Vinayak Deshpande
Vinayak Deshpande
Professor of Operations at University of North Carolina, Chapel Hill
Professor Deshpande is the Mann Family Distinguished Professor of Operations at the Kenan-Flagler business school at University of North Carolina. He holds a Ph.D. in Operations Management from the Wharton School, University of Pennsylvania. He also holds a M.S. in Operations Research from Columbia University, New York, and a B.Tech. in Mechanical Engineering from I.I.T., Mumbai. Prof. Deshpande was awarded with the Dantzig Dissertation award for his Ph.D. dissertation for his work with the US Navy and DLA in optimizing the weapon systems spare parts supply chain. He has worked with the US Coast Guard on a series of projects for optimizing the supply chain used for aircraft service parts. His work with the US Coast Guard was selected as a finalist for the Edelman award and he was honored as an Edelman Award Laureate for an outstanding example of management science and operations research practice. His work on airline operations has been honored with the AGIFORS best contribution award by the Airline Operations Research Society AGIFORS. His research using data from Alibaba’s Cainiao network and JD.com on e-commerce logistics received the MSOM data driven research challenge finalist award. His recent work on surgical tray optimization was selected as a finalist for the Innovative Applications of Analytics Award by the INFORMS society. His research interests are in the area of Supply Chain Management, E-commerce logistics, Service/Spare Parts Management, Inventory Management, Sustainable Operations, and Healthcare Operations. His research has been motivated by contexts from various industry sectors such as defense, aviation, hi-tech, retail, e-commerce, airlines, and healthcare. His research has been published in premier academic journals such as Management Science, Operations Research, POMS, and M&SOM. He recently served as the president of the supply chain college of the Production and Operations Management society.
Track: Cloud Data
Unlocking the Potential of AI/ML to Build Agility and Resilience in Supply Chains
In this session we introduce a new paradigm to overcome current deficiencies in how artificial intelligence (AI) and machine learning (ML) are being implemented to support supply chain resiliency and agility. The new paradigm is based on a novel application of a machine learning framework that combines data integration, a digital twin and an optimization model to drive the best use of the extensive data that is now available to managers. We review two industry case studies (semiconductor equipment manufacturing and consumer electronics) to illustrate how the approach can be applied. The case studies demonstrate the power of incorporating additional potential data drivers of decisions. They also quantify the value of explicitly considering past and future what-if scenarios that can be used to derive actionable supply chain strategies. We conclude with a discussion of management lessons learned from the cases and a recipe for how to achieve the desired results.
