2021 Model-Based Enterprise Summit

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Abstract and Bios

Robert Bonneau, Ph.D. 

Abstract: The talk will provide an overview of complex information systems including quantifying, managing, and designing heterogeneous complex systems through assessment of information services.  Methods of measuring and assessing the performance of network, software, and hardware infrastructures such as cloud architectures will be discussed including techniques of systems measurement, and mathematical metrics for performance. Strategies of quantifying risk over different system architectures including model based software and systems engineering and Development and Operations (DevOps) on the cloud will also be presented.

Bio: Robert Bonneau is currently Director Software, for Data Analytics, and Embedded Systems in the Office of the Secretary of Defense, Under Secretary of Defense for Research and Engineering. He also is co-chair of the White House Office of Science and Technology Policy (OSTP), National Information Technology Research and Development, Large Scale Networking Interagency Working Group and member of the OSTP Senior Steering Group for Big Data. Dr. Bonneau was also the Chief of the Information, Decision, and Complex Networks Division at the Air Force Office of Scientific Research, where he established the Complex Networks, and Foundations of Information Systems Programs.  He has held academic positions most recently in the Statistics Department at George Washington University, and engineering and computer science departments at Columbia University, Rensselaer Polytechnic Institute, and Temple University. Dr Bonneau has a Ph.D. in electrical engineering from Columbia University, and a Masters and Bachelors in electrical engineering from Cornell University. Dr. Bonneau has served as Associate Editor of the Springer Journal of Infrastructure Complexity, has over 85 journal and conference papers, has 1 book co-authorship, contributed to 2 book chapters, holds 3 patents, and is a Senior Member of IEEE.

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Thomas Hedberg, Jr., Ph.D., P.E.

Abstract: This year brings us the 12th MBE Summit, which proves the active digital transformation of the U.S. manufacturing industrial base and its supply chains. The DoD has presented its Digital Engineering strategy at the MBE Summit, which calls for a digital transformation to address challenges associated with complexity, uncertainty, and rapid change in deploying and using systems. The DOE's NNSA has also presented its Model-Based Enterprise Transition Initiative (MBIT-I) at the Summit. Further, many MBE Summit presentations have provided recommendations using model-based practice to enable holistic and comprehensive approaches in making decisions on designing, deploying, and maintaining technologies, policies, and capabilities. But one area we have not addressed is what does MBE mean for the safeguarding and resilience of our supply chains? MITRE's recent Deliver Uncompromised report discusses that the United States' acquisition and industrial processes are under constant threat from blended operations in supply chains. Many key issues for acquisition and industrial security go beyond technical concerns -- including legal, policy, and procurement concerns. This presentation aims to discuss how different considerations of MBE, many of which have been presented over the years at the Summit, can be applied to supply chain security in support of resilience -- the ability to detect, mitigate, and recover.

Bio: Dr. Thomas Hedberg, Jr. directs the Acquisition and Industrial Security mission at the Applied Research Laboratory for Intelligence and Security (ARLIS), a DoD-sponsored UARC at the University of Maryland. Dr. Hedberg joined ARLIS from the National Institute of Standards and Technology (NIST), where he was the Program Manager of the Model-Based Enterprise (MBE) program and the Co-Leader of the Smart Manufacturing Systems Test Bed, which earned him the U.S. Department of Commerce Gold Medal. Dr. Hedberg is the Chair of the ASME MBE Standards Committee, a member of the several ASME Y14 committees, and a past member of ISO TC184/SC4/WGs 11, 12, 15, 21 and the ISO TC184/SC4 Quality Committee. Dr. Hedberg earned a B.S. in Aeronautical and Astronautical Engineering from Purdue University, a M.Eng. in Engineering Management from the Pennsylvania State University, and a Ph.D. in Industrial and Systems Engineering from the Virginia Polytechnic Institute and State University.

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Deborah Dull

Abstract: What will our supply chains look like in 2030? Will we have the same disruptions and challenges that we to do today? In this session, we’ll time travel to the year 2030 and  explore how our supply chains will use data, materials and resources to operate in a world that has disruptions and opportunities, a virtual and global workforce, and circular operating models. We’ll look at the role of Industry in ensuring a sustainable operating environment that supports supply chains for years to come.

