Partner with a Third-Party Delivery Service or Not? — a Prediction-and-Decision Tool for Restaurants Facing Takeout Demand Surges During a Pandemic

Jia, Huiwen and Shen, Siqian and Garcıa, Jorge Alberto Ramırez and Shi, Cong, Partner with a Third-Party Delivery Service or Not? — a Prediction-and-Decision Tool for Restaurants Facing Takeout Demand Surges During a Pandemic (November 18, 2020). Available at SSRN: https://ssrn.com/abstract=3734018 or http://dx.doi.org/10.2139/ssrn.3734018

Amidst the COVID-19 pandemic, restaurants become more reliant on no-contact pick-up or delivery ways for serving customers. As a result, they need to make tactical planning decisions such as whether to partner with online platforms, to form their own delivery team, or both. In this paper, we develop an integrated prediction-decision model to analyze the profit of combining the two approaches and to decide the needed number of drivers under stochastic demand. We first employ the Susceptible-Infected-Recovered (SIR) model to forecast future infected cases in a given region and then construct an autoregressive-moving-average (ARMA) regression model to predict food-ordering demand. Using predicted demand samples, we formulate a stochastic integer program to optimize food delivery plans. We conduct numerical studies using COVID-19 data and food-ordering demand data collected from local restaurants in Nuevo Leon, Mexico, from April to October 2020, to show results for helping restaurants build contingency plans under rapid market changes. Our method can be used under unexpected demand surges, various infection/vaccination status and demand patterns. Our results show that a restaurant can benefit from partnering with third-party delivery platforms when (i) the subscription fee is low, (ii) customers can flexibly decide whether to order from platforms or from restaurants directly, (iii) customers require more efficient delivery, (iv) average delivery distance is long, or (v) demand variance is high.