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Developing and Implementing Machine Learning Software at Home Depot

Developing and Implementing Machine Learning Software at Home Depot

Herbert Remidez, Sri Beldona
Copyright: © 2021 |Volume: 23 |Issue: 4 |Pages: 10
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781799859192|DOI: 10.4018/JCIT.293284
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MLA

Remidez, Herbert, and Sri Beldona. "Developing and Implementing Machine Learning Software at Home Depot." JCIT vol.23, no.4 2021: pp.1-10. http://doi.org/10.4018/JCIT.293284

APA

Remidez, H. & Beldona, S. (2021). Developing and Implementing Machine Learning Software at Home Depot. Journal of Cases on Information Technology (JCIT), 23(4), 1-10. http://doi.org/10.4018/JCIT.293284

Chicago

Remidez, Herbert, and Sri Beldona. "Developing and Implementing Machine Learning Software at Home Depot," Journal of Cases on Information Technology (JCIT) 23, no.4: 1-10. http://doi.org/10.4018/JCIT.293284

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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.