Rushing to adopt AI? Watch out for speed bumps.

This is an excerpt of the original article. It was written for the July-August 2018 edition of Supply Chain Management Review. The full article is available to current subscribers.

July-August 2018

At Supply Chain Management Review, we’ve been writing about the talent crisis in our profession since at least 2012 when our MIT contributors were publishing a column on talent strategies. Last winter, the topic touched home when I picked up my local newspaper one Saturday morning. One of the lead stories was about two initiatives launched by C&S Wholesale Grocers with two local academic institutions: Keene State College and Franklin Pierce University.

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Artificial intelligence (AI) along with the AI application known as machine learning have captured the imaginations of supply chain professionals, and many companies are pursuing AI projects.

While the technology is starting to make its mark in the supply chain domain, practitioners should not underestimate the amount of preparatory work that needs to be done to provide a strong foundation for AI applications that can yield real value.

Varied applications

Definitions of AI refer to the technology as a branch of computer science that focuses on the use of machines that mimic or simulate human intelligence. Machine learning is essentially the process by which machines learn by analyzing data and spotting patterns and relationships.

Applications of these technologies are being developed in many areas of supply chain management. Here are some highlighted at the MIT Center for Transportation & Logistics’ 2018 Crossroads conference, which took place on April 17th, 2018.

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