Effective Online Order Acceptance Policies for Omni-Channel Fulfillment

Problem Definition: Omni-channel retailing has led to the use of traditional stores as fulfillment centers for online orders. Omni-channel fulfillment problems have two components: (1) accepting a certain number of on-line orders prior to seeing store demands, and (2) satisfying (or filling) some of these accepted on-line demands as efficiently as possible with any leftover inventory after store demands have been met. Hence, there is a fundamental trade-off between store cancellations of accepted online orders and potentially increased profits due to more acceptances of online orders. We study this joint problem of online order acceptance and fulfillment (including cancellations) to minimize total costs, including shipping charges and cancellation penalties in single-period and limited multi-period settings.Academic/Practical Relevance: Despite the growing importance of omni-channel fulfillment via online orders, our work provides the first study incorporating cancellation penalties along with fulfillment costs. Methodology: We build a two-stage stochastic model. In the first stage, the retailer sets a policy specifying which online orders it will accept. The second stage represents the process of fulfilling online orders once the uncertain quantities of in-store purchases are revealed. We analyze two classes of threshold policies that accept online orders as long as the inventories are above a global threshold, or a local threshold per region. Results: Total costs are unimodal as a function of the global threshold, and unimodal as a function of a single local threshold holding all other local thresholds at constant values, motivating a gradient search algorithm. Reformulating as an appropriate linear program with network flow structure, we estimate the derivative (using infinitesimal perturbation analysis) of the total cost as a function of the thresholds. We validate the performance of the threshold policies empirically using data from a high-end North American retailer. Our two-store experiments demonstrate that Local Thresholds perform better than Global Thresholds in a wide variety of settings. Conversely, in a narrow region with negatively correlated online demand between locations and very low shipping costs, Global Threshold outperforms Local Thresholds. A hybrid policy only marginally improves on the better of the two. In multiple periods, we study one- and two-location models and provide insights into effective solution methods for the general case.Managerial Implications: Our methods give an effective way to manage fulfillment costs for online orders, demonstrating a significant reduction compared to policies that treat each store separately, reflecting the significant advantage of incorporating shipping in computing thresholds