Data-driven dynamic pricing and ordering with perishable inventory in a changing environment

Speaker: Prof. Jing-Sheng (Jeannette) Song

Time: 20: 30-22:30, 8th July 2022 (Beijing time)

Tencent ID: 277-938-432

Code: 706708

Abstract: We consider a grocery retailer selling a perishable product in a dynamic environment where consumers’ price sensitivity changes at unknown times (due to pandemics, weather events, etc.), and the product perishes at an unknown rate. We design online price experiments for learning about these unknown features over time. We then prescribe how to use the newly gained knowledge and the most up-to-date data to make informed joint pricing and inventory ordering decisions. Depending on whether the demand shock distribution is parametric or nonparametric, we design two versions of the data-driven pricing and ordering (DDPO) algorithm with the best achievable performance guarantee. Implementing our algorithm on a real-life data set from a supermarket chain, we show that our data-driven, learning-and-earning approach significantly outperforms the historical decisions of the supermarket chain by reducing the profit loss due to uncertainty by over 80%. In particular, avoiding active learning for price-sensitivity changes leads to an annual profit loss of over 62 million U.S. dollars; avoiding active learning for perishability results in a yearly profit loss of over 11 million U.S. dollars.

Bio:Jing-Sheng Song教授为美国杜克大学富卡商学院R. David Thomas讲席教授。长期致力于供应链管理与运营战略领域的研究，研究方向包括库存和物流系统规划与设计、3D打印、动态定价、全球供应链风险管理和社会责任；在国际主流期刊上发表学术论文70余篇，包括运作管理领域顶级期刊Management Science、Operations Research、Production and Operations Management、Manufacturing & Service Operations Management。Jing-Sheng Song教授为教育部长江讲座教授、中国自然科学基金委海外杰出青年、INFORMS Fellow、MSOM Fellow；目前担任Management Science和Service Science部门主编、曾担任Operations Research区域主编和IIE Transactions部门主编。