How to Make Sure Machine Learning Has an Impact on Supply Chains, Polly Mitchell-Guthrie, Kinaxis
Abstract: The tumult brought on by the pandemic has jumpstarted interest in applying machine learning and automation to address supply chain problems, but adoption rate is still low. Corporate initiatives around AI, machine learning, and data science abound, but measurements of their failure rate range are as high as 87%. The problem space is rich in complexity and worthy of research advancements, so what is required to translate innovation into value on the ground? Solving supply chain problems means thinking end to end across the entire supply network, but it also means thinking end to end across the analytical life cycle, from business problem framing to modeling to MLOps and maintenance. Anything less will fall short on delivering results. This talk will address practical considerations for incorporating machine learning into supply chains based on experience across many different companies in a variety of industries.
BioSketch: Polly is the VP of Industry Outreach and Thought Leadership at Kinaxis, a supply chain planning and analytics software company. Previously, she was Director of Analytical Consulting Services at the University of North Carolina Health Care System and worked in various roles at SAS, in Advanced Analytics R&D, as Director of the SAS Global Academic Program, and in Alliances. She has an MBA from the Kenan-Flagler Business School of the University of North Carolina at Chapel Hill, where she also received her BA in Political Science as a Morehead Scholar. She has been very active in INFORMS (the leading professional society for operations research and analytics) and co-founded the third chapter of Women in Machine Learning and Data Science (now more than 60 chapters worldwide).