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

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 "stall economy" paradigm is being spurred by the recent global pandemic, but has been scarcely studied. This paper provides models, algorithms, and managerial insights to understand how to deploy and operate wheeled stalls in cities to scale up the stall economy. The spatial-queueing models characterize the stall operations of serving customers. The joint truck-stall routing model analytically captures the inventory replenishment operations. The personalized demand learning model effectively estimates customer demand while respecting the increasingly stringent privacy regulations. Combining these results leads to the optimal scheme of citywide stall deployment, which is then calibrated in a realistic setting with real data. The major finding is that the stall economy has potential for tapping large economic opportunities when the customer demand is low or moderate. The stall economy is able to provide high-quality service (in terms of the proximity to customers and the wait time) without incurring significant cost, thanks to the stall mobility, the operational flexibility, and the deployment adaptability. On the other hand, these advantages will diminish as the customer demand scales up. In addition, enhancing service quality by shortening the wait time, shrinking the customer walk distance and prolonging the shopping time poses different operational challenges, but can be accommodated by flexible stall deployment and operations. In a broader sense, this work demonstrates an expanded scope of retail operations reshaped by the pandemic and big data.