JOURNAL ARTICLE

RiRiShun Logistics: Home Appliance Delivery Data for the 2021 Manufacturing & Service Operations Management Data-Driven Research Challenge.

  • Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2024, v. 26, n. 4. P. 1358 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Guo, Xiaolong; Yu, Yugang; Allon, Gad; Wang, Meiyan; Zhang, Zhentai 3 of 3

Abstract

This article focuses on the detailed logistics operational-level data provided by RiRiShun Logistics (RRS), a subsidiary of Haier Group specializing in home appliance delivery and installation in China, to support the 2021 Manufacturing & Service Operations Management (M&SOM) Data-Driven Research Challenge. The data set encompasses over 14 million orders from 149 clients (including retailers, logistics operators, and manufacturers) involving 18,000 stock keeping units (SKUs) processed through 103 distribution centers and covering 4.2 million end consumers. It captures the full delivery cycle—from order issuance to delivery completion or installation—and includes detailed information on order characteristics, SKU dimensions, appointment scheduling, delivery operations, consumer demographics, and the logistics network structure. The article also outlines potential research directions related to contract design, transshipment policies, inventory management, delivery performance, and distribution network optimization within this large-scale home appliance logistics ecosystem.

Additional Information

  • Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2024/07, Vol. 26, Issue 4, p1358
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:1523-4614
  • DOI:10.1287/msom.2021.0994
  • Accession Number:178447824
  • Copyright Statement:Copyright of Manufacturing & Service Operations Management (M&SOM) (INFORMS) 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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