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Itzhak Gilboa, HEC, Paris, France, France
Accelerating Supply Chain Digital Transformation with Digital Twins and Agentic AI
Oleg Gusikhin, Ford Motor Company, United States, United States
Brief Bio
Itzhak Gilboa studied Mathematics and Computer Science (BSc, 1982) and Economics (BA, 1982, MA, 1984, Ph.D., 1987) at Tel Aviv University, with the graduate studies under the supervision of David Schmeidler.
He works in decision theory and other fields in economic theory such as game theory and social choice. His main research areas are decision under uncertainty, focusing on the definition of probability, notions of rationality, non-Bayesian decision models, and related issues.
He has been teaching a variety of courses on microeconomics, decision theory, game theory, psychology and economics, and related fields, at the undergraduate, graduate, and MBA levels.
He is the author of severel books such as Analogies and Theories: Formal Models of Reasoning, Oxford University Press, 2015 ; Theory of Decision under Uncertainty, Cambridge University Press, 2009 ; Rational Choice, MIT Press, 2010 and Making Better Decisions, Wiley-Blackwell, 2010.
His recent research papers are published in prestigious scientific journals such as American Economic Review, Games and Economic Behavior, Annual Reviews in Economics, BE Journals in Theoretical Economics, Econometrica, Economics and Philosophy, Journal of Economic Theory, Journal of Economic Perspectives, Mathematical Social Sciences, Review of Economics and Statistics, The Journal of Econometrics.
Itzhak Gilboa is also the holder of the AXA Chair in Decision Sciences
Brief Bio
Dr. Oleg Gusikhin is a Senior Director, Data Science & Machine Learning at Ford Global Data Insight & Analytics, where he leads Supply Chain Analytics. He has over 30 years of experience in application of advanced technology and analytics in the automotive industry. During his tenure at Ford, he has created numerous high-impact long-lasting applications for Ford manufacturing, supply chain and connected vehicles, and holds over 100 patents. Dr. Gusikhin is a Fellow of IEEE, a Fellow of INFORMS and a Fellow of AAIA. He is a recipient of three Henry Ford Technology Awards in the Manufacturing, Research, and Product Development categories, the 2025 INFORMS Innovative Applications in Analytics Award, and the 2014 INFORMS Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research. In addition, Dr. Gusikhin is a Lecturer at the University of Michigan Industrial & Operations Engineering and engineering faculty advisor at the Tauber Institute for Global Operations.
Abstract
Digital Twins and Agentic Artificial Intelligence (AI) are driving a fundamental change in how enterprise information systems are designed, deployed, and managed. This new paradigm empowers business users to directly define and manage AI agents using intuitive, no-code tools and large language models emphasizing self-service analytics, enabling rapid, iterative decision-making and driving a shift from reactive to proactive enterprise management.
This broader change has particularly profound implications for supply chain management, where conventional systems often struggle to provide the real-time adaptability and predictive insights required by today's dynamic global markets. In this talk, we explore how the convergence of digital twins and agentic AI is accelerating the reinvention of supply chain information systems.
Digital twins create data-driven, real-time representations of supply chains, enabling continuous monitoring, scenario simulation, and performance evaluation. Building on this foundation, agentic AI introduces autonomy into decision-making, allowing AI agents to reason over alternatives, negotiate trade-offs, and coordinate actions across organizational silos, often by seamlessly integrating and orchestrating existing software tools and platforms.
This presentation overviews the development of Ford's end-to-end supply chain digital twin, illustrating its foundational capabilities and the comprehensive visibility it provides. It demonstrates how agentic AI is incorporated into this digital twin environment to address critical practical challenges.