Machine Learning & Supply Chain Management Workshop

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Machine Learning and Supply Chain Management Workshop
December 13-14, 2021

WORKSHOP OVERVIEW

The TRIPODS+X workshop on Machine Learning & Supply Chain Management features speakers from academia and industry whose research and practice makes innovative use of machine learning (ML) for making decisions in supply chains. Most recent applications of ML for supply chains fall under the categories of either descriptive or predictive analytics. For example, clustering methods have been used to segment customers or suppliers (descriptive), and deep neural networks have been applied to forecast demands (predictive). In contrast, the focus of this workshop is on the use of ML for prescriptive analytics within the supply chain—on using ML to optimize supply chains.

The aim of this workshop is to bring together researchers from both the ML and supply chain communities in order to foster a vibrant exchange of ideas and to stimulate new collaborations. The workshop will feature approximately 12 invited speakers, a poster session for students, and a panel discussion. Graduate students will be invited to a one-day training event immediately preceding the conference, where they will learn the basics of supply chain management and relevant ML approaches, and where they will have an opportunity to perform coding exercises and to develop simple algorithmic approaches. The workshop is funded by an NSF TRIPODS+X grant and is being hosted by the Institute for Data, Intelligent Systems, and Computation (I-DISC) at Lehigh. 

Confirmed Speakers* 

* Please note: subject to change due to ongoing uncertainities on travel restrictions/guidelines within the USA and internationally due to COVID. 

Organizers: (Left) Larry Snyder & (Right) Jan Van Mieghem

Organizers

  • Larry Snyder, Professor, Industrial & Systems Engineering and Director, I-DISC, Lehigh University.

  • Jan A Van Mieghem, A.C Buehler Professor and Professor of Operations, Kellogg School of Management, Northwestern University.