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- Title
A Multiobjective Optimization for Clearance in Walmart Brick-and-Mortar Stores.
- Authors
Yixian Chen; Mehrotra, Prakhar; Samala, Nitin Kishore Sai; Ahmadi, Kamilia; Jivane, Viresh; Linsey Pang; Shrivastav, Monika; Lyman, Nate; Pleiman, Scott
- Abstract
We developed a novel multiobjective markdown system and deployed it across many merchandising units at Walmart. The objectives of this system are to (1) clear the stores' excess inventory by a specified date, (2) improve revenue by minimizing the discounts needed to clear shelves, and (3) reduce the substantial cost to relabel merchandise in the stores. The underlying mathematical approach uses techniques such as deep reinforcement learning, simulation, and optimization to determine the optimal (markeddown) price. Starting in 2019, after six months of extensive testing, we implemented the new approach across all Walmart stores in the United States. The result was a highperformance model with a price-adjustment policy tailored to each store. Walmart increased its sell-through rate (i.e., the number of units sold during the markdown period divided by its inventory at the beginning of the markdown) by 21% and reduced its costs by 7%. Benefits that Walmart accrues include demographics-based store personalization, reductions in operating costs with limited numbers of price adjustments, and a dynamic time window for markdowns.
- Subjects
UNITED States; RETAIL stores; REINFORCEMENT learning; DEEP learning; OPERATING costs; COST control
- Publication
INFORMS Journal on Applied Analytics, 2021, Vol 51, Issue 1, p76
- ISSN
2644-0865
- Publication type
Article
- DOI
10.1287/inte.2020.1065