Faster Deliveries and Smarter Order Assignments for an On-Demand Meal Delivery Platform

Abstract

Academic/Practical Relevance: Our intent is to identify the underlying factors and develop an order assignment policy that can help an on-demand meal delivery service platform to grow. Methodology: By analyzing transactional data obtained from an online meal delivery platform in Hangzhou (China) over a two-month period in 2015, we examine the impact of meal delivery performance on a customer's future orders. Through a simulation study, we illustrate the importance of incorporating our empirical results into the development of a smarter "order assignment policy". Results: We find empirical evidence that an "early delivery'' is positively correlated with customer retention: a 10-minute earlier delivery is associated with an increase of one order per month from each customer. However, we find that the negative effect on future orders associated with "late deliveries'' is much stronger than the positive effect associated with "early deliveries". Moreover, we show empirically that a driver's individual local area knowledge and prior delivery experience can reduce late deliveries significantly. Finally, through a simulation study, we illustrate how one can incorporate our empirical results in the development of an order assignment policy that can help a platform to grow its business through customer retention. Managerial Implications: Our empirical results and our simulation study suggest that to increase future customer orders, an on-demand service platform should address the issues arising from both the supply side (i.e., driver's local area knowledge and delivery experience) and the demand side (i.e., asymmetric impacts of early and late deliveries on future customer orders) into their operations.