An Unintended Consequence of Platform Dependence: Empirical Evidence from Food-Delivery Platforms

Abstract

Food waste is a severe economic and social problem. Restaurants contribute significantly to food waste because they face the classic trade-off between speed of service and leftover inventory, which is particularly crucial in the context of quick service restaurants (QSRs). To offer a high speed of service, QSRs pre-cook most of their food, but they can hold it only for a short time. To effectively manage this trade-off, QSRs have become increasingly reliant on demand forecasts. However, online food-delivery platforms that connect restaurants, riders/drivers, and consumers are growing in popularity, and it is unclear how the growth of food-delivery platforms impacts the ability of restaurants to accurately forecast their demand. We empirically investigate the impact of food-delivery platforms on the demand forecast error in QSRs and analyze the underlying mechanism. We find that as customers become increasingly dependent on food-delivery platforms, QSR demand becomes harder to forecast. We also find that the majority of the increase in overall forecast error is due to an increase in the error associated with the demand pattern and a smaller portion is due to error in forecasting demand amplitude. Based on our results, we offer suggestions for QSRs on how to manage their relationship with food-delivery platforms to decrease their forecast error and increase operational effciency.

Keywords: Food-Delivery Platforms, Quick Service Restaurants, Forecasting, Econometrics