Professor Deshpande is the Mann Family Distinguished Professor of Operations at the Kenan-Flagler business school at University of North Carolina. \u00a0He 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\u00a0from I.I.T., Mumbai.<\/p>\n
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Prof. Deshpande was awarded with the Dantzig Dissertation award\u00a0for 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\u00a0for an outstanding example of management science and operations research practice. His work on airline operations has been honored with the AGIFORS best contribution award\u00a0by the Airline Operations Research Society AGIFORS. His research using data from Alibaba\u2019s Cainiao network and JD.com on e-commerce logistics received the MSOM data driven research\u00a0challenge finalist award. His recent work on surgical tray optimization was selected as a finalist for the Innovative Applications of Analytics Award\u00a0by the INFORMS society.<\/p>\n
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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.\u00a0His research has been published in premier academic journals such as Management Science, Operations Research<\/em>, POMS, <\/em>and M&SOM. <\/em>He recently served as the president of the supply chain college of the Production and Operations Management society.<\/p>\n"}],"event_agenda":false,"event_photo_gallery":false,"event_presentations":false,"event_custom_heading":[{"event_custom_title":"Abstract","event_custom_details":" The explosion in availability of data has enabled organizations to collect wealth of information for their business operations. In this talk, I will share my experience in using a data-driven approach for improving Operations decisions from two settings: Healthcare and Aviation. I will highlight the opportunities and challenges in a data driven research approach for operational decision making.<\/p>\n In the first half of the talk, I will discuss the challenge of improving the efficiency of surgical procedures which account for approximately 60% of the operating cost of a hospital the United States. Hospitals spend several million dollars annually on instrument sterilization, instrument tray assembly, and instrument repurchase costs. However, in a large majority of hospitals, less than 20%\u201330% of reusable instruments supplied to a surgery are used on average. We obtained actual surgical instrument usage at a large multispecialty hospital in partnership with OpFlow, a healthcare software company. We formulate a data-driven mathematical optimization model for surgical tray configuration and assignment with the goal of reducing costs of unused instruments, such as sterilization, instrument purchase, and tray assembly costs. Our solution was implemented at the UNC Rex Hospital, and we report on the results of our implementation. This analysis has quantified the value of collecting point-of-usage data to be at least $1.39 million per year from using the model-recommended solution at the hospital.<\/p>\n In the second half of the talk, I will discuss the challenge of flight delays in the aviation sector which impacts airlines\u2019 operating cost including increased expenses for crew, fuel, and maintenance. Propagated delays due to late arriving aircraft contribute to 40% of all flight delays as reported by the Bureau of Transportation Statistics. The aircraft assignment problem is to assign tail numbers on scheduled arriving flights at an airport to scheduled departing flights at the same airport with the objective of minimizing propagated delays. In this paper, we propose a new data-driven approach for the aircraft assignment problem by formulating it as a balanced assignment problem between incoming and outgoing flights flown by the same aircraft type at the major hub airports. We propose a data-driven clustering method to account for factors such as the originating airport, time of day, and aircraft type that affect the primary delay distribution. These empirical cluster-based aircraft assignment costs serve as an input to our stochastic assignment model. These assignment costs are then used to derive the optimal stochastic aircraft assignment for an out-of-sample data set for Delta Airlines at its three largest hub airports. We show that the stochastic assignment derived from the data-driven approach performs 2.31% better than the benchmark FIFO assignment in total propagated delay at these hub airports.<\/p>\n"}],"event_enquiry_details":{"event_enq_full_name":"","event_enq_department":"","event_enq_email":"","event_enq_telephone":"","event_enq_website":""}}]