Using Machine Learning to Smartly Stock Grocery Stores

Consider the unassuming Chinese cabbage. Low in calories yet rich in nutrients, it’s a staple on dinner tables around China, but when Chinese grocery stores stock too much of it as well as other highly perishable fruits and vegetables, already razor-thin profit margins take a hit.

Thanks to a new data-driven replenishment and allocation policy developed by Duke Fuqua School of Business professor Kevin Shang, the fast-growing grocery chain Fresh Hema, a subsidiary of internet retail giant Alibaba, may soon see an almost 11% cost savings for this item, a finding that could mean similar savings for other stock-keeping-units that must be swiftly sold or thrown away.

Shang is co-author of “Taylor Approximation of Inventory Policies for One-Warehouse, Multi-Retailer Systems with Demand Feature Information” which will be published in the journal Management Science. He and his co-authors at Zhejiang University built an innovative machine-learning algorithm for Fresh Hema based on six months of its daily inventory sales and other factors such as weather, shopper demographics, income, genders, age, seasonal variations, and unique characteristics of its stores in Chengdu, a city of 20 million in southern China.