Problem Definition: We consider the setting where a retailer with many physical stores and an online presence seeks to fulfill online orders using an omnichannel fulfillment program such as buy-online ship-from-store. These fulfillment strategies try to minimize cost while fulfilling orders within acceptable service times. We focus on single-item orders. Typically, all online orders for the item are sent to a favorable set of locations to be filled. Failed trials are sent back for further stages of trial fulfillment until the process times out. The multi-stage order fulfillment problem is thus an interplay of the pick failure probabilities at the stores where they may be shipped from, and the picking, shipping and cancellation costs from these locations. Methodology/Results: We model the problem as one of breaking up the order into parts that are attempted to be picked and shipped by assigning them to the most cost-effective stores in multiple stages. We solve the fulfillment problem optimally by taking into account the changing pick-failure probabilities as a result of other online order fulfillment trials, by casting it as a network flow problem with convex costs. We incorporate this as the second stage of a two-stage online order acceptance problem, and generalize earlier results of Jeremy Karp, 2017. Models and Methods for Omni-channel Fulfillment (Doctoral dissertation, Carnegie Mellon University) to the case with pick failures at stores. We investigate the real-world performance of our methods and models on real order data of several of the top US retailers that use our collaborating e-commerce solutions provider to optimize their fulfillment strategies. Managerial Implications: Our work enables retailers to incorporate pick failure in their order management systems for ship-from-store programs. Our new online order-acceptance policies that take into account pick-failures can thus create significant savings for omnichannel retailers.
Short Bio: Sagnik Das is an Operations Research PhD student at the Tepper School of Business, Carnegie Mellon University. He is working on problems to make supply chains efficient, robust, and resilient. In his research, he uses data-driven approaches to solve real-world applications.