Stall Economy: The Value of Mobility and Precision Deployment of Retail on Wheels

Abstract

Urban open space emerges as a new territory to embrace retail innovations. Selling products in public spaces with wheeled stalls can potentially become ubiquitous in our future cities. Transition into such a paradigm is being spurred by the rapidly advancing self-driving technologies. This paper provides models, theory, and insights concerning how to deploy and operate autonomous wheeled vending stalls to scale up the stall economy. The central challenge here is to understand the interdependence between customer wait, walk, and stall movements. We address this challenge by proposing spatial-queueing models for two service modes: i) on-demand, first-in-first-serve, and ii) spatially and temporally pooling customer demands. In each mode, we derive the dependence of customer waiting and stall repositioning on two key decisions: the service zone size and the walking distance imposed on customers to meet a stall. In particular, for the on-demand mode, we propose and solve a Rendezvous Problem to analytically characterize the spatial distribution of the stall-customer meeting locations. We also propose a stylized joint truck-stall routing model to capture the inventory replenishment operations. Integrating these results leads to a stall-economy planning problem, which is then calibrated in a realistic setting. Our main finding is that the stall economy potentially profits more than stationary retail, not only because of the mobility of stalls for providing proximity to customers, but also because of its operational flexibilities that allow for avoiding the last 100 meters and pooling demands. In addition, flexible stall deployment and operations address customers' being less willing to wait or walk and can prolong the shopping time, each of which poses a different operational challenge. In a broader sense, this work looks toward an expanded scope of future retail, empowered by self-driving technologies.