Case Study: Using Machine Learning to Manage Perishable Inventory & Reduce Costs

Case Study: Using Machine Learning to Manage Perishable Inventory & Reduce Costs

fri15dec11:00 amfri12:00 pmCase Study: Using Machine Learning to Manage Perishable Inventory & Reduce Costs

Event Details

Presenters:

Kevin Shang, Joseph J. Ruvane, Jr. Distinguished Professor of Business Administration, Fuqua School of Business, Duke University

Description:

In this exclusive webinar for CMA members, award-winning professor Kevin Shang of Duke University’s prestigious Fuqua School of Business takes us through a study of a fast and efficient data-driven solution for replenishing perishable goods for one-warehouse-multi-retailer systems using Machine Learning. Attendees will learn how this solution incorporates real-time information to predict the demand and manage inventory. Referred to as the Data-Driven Taylor Approximation (DDTA) Policy, this significantly outperforms existing methods in both computational speed and inventory costs. Tested with real data from Fresh Hema, the DDTA Policy demonstrates a notable reduction in average costs, maintaining its effectiveness even under varied demand conditions.

Time

December 15, 2023 11:00 am - 12:00 pm(GMT-05:00)

MEMBERS REGISTER HERE

Members Register Here
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