Developing and Implementing Machine Learning Software at Home Depot

Abstract

This teaching case explores the problem of shelfouts and the use of technology adoption to minimize it. Shelfout, wherein a product is not on the shelf when it is supposed to be, has received renewed interest especially given the fact that many brick-and-mortar stores shut down due to their inability to compete with online vendors. The coronavirus pandemic worsened this problem and companies continue to struggle with the resulting supply chain disruptions. Increasingly consumers are searching for products on the website to confirm product availability before traveling to the store. In this case we show how The Home Depot, Inc., (Home Depot), is addressing shelfouts and the process they went through in solving this problem. Instructors can use this case to introduce students to the machine learning development lifecycle, marketing courses discussing shelfouts, and courses with lessons related to technology implementation.