Over the past two years, you’ve likely heard “global supply chain” repeated over and over in news reports and everyday discourse.
Pandemic-borne story lines abound: empty shelves at the local Costco, container ships cruising the Pacific Coast in search of an accessible port of entry, car salespeople playing cornhole in dealership showrooms because there were simply no cars to sell.
With this global crisis swirling in the backdrop, Lehigh’s Institute for Data, Intelligent Systems, and Computation (I-DISC), with support from the National Science Foundation’s TRIPODS+X program, convened a group of top researchers from across the country and around the world to explore innovative approaches to strengthening the global supply chain.
“Most of the prominent recent applications of machine learning for supply chains have been focused on descriptive or predictive analytics,” says Larry Snyder, a professor of industrial and systems engineering and co-director of I-DISC. “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.”
Snyder, who co-organized the December 2021 event, says the TRIPODS+X Workshop on Machine Learning & Supply Chain Management took a somewhat different direction: “We focused on the use of machine learning for prescriptive analytics within the supply chain—on using the power of machine learning to not just analyze but also optimize efficiency and resiliency across the global supply chain.”
The two-day, hybrid (in-person and online) workshop was held on Lehigh’s Mountaintop Campus and featured 13 invited speakers, a poster session for students, and a panel discussion to promote further exploration at the intersection of machine learning and supply chain management. Participants (including academic, industry, and government researchers focused on supply chain and logistics, artificial intelligence and machine learning, or associated fields) represented more than 70 academic institutions and numerous companies based in the United States, Europe, the Middle East, Asia, and South America.
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