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

Author

Listed:
  • Huiwen Jia

    (Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

  • Siqian Shen

    (Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

  • Jorge Alberto Ramírez García

    (Department of Industrial and Operations Engineering, University of Monterrey, 66238 Monterrey, Mexico)

  • Cong Shi

    (Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

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 use 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.

Suggested Citation

  • Huiwen Jia & Siqian Shen & Jorge Alberto Ramírez García & Cong Shi, 2022. "Partner with a Third-Party Delivery Service or Not? A Prediction-and-Decision Tool for Restaurants Facing Takeout Demand Surges During a Pandemic," Service Science, INFORMS, vol. 14(2), pages 139-155, June.
    • Handle: RePEc:inm:orserv:v:14:y:2022:i:2:p:139-155
    DOI: 10.1287/serv.2021.0294
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    References listed on IDEAS

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    Cited by:

    1. Liu, Minjian & Dong, Qi & Li, Yunbing & Du, Shaofu, 2026. "Navigating trade-offs in online food delivery: The interplay of buy-online-and-pick-up-in-store and delay insurance," International Journal of Production Economics, Elsevier, vol. 291(C).
    2. Fu, Genshen & Chi, Yujie & Zheng, Li & Shen, Zuo-Jun Max, 2026. "A Deep & Cross Network-based framework for online food delivery time prediction with driver-specific information," Omega, Elsevier, vol. 139(C).
    3. Liu, Minjian & Du, Shaofu & Nie, Tengfei & Zhu, Yangguang, 2024. "Pricing, channel design, and platform operations with buy-online-and-pick-up-in-store in online food delivery market," International Journal of Production Economics, Elsevier, vol. 274(C).
    4. Linxuan Shi & Zhengtian Xu, 2024. "Dine in or Takeout? Trends on Restaurant Service Demand amid the COVID-19 Pandemic," Service Science, INFORMS, vol. 16(4), pages 241-271, December.
    5. Zheng, Zhijun & Li, Gang & Cheng, T.C.E & Wu, Feng, 2022. "Offline supplementary service strategies for the online marketplace: Third-party service or marketplace service?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).

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