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Real-Time Delivery Time Forecasting and Promising in Online Retailing: When Will Your Package Arrive?

Author

Listed:
  • Nooshin Salari

    (Rotman School of Management, University of Toronto, Toronto, Ontario M5S 1A1, Canada; Department of Industrial Engineering and Operations Research, University of California, Berkeley, Berkeley, California 94720)

  • Sheng Liu

    (Rotman School of Management, University of Toronto, Toronto, Ontario M5S 1A1, Canada)

  • Zuo-Jun Max Shen

    (Faculty of Engineering & Faculty of Business and Economics, The University of Hong Kong, Hong Kong S.A.R., China; College of Engineering, University of California, Berkeley, California 94720)

Abstract

Problem definition : Providing fast and reliable delivery services is key to running a successful online retail business. To achieve a better delivery time guarantee policy, we study how to estimate and promise delivery time for new customer orders in real time. Academic/practical relevance : Delivery time promising is critical to managing customer expectations and improving customer satisfaction. Simply overpromising or underpromising is undesirable because of the negative impacts on short-/long-term sales. To the best of our knowledge, we are the first to develop a data-driven framework to predict the distribution of order delivery time and set promised delivery time to customers in a cost-effective way. Methodology : We apply and extend tree-based models to generate distributional forecasts by exploiting the complicated relationship between delivery time and relevant operational predictors. To account for the cost-sensitive decision-making problem structure, we develop a new split rule for quantile regression forests that incorporates an asymmetric loss function in split point selection. We further propose a cost-sensitive decision rule to decide the promised delivery day from the predicted distribution. Results : Our decision rule is proven to be optimal given certain cost structures. Tested on a real-world data set shared from JD.com, our proposed machine learning–based models deliver superior forecasting performance. In addition, we demonstrate that our framework has the potential to provide better promised delivery time in terms of sales, cost, and accuracy as compared with the conventional promised time set by JD.com. Specifically, our simulation results indicate that the proposed delivery time promise policy can improve the sales volume by 6.1% over the current policy. Managerial implications : Through a more accurate estimation of the delivery time distribution, online retailers can strategically set the promised time to maximize customer satisfaction and boost sales. Our data-driven framework reveals the importance of modeling fulfillment operations in delivery time forecasting and integrating the decision-making problem structure with the forecasting model.

Suggested Citation

  • Nooshin Salari & Sheng Liu & Zuo-Jun Max Shen, 2022. "Real-Time Delivery Time Forecasting and Promising in Online Retailing: When Will Your Package Arrive?," Manufacturing & Service Operations Management, INFORMS, vol. 24(3), pages 1421-1436, May.
    • Handle: RePEc:inm:ormsom:v:24:y:2022:i:3:p:1421-1436
    DOI: 10.1287/msom.2022.1081
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    References listed on IDEAS

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    Cited by:

    1. Ma, Bohao & Teo, Chee-Chong & Wong, Yiik Diew & Sun, Shanshan, 2025. "Delivery time reliability in on-demand food delivery: Heterogeneity from attribution effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 202(C).
    2. Baozhuang Niu & Fanzhuo Zeng & Zifan Shen & Jimmy Yong Jin, 2026. "In-house purchasing for green design products when the manufacturer’s promised-delivery-time matters," Annals of Operations Research, Springer, vol. 359(1), pages 469-507, April.
    3. Ma, Bohao & Teo, Chee-Chong & Wong, Yiik Diew, 2024. "Location analysis of parcel locker Network: Effects of spatial characteristics on operational performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
    4. Aloini, Davide & Benevento, Elisabetta & Dulmin, Riccardo & Guerrazzi, Emanuele & Mininno, Valeria, 2025. "Unlocking Real-Time Decision-Making in Warehouses: A machine learning-based forecasting and alerting system for cycle time prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).
    5. Niels Agatz & Soo-Haeng Cho & Hao Sun & Hai Wang, 2024. "Transportation-Enabled Services: Concept, Framework, and Research Opportunities," Service Science, INFORMS, vol. 16(1), pages 1-21, March.
    6. Siddharth Arora & James W. Taylor & Ho-Yin Mak, 2023. "Probabilistic Forecasting of Patient Waiting Times in an Emergency Department," Manufacturing & Service Operations Management, INFORMS, vol. 25(4), pages 1489-1508, July.
    7. An, Min Jeong & Jung, Seung Hwan & Lee, Dong Hee, 2025. "Demand forecasting in micro-fulfillment centers using association rule-based machine learning," International Journal of Production Economics, Elsevier, vol. 290(C).
    8. Zhao, Wenhan & Yan, Xiaoming & Yu, Yugang, 2025. "Product price and delivery-time commitment decisions with reference effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 197(C).
    9. Zhang, Yuxin & Huang, Min & Cao, Zhiguang & Wang, Xingwei & Shen, Zhiqi & Zhang, Jie, 2026. "Multi-period fourth-party logistics network design with promised service time and regret behavior," Omega, Elsevier, vol. 138(C).

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