There is a very positive 10 year outlook for hiring and STEM innovation in the supply chain, and a growing entry-level STEM workforce seeking opportunity in applied data science. These two forces, combined with the relative “newness” of university training programs focused on the supply chain, create a knowledge gap between the typical entry-level data scientist seeking employment and the knowledge and skills needed to succeed as a leader of the supply chain of the future. The purpose of this talk is to close that gap, to help entry level and mid career “citizen scientists” understand these forces in supply chain innovation, and to generate conversations on how to match supply chains in the private and public sectors with data scientists who can lead in sustainable, socially responsible supply chain innovation.
We are surrounded by, interact with, and are dependent on a large set of highly complex supply chain networks. These private sector and public sector network interact with each other, creating massive amounts of high value data assets, in an ecosystem that is primed for smart and socially responsible application of artificial intelligence and machine learning. This talk focuses on how these supply chain networks has become so integral to day-to-day human activity in ways that the common person often overlooks, but that are fertile development opportunities for data scientists. A brief history, current state-of-the-science, and 10 year vision of enhanced AI in the supply chain will be presented, followed by deeper illustration of 3 central machine learning use cases: enterprise-scale multivariate demand forecasting, human-computer interface enhancement, and IOT. The presenter will describe algorithms with broad application in the supply chain, the method of productizing algorithms for commercial distribution, and the skill set needed for an entry level data scientist to move into this type of work.
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Applied Machine Learning Conference.
