Researchers to gather at NSF-funded 'Machine Learning & Supply Chain Management Workshop' at Lehigh University on Dec 13-14, 2021
Over the past 18 months, the phrase "global supply chain" has been as prominent as ever across news reporting and popular discussion.
Pandemic-borne stories abound: empty shelves at the local Costco, container ships aimlessly circling the Pacific in search of an accessible port of California entry, car salespeople playing cornhole in dealership showrooms because there are simply no cars on the lot to sell. Not to mention the impact it will all have on families, and the retailers who rely upon them, as the upcoming holiday shopping season approaches.
With this global crisis swirling in the backdrop, Lehigh University's Institute for Data, Intelligent Systems, and Computation (I-DISC), with support from the National Science Foundation's TRIPODS+X program, is convening a group of top researchers from across the country and around the world to explore innovative approaches to strengthening the global supply chain. Academic, industry, and government researchers focused on supply chain and logistics, artificial intelligence and machine learning, or associated fields are invited to attend.
On December 13-14, 2021, preeminent researchers in the fields of artificial intelligence and supply chain management will gather at Lehigh's Iacocca Hall for the "TRIPODS+X Workshop on Machine Learning & Supply Chain Management," featuring speakers from academia and industry, to foster a vibrant exchange of ideas and to stimulate new collaborations. The workshop will feature a dozen invited speakers, a poster session for students, and a panel discussion to promote further exploration at the intersection of these converging fields.
"Most of the prominent recent applications of machine learning for supply chains were focused on descriptive or predictive analytics," says Larry Snyder, a professor of industrial and systems engineering at Lehigh and co-director of I-DISC, who serves as a co-organizer of the TRIPODS+X event. "For example, clustering methods have been used to segment customers or suppliers in a descriptive way, and deep neural networks have been applied predictively to forecast demand. We are taking a bit of a different direction: the focus of our workshop is on the use of machine learning for prescriptive analytics within the supply chain—on using the power of machine learning not just to analyze, but to optimize, efficiency and resiliency across the gloabl supply chain."
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