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Decision support system for vendor managed inventory supply chain: a case study

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  • Atul B. Borade
  • Edward Sweeney

Abstract

Vendor-managed inventory (VMI) is a widely used collaborative inventory management policy in which manufacturers manages the inventory of retailers and takes responsibility for making decisions related to the timing and extent of inventory replenishment. VMI partnerships help organisations to reduce demand variability, inventory holding and distribution costs. This study provides empirical evidence that significant economic benefits can be achieved with the use of a genetic algorithm (GA)-based decision support system (DSS) in a VMI supply chain. A two-stage serial supply chain in which retailers and their supplier are operating VMI in an uncertain demand environment is studied. Performance was measured in terms of cost, profit, stockouts and service levels. The results generated from GA-based model were compared to traditional alternatives. The study found that the GA-based approach outperformed traditional methods and its use can be economically justified in small- and medium-sized enterprises (SMEs).

Suggested Citation

  • Atul B. Borade & Edward Sweeney, 2015. "Decision support system for vendor managed inventory supply chain: a case study," International Journal of Production Research, Taylor & Francis Journals, vol. 53(16), pages 4789-4818, August.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:16:p:4789-4818
    DOI: 10.1080/00207543.2014.993047
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    Cited by:

    1. Ghasemi, Elaheh & Lehoux, Nadia & Rönnqvist, Mikael, 2024. "A multi-level production-inventory-distribution system under mixed make to stock, make to order, and vendor managed inventory strategies: An application in the pulp and paper industry," International Journal of Production Economics, Elsevier, vol. 271(C).
    2. Weißhuhn, Sandria & Hoberg, Kai, 2021. "Designing smart replenishment systems: Internet-of-Things technology for vendor-managed inventory at end consumers," European Journal of Operational Research, Elsevier, vol. 295(3), pages 949-964.
    3. Dellino, G. & Laudadio, T. & Mari, R. & Mastronardi, N. & Meloni, C., 2018. "Microforecasting methods for fresh food supply chain management: A computational study," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 147(C), pages 100-120.

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