讲座：A Data Analytics Approach to Integrated Location, Assortment and Inventory Planning in Omni-Channel Retail
题 目：A Data Analytics Approach to Integrated Location, Assortment and Inventory Planning in Omni-Channel Retail
嘉 宾：沈浩 助理教授 中国人民大学商学院
主持人：宋颖达 副教授 安泰经济与管理学院管理科学系
地 点：上海交通大学徐汇校区 新上院S203
While the rise of e-commerce provided retailers with new avenues to reach customers, fierce competition has pushed retailers to turn to data and analytics to make better business decisions. Observing retailers aggressively expanding in both online and offline channels, we study a data analytics approach that can help an omni-channel retailer to make better location, assortment, and inventory decisions. Facing the online channel and offline retail stores, customers' purchase decisions depend on not only their preferences across products, but also hassle costs such as offline travel costs and online disutility costs. Consequently, the product assortment decision in a local retail store affects the demand in both the online channel and in retail stores at other locations. We address how companies should determine the offline location-dependent assortment as well as the inventory levels of offered products in each retail store to maximize profits across both the online channel and offline retail stores. We incorporate the hassle costs into the customer choice model, propose a sequence of models for the optimal offline assortment and inventory planning problem, and develop efficient solution approaches. We conduct numerical experiments to demonstrate the efficacy of the data analytics approach as well as the integrated model, and draw interesting observations that can be valuable to practitioners.
Hao Shen is an assistant professor at School of Business, Renmin University of China. He received his Ph.D. in Management Science and Engineering, and a B.E. in Engineering Mechanics, both from Tsinghua University. His research interests include supply chain management, flexible operations management, data-driven decision methods, and business analytics. His work has been published or forthcoming in Production & Operations Management, and Manufacturing & Service Operations Management.