Managing the Personalized Order-Holding Problem in Online Retailing

Problem definition: A significant percentage of online consumers place consecutive orders within a short duration. To reduce the total order arrangement cost, an online retailer may consolidate consecutive orders from the same consumer. We investigate how long the retailer should hold the consumer’s orders before sending them to a third-party logistics provider (3PL) for processing. In this order-holding problem, we optimize the holding time to balance the total order arrangement cost and the potential delay in delivery. Methodology/results: We model the order-holding problem as a Markov decision process. We show that the optimal order-holding decisions follow a threshold-type policy that is straightforward to implement: Hold any pending orders if the holding time is within a threshold or send them to the 3PL otherwise. Whenever the consumer places a new order, the holding time is reset, and the threshold is updated based on a cumulative set of the past consecutive orders in the consumer’s shopping journey. Using a consumer’s sequential decision model, we personalize the threshold by finding its closed-form expression in the consumer’s order features. We determine the model’s coefficients and evaluate the threshold-type policy using the data of the 2020 MSOM Data Driven Research Challenge. Extensive numerical experiments suggest that the personalized threshold-type policy outperforms two commonly used benchmarks by having fewer order arrangements or shorter holding times. Furthermore, personalizing the order-holding decisions is significantly more valuable for “enterprise” customers. Managerial implications: Our research suggests a higher threshold for consumers who are more likely to place consecutive orders within a short duration. The consumers’ demographic information has a significant effect on the threshold. Specifically, the threshold is higher for “plus” consumers, female consumers, and consumers in the age group of 16–25 years. The threshold for tier 1 cities is lower than that for tier 2 to tier 4 cities but higher than that for tier 5 cities. History: This paper has been accepted for the Manufacturing & Service Operations Management Data Driven Challenge. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71931009, 72201237, and 72231009], the Research Grants Council of Hong Kong [Grants 15501920 and 15501221], the Singapore Ministry of Education Academic Research Fund [Tier 1, Grant RG17/21; Tier 2, Grant MOE2019-T2-1-045], the Association of South-East Asian Nations Business Research Initiative Grant [Grant G17C20421], and the Neptune Orient Lines [Fellowship NOL21RP04]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1201 .