Online platforms’ warehouse capacity allocation strategies for multiple products

Online platforms typically store multiple products in their warehouses, which often run out of space. Hence the authors are motivated to study how platforms can effectively allocate their limited warehouse capacity among different products. They establish a game-theoretic model to investigate the optimal warehouse capacity allocation strategy of a platform that serves the dual role of a marketplace and a reseller. Their research reveals that, in the single-product case, a platform’s profit increases – first convexly and then concavely – with warehouse capacity. Second, they study the optimal warehouse capacity allocation strategy of a platform in the case of multiple products with different characteristics. They establish that the capacity allocated to a product increases with the commission rate and with the platform’s unit procurement cost, and also as the retailer’s unit procurement cost decreases. However, the capacity allocation for products characterized by high fixed warehouse operating costs or high unit product volumes depends to a large extent on the platform’s total warehouse capacity. The numerical study verifies their theoretical results and generates more management insights. The authors complete their study with two extensions. The first shows that platforms without warehouses cannot use the same warehouse operation strategy as those that do have warehouses unless (a) the warehouse investment cost coefficient is relatively low and (b) the platform intends to manage all products in its newly built warehouse. Their second extension demonstrates that a platform is generally more willing to store and sell high-quality (than low-quality) products.

• Record URL:
  • https://doi.org/10.1016/j.tre.2023.103170
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1366554523001588
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13665545
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Hu, Shu
  • 0000-0002-4737-4291
  • Yu, Dennis Z
  • Fu, Ke
• Publication Date: 2023-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 103170
• Serial:
  • Transportation Research Part E: Logistics and Transportation Review
  • Volume: 175
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1366-5545
  • Serial URL: http://www.sciencedirect.com/science/journal/13665545

Subject/Index Terms

• TRT Terms: Electronic commerce; Logistics; Resource allocation; Supply chain management; Warehousing
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01886582
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 28 2023 4:57PM