Capacity Planning for O2O On-Demand Delivery Systems with Crowd-Sourcing

Abstract

The online-to-offline (O2O) e-commerce business model enables customers to place orders online and receive products/services offline. The key element for converting online traffic to offline products/services is a highly efficient logistics system. Compared with conventional e-commerce, the O2O logistics network is characterized by highly time-sensitive, fluctuating and large-volume fulfillments. It is therefore necessary to leverage crowd-sourcing to deliver on-demand fulfillments. Although its drivers are flexible, crowd-sourcing brings supply fluctuation and issues with incentives to capacity management. Therefore, managing the capacity plan for an O2O on-demand system with crowd-sourcing is challenging. Because this business model is emerging, there is lack of both academic and operational research on O2O capacity management. Our research builds analytical models to determine the optimal capacity and staffing plan to minimize total logistics costs. We apply a data set from one of the largest O2O platforms in China to demonstrate our model. Our analysis shows that (i) the capacity plan priority is first part-time crowd-sourced drivers, then full-time crowd-sourced drivers, and finally in-house drivers; while the order assignment priority reverses; (ii) setting a proper guaranteed minimum order level and using single service mode for full-time crowd-sourced drivers can significantly reduce the unfulfilled rate and total cost. Moreover, customizing the design of these schemes further enhances their potential. We expect these results to shed light on cost control and provide a model for crowd-sourcing which can improve the efficiency of O2O on-demand businesses.

Keywords: O2O, crowd-sourcing, capacity plan, order assignment