Data-Driven Order Fulfillment Consolidation for Online Grocery Retailing

Improving fulfillment efficiency is critical for long-term sustainability of online grocery retailing. In this paper, we study reducing order fulfillment cost by order consolidation. Motivated by the observation that a significant percentage of buyers place multiple orders within a short time interval, we propose a scheme that attempts to consolidate such “multiorders” to reduce the number of parcels and hence, the shipping cost. At the same time, it cannot significantly disturb the existing order fulfillment process or undermine the customer service level. Successful execution of the scheme requires a prediction of multiorder probabilities and a control policy that selectively prioritizes order processing. For the prediction task, we formulate a binary classification problem and use machine-learning algorithms to predict in real time the probability of a multiorder. For the control task, our proposal is to hold arriving orders in a temporary order pool for potential consolidation and to determine the release timing by a dynamic program. The proposed solution is estimated to capture 92.8% of all the multiorders at the cost of holding the orders for about 20.3 minutes on average. This translates to more than 10 million U.S. dollars of order fulfillment cost saving annually.