Machine Learning & Supply Chain Management Workshop

Iacocca Hall, Lehigh University.
Held: December 13-14, 2021

The TRIPODS+X workshop on Machine Learning & Supply Chain Management featured 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 was on the use of ML for prescriptive analytics within the supply chain—on using ML to optimize supply chains.

The aim of this workshop was 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 featured:

• 13 invited speakers
• poster session for students
• panel discussion

The workshop was funded by a NSF TRIPODS+X grant and hosted by the Institute for Data, Intelligent Systems, and Computation (I-DISC) at Lehigh. 

Schedule of Events

Speaker Abstracts, Bios and Presentation Videos 

Speakers 

• Joren Gijsbrechts, Universidade Católica Portuguesa, Portugal
• Swati Gupta, Georgia Tech, USA
• Andrea Lodi, Cornell Tech, USA
• Polly Mitchell-Guthrie, Kinaxis, USA
• Richard Pibernik, Julius-Maximilians-University, Würzburg, Germany
• Cynthia Rudin, Duke University, USA
• Zuo-Jun Max Shen, UC-Berkeley, USA / Univ. of Hong Kong
• David B. Shmoys, Cornell University, NY, USA
• Larry Snyder, Lehigh University, USA
• Jiankun Sun, Imperial College London, UK
• Martin Takáč, Lehigh University, Bethlehem, USA / Mohamed bin Zayed University of Artificial Intelligence, UAE
• Barrett Thomas, University of Iowa, USA
• Michael Watson, Coupa Software, USA