A Practical End-to-End Inventory Management Model with Deep Learning

Meng Qi (), Yuanyuan Shi (), Yongzhi Qi (), Chenxin Ma (), Rong Yuan (), Di Wu () and Zuo-Jun (Max) Shen ()
Additional contact information
Meng Qi: SC Johnson College of Business, Cornell University, Ithaca, New York 14853
Yuanyuan Shi: Department of Electrical and Computer Engineering, University of California–San Diego, San Diego, California 92161
Yongzhi Qi: JD.com Smart Supply Chain Y, Mountain View, California 94043
Chenxin Ma: JD.com Silicon Valley Research Center, Mountain View, California 94043
Rong Yuan: JD.com Silicon Valley Research Center, Mountain View, California 94043
Di Wu: JD.com Silicon Valley Research Center, Mountain View, California 94043
Zuo-Jun (Max) Shen: SC Johnson College of Business, Cornell University, Ithaca, New York 14853

Management Science, 2023, vol. 69, issue 2, 759-773

Abstract: We investigate a data-driven multiperiod inventory replenishment problem with uncertain demand and vendor lead time (VLT) with accessibility to a large quantity of historical data. Different from the traditional two-step predict-then-optimize (PTO) solution framework, we propose a one-step end-to-end (E2E) framework that uses deep learning models to output the suggested replenishment amount directly from input features without any intermediate step. The E2E model is trained to capture the behavior of the optimal dynamic programming solution under historical observations without any prior assumptions on the distributions of the demand and the VLT. By conducting a series of thorough numerical experiments using real data from one of the leading e-commerce companies, we demonstrate the advantages of the proposed E2E model over conventional PTO frameworks. We also conduct a field experiment with JD.com, and the results show that our new algorithm reduces holding cost, stockout cost, total inventory cost, and turnover rate substantially compared with JD’s current practice. For the supply chain management industry, our E2E model shortens the decision process and provides an automatic inventory management solution with the possibility to generalize and scale. The concept of E2E, which uses the input information directly for the ultimate goal, can also be useful in practice for other supply chain management circumstances.

Keywords: end-to-end decision-making; inventory management; deep learning; e-commerce (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2022.4564 (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:ormnsc:v:69:y:2023:i:2:p:759-773

Access Statistics for this article

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