Sector: Manufacturing USA Institute
Funding: $140 Million (Public + Private)
Membership: Practitioners, Solution Providers, Machine Builders, System Integrators, Universities, National Laboratories, Non-government Institutes, Local Government Offices
Presence: U.S. National: 3 Regional Centers and HQ

Vice Provost IT & Chief Academic Technology Officer, UCLA; Principal Investigator & CIO Advisor, Clean Energy Smart Manufacturing Innovation Institute; Professor Department of Chemical and Biomolecular Engineering, UCLA
Nationality: American
Education: B.S. degree in chemical engineering, University of Illinois at Urbana-Champaign; M.S. and Ph.D. in chemical engineering, Northwestern University
Languages: English
Previous Roles Include:
Associate Vice Chancellor IT and CIO, UCLA
– Professor, Department of Chemical Engineering, Ohio State University
– CIO/Associate Provost IT, Ohio State University
– Associate Director, Research Computing, Ohio State University
– Lecturer, Department of Mechanical Engineering, University of Wisconsin-Madison
– Research Engineer, Amoco Chemicals Corporation
– Other Industry Roles and Awards:
– 2019 Manufacturing Leadership Lifetime Achievement Award
– Chair, Governance Board, CESMII
– Program Lead and Board Member, IS Associates (CIO leadership coalition of 50 companies and government offices in Southern California)
– Co-Founder and CTO, Smart Manufacturing Leadership Coalition (SMLC)
– Board Member, Manufacturing Leadership Council’s Board of Governors
– Board Member, Mforesight: Alliance for Manufacturing Foresight’s Leadership Council
– Advisory Board, Centre for Innovative Manufacturing in Emergent Macromolecular Technologies, University College London
– Chair and Board Member, Corporation for Education Network Initiatives in California (CENIC)
– Fellow, American Institute of Chemical Engineers
– Board of Trustees, Computer Aids in Chemical Engineering Corporation (CACHE)

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