Inventory Control and Learning for Fast-Fashion Industry with Censored Demand

Workshop’s Topic: Motivated by our collaboration with one of the largest fast-fashion retailers in Europe, we study an (in-season) inventory management problem for the fast-fashion industry when the demand distribution is unknown. This system has a central warehouse that receives an initial replenishment and distributes its inventory to multiple stores in each time period during a finite horizon. By observing the censored demand, the firm has to jointly lead the demand and make inventory control decisions on the fly. We first develop a learning algorithm based on empirical demand distribution and prove a worst-case bound on its theoretical performance when the demand information is uncensored, Then, in the censored demand case, we propose a more sophisticated algorithm based on a primal-dual learning and optimization approach, Results show that both algorithms have great theoretical and empirical performances.

Time and Location: 15:30-17:00 PM (GMT+8), Room A523 (School of Management)

Language: English

Introduction of Speakers

Prof. Mehmet Gumus

McGill University, Faculty of Management

Mehmet Gumus is a Professor of Operations Management at the Faculty of Management at McGill University. He joined McGill in 2007 from the University of California at Berkley where he completed his Ph.D. in Industrial Engineering and Operations Research and M.A. in Economics. In his research, the focus is on supply chain management, dynamic pricing, and risk management. His papers are accepted for publication in Management Science, Operations Research, Manufacturing and Service Operations Management, Marketing Science and Production and Operations Management. He serves as AE for Production and Operations Management, And IE Transportations. In 2017, he developed a new specialized Masterss Program in Management Analytics (MMA) and since its inauguration, he is managing the MMA program as the Academic Director. In 2015, he co-founded Plannica Inc. to develop an integrated Decision Support System for the supply chain planning solution and implemented Plannica (R) in various industries.