A Multiobjective Optimization for Clearance in Walmart Brick-and-Mortar Stores

Yixian Chen (), Prakhar Mehrotra (), Nitin Kishore Sai Samala (), Kamilia Ahmadi (), Viresh Jivane (), Linsey Pang (), Monika Shrivastav (), Nate Lyman () and Scott Pleiman ()
Additional contact information
Yixian Chen: Walmart Labs, Sunnyvale, California 94086;
Prakhar Mehrotra: Walmart Labs, Sunnyvale, California 94086;
Nitin Kishore Sai Samala: Walmart Labs, Sunnyvale, California 94086;
Kamilia Ahmadi: Walmart Labs, Sunnyvale, California 94086;
Viresh Jivane: Walmart Labs, Sunnyvale, California 94086;
Linsey Pang: Walmart Labs, Sunnyvale, California 94086;
Monika Shrivastav: Walmart Labs, Sunnyvale, California 94086;
Nate Lyman: Walmart, Bentonville, Arkansas 72716
Scott Pleiman: Walmart, Bentonville, Arkansas 72716

Interfaces, 2021, vol. 51, issue 1, 76-89

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 (marked-down) 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 high-performance 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.

Keywords: merchandising; price optimization; inventory optimization; deep reinforcement learning; Edelman Award (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1287/inte.2020.1065 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:51:y:2021:i:1:p:76-89

Access Statistics for this article

More articles in Interfaces from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().