Home

[Lecture, Oct 23] Faster Deliveries and Smarter Order Assignments for an On-Demand Meal Delivery Platform

time: 2019-10-21

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

Speaker: Prof. RONG Ying, Antai College of Economics & Management, Shanghai Jiao Tong University

Time: 3:00 - 4:30 pm, October 23, 2019

Venue: Room 210, Building 37, Wushan Campus


Introduction to the speaker:

Dr. Rong Ying is professor and doctoral supervisor with the Antai College of Economics & Management of Shanghai Jiao Tong University. He received his Bachelor’s degree from Shanghai Jiao Tong University, and Master and doctoral degree from Lehigh University. Before joining Shanghai Jiaotong University, Prof. Rong has been a postdoctoral scholar at University of California, Berkeley and Lehigh University. His research fields include Service Operation Optimization, Operation in Emerging Business Models, and Data-Driven Optimization. His research work has been published by international journals such as Management Science, Operations Research, Manufacturing & Service Operations Management, Production and Operations Management, Naval Research Logistics, and IIE Transactions etc. Prof. Rong is the recipient of 2015 National Science Fund for Excellent Young Scholars, and has won several international academic awards including two MSOM Best Paper awards and the INFORMS Energy, Natural Resources & Environment Young Researcher Prize.



Abstract:

The focus of this talk is to identify the underlying factors and develop an order assignment policy that can help an on-demand meal delivery service platform to grow. By analyzing transactional data obtained from an online meal delivery platform in Hangzhou (China) over a two-month period in 2015, 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. 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 customer future orders) into their operations.