Information Design and Coordination in Cloud Kitchens -How Human-Tech Overrides affect Order Fulfillment time?

<div> Problem definition: Cloud or ghost kitchens operate without store fronts, and serve take-out and home-delivery customers. Recently, multi-brand cloud kitchens that mimic food courts have emerged, allowing customers to order from multiple brands in a single order. Coordinating such orders poses unique challenges, making the fulfillment process critical. We investigate the interplay between brand chefs and the platform's intended fulfillment process. Specifically, we examine the extent of information the platform should share with chefs for orders requiring coordination between brands, the consequences of chefs independently prioritizing tasks, and whether the platform should prioritize orders that demand coordination.&nbsp;<span>Methodology and results: We collaborate with a large Indian multi-brand cloud kitchen platform and collect micro-level data on 6.6 million orders spanning 16 months. Our empirical analysis revealed: (1) Orders involving multiple brands account for only 0.01% to 2% of total orders but experience fulfillment times that are 50% longer than single-brand orders. (2) To boost sales, these brands were collaborating by including dishes from other brands in their menus. Therefore, 30-40% of orders that appear to be single-brand actually consist of dishes from multiple brands, which surprisingly have similar fulfillment times to single-brand orders. Field visits revealed this is due to chefs having more information about such orders and, subsequently, overriding the platform's chosen sequence.&nbsp;</span> </div> <div> Using a fork-join queuing network model and the Simulated Method of Moments (SMM) approach, we evaluate fulfillment times and quality metrics under various alternate policies. Managerial implications: Sharing more information about multi-brand orders with chefs enables the platform to achieve Pareto improvement wherein it can not only reduce the order fulfillment time (by 0.10-4.7%) and improve quality of temperature-sensitive orders (by 0.01 - 2.44%) over the current policy across different order classes but also create more equity in outcomes across order classes. </div>