OPERATIONS, TECHNOLOGY, AND INFORMATION MANAGEMENT


Learning Newsvendor Problems with Intertemporal Dependence and Moderate


MENG QI ASSISTANT PROFESSOR


Cornell Peter and Stephanie Nolan School of Hotel Administration


Cornell SC Johnson College of Business Cornell University


Co-authors • Meng Qi


Assistant Professor, Cornell Peter and Stephanie Nolan School of Hotel Administration, Cornell SC Johnson College of Business, Cornell University


• Zuo-Jun (Max) Shen, University of Hong Kong • Zeyu Zheng, University of California Berkeley


Summary Inventory management has been an active research topic for more than a cen-


tury. Today’s e-commerce companies face new challenges of customer diver- sity, the increasing variety of products, and the higher level of service required. For example, on large e-commerce platforms (such as Amazon and JD.com), hundreds of millions of products are simultaneously sold with various de- mand patterns that require different replenishment strategies. Hence, it is critical to develop a framework that is able to identify the optimal/close-to-op- timal strategy automatically for different demand because they can’t manage these many products efficiently by current practices.


Te authors propose a data-driven, end-to-end framework to automate reple- neishment decisions for a large number of SKUs, considering the multiperiod inventory management problem over a finite horizon, proposing a dynamic labeling. Teir results contribute to the literature by introducing an end-to- end framework for the multiperiod replenenishment problem, addressing various demand patterns, and innovating the design of deep neural network structure which reduces computational complexity and the number of weights in magnitude, while achieving good performance.


Non-stationarities Production and Operations Management, 33, 5, May 2024 LINK TO PAPER LINK TO MENG QI VIDEO


CONTENTS TO MAIN


| RESEARCH WITH IMPACT: CORNELL SC JOHNSON COLLEGE OF BUSINESS • 2024 EDITION


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