JOURNAL ARTICLE

SF Express Revolutionizes Its Operations Planning Strategy Using Operations Research.

  • Published In: INFORMS Journal on Applied Analytics, 2026, v. 56, n. 1. P. 76 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Huang, Yixiao; Liu, Ziheng; Chen, Huan; Geng, Yankun; Gao, Fei; Liao, Yiwen; Lin, Jiaxin; Zhang, Kai; Zhu, Guangyuan; Wang, Zhenmeng; Chen, Qichang; Lv, Xiangtan; Shi, Junhao; Pi, Yaomei; Jia, Shengyang; Zhang, Bingying; Lin, Mengting; Tang, Shuxian; Feng, Song; Xiang, Hao 3 of 3

Abstract

This article focuses on SF Express’s transformation of its logistics network planning from a manual, regional process to a centralized, operations research (OR)-based system starting in 2018. SF Express, China’s leading integrated logistics provider, developed advanced mathematical models and algorithms to optimize its intercity air and ground networks as well as its intracity feeder and same-day delivery networks, addressing large-scale, complex constraints such as service-level agreements, fleet capacities, and hub operations. The centralized OR-driven approach has generated over $1 billion in cost savings, reduced delivery times for more than one billion parcels, and cut millions of tons of carbon dioxide equivalent emissions since implementation. The project also fostered organizational innovation by integrating OR experts with network planners, improved decision-making efficiency, and contributed to academic research and industry standards, with potential applicability to other logistics providers globally.

Additional Information

  • Source:INFORMS Journal on Applied Analytics. 2026/01, Vol. 56, Issue 1, p76
  • Document Type:Case Study
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2026
  • ISSN:2644-0865
  • DOI:10.1287/inte.2025.0281
  • Accession Number:191204595
  • Copyright Statement:Copyright of INFORMS Journal on Applied Analytics is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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