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

Speaker: Prof.Mehmet Gumus,McGill University

Venue: SOM A523 meeting room

Abstract: Motivated by our collaboration with one of the largest fast-fashion retailers in Europe, we study a (in-season) inventory management problem for 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 learn 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. Result show that both algorithms have great theoretical and empiral performances.