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

• 成果类型: Article
• 署名作者: Qi, Meng; Shi, Yuanyuan; Qi, Yongzhi; Ma, Chenxin; Yuan, Rong; Wu, Di; Shen, Zuo-Jun (Max)
• 署名单位: Cornell University; University of California System; University of California San Diego; University of California System; University of California Berkeley; University of Hong Kong; University of Hong Kong
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.4564
• 发表日期: 2023
• 页码: 759-773
• 关键词: end-to-end decision-making; Inventory management; Deep learning; E-commerce

摘要

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.