Bio: Deborah Dull is a Principal of Manufacturing Product Management for GE Digital where she is responsible for supply chain, circular economy, and customer success. Prior to that she was an Impact Investor for Health Supply Chains at the Bill & Melinda Gates Foundation and spent six years at Microsoft where she oversaw launch management, inventory management, and innovation. She is a sought after author and speaker having been published in various books, articles, and white papers and spoken at dozens of industry events. Deborah holds Supply Chain & Operations Management degrees from Western Washington University (BA) and the University of Liverpool (MSc), with a thesis focused on the digital supply chain and is currently pursuing her doctorate focused on Supply Chain and the Economy at Hariot-Watt University.

Jan de Nijs

Abstract: Over the last decades, the amount of process and test data-artifacts generated by production systems has significantly increased. However, many corporations (large and small) have been struggling to unlock the true value of this data by applying Artificial Intelligence (AI) and Machine Learning (ML) to these data-artifacts. At great effort and cost, very impressive demonstrations have been devised so far, but it is proving very hard to generalize the insights derived from such demonstration. This typically leads to more very expensive and hard to perform demonstrations. In other words, it is proving very hard to monetize these data-artifacts using generalized AI and ML.

Bio: Jan de Nijs is the Lockheed Martin (LM) Fellow for Enterprise Digital Production and co-leader of the team within the Lockheed Martin Digital Transformation Program that has been chartered with unlocking the value of Digital Production.  Jan is principally engaged in the research, design, development, manufacture, integration and sustainment of advanced technology systems, products and services. Jan leads the corporate effort for implementing Industrial Internet of Things (IIoT) across Lockheed Martin. He is responsible for efforts on the full stack production solution, from floor level machinery connection, to high level Artificial Intelligence and Machine Learning driven value propositions that increase value for our customers. Before joining Lockheed Martin, he spent the first 15 years of his career in leadership roles in the capital goods industry, designing and building automation solutions for the automotive, aerospace, and the food processing industries, working both in Europe and the United States. In 2004, he joined Lockheed Martin Missiles and Fire Control in Orlando where he worked as a leader on many factory automation and production process improvement projects. In 2016, he moved to Lockheed Martin Aeronautics in Fort Worth to become part of the F-35 Production team. Jan is the recipient of several awards including Lockheed Martin’s highest honor, the Excellence Award for designing and deploying the defense industry’s first Intelligent Factory Framework. Jan holds an MS degree as well as a Technical Doctorate in Mechanical Engineering from Eindhoven University of Technology in The Netherlands. Jan serves as the LM voting member for the ANSI/MTConnect standards group, as well as various ISO efforts, such as ISO23247 “Digital Twin Framework”. He resides in Fort Worth, Texas. Jan can be contacted at jan.de.nijs [at] lmco.com (jan[dot]de[dot]nijs[at]lmco[dot]com) 

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Michael Sharp

Abstract: Much talk and interest has centered on the growing potential of the use of Artificial Intelligence (AI) for Industrial applications. This emerging field of Industrial AI (IAI) has promised greater process efficiency, higher production, and more optimal system control. But as industry increases adoption of these IAI tools, many questions emerge. Are existing AI tools suited to the demands of an industrial environment? Are particular applications more or less conducive to particular AI tools? How much should you trust the output of an IAI tool? This talk seeks to address these questions and more by highlighting the use of IAI as a tool adapted to use for and with a human operator. Maximizing the effectiveness of any tool starts by understanding the true end goal and users of that tool. The differences regarding unique challenges and opportunities of applying AI in an industrial setting are highlighted, as well as suggestions on managing such IAI tools. The talk touches on some examples of what is working well and what still needs improvement. The talk concludes by looking ahead and designing IAI tools with humans in the loop. IAI created with and for human understanding making in mind will define the next wave of these digital tools. Where machine intelligence meets human intuition and creative decision making, that is where we find actionable intelligence.

Bio: Dr. Michael E. Sharp is a Reliability Engineer at the National Institute of Standards and Technology (NIST) located in Gaithersburg, MD. He received a B.S (2007), M.S. (2009), and Ph.D. (2012) in Nuclear Engineering from the University of Tennessee, Knoxville, TN, USA. His research interests include signal analytics, machine learning, artificial intelligence, optimization, and natural language processing. Michael has worked on a wide array of projects including image processing for elemental material recognition, navel reliability monitoring, and manufacturing robotics diagnostic monitoring. He currently works with the NIST Systems Integration Division for Smart manufacturing.

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