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).
Original language | English |
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Pages (from-to) | 4789-4818 |
Number of pages | 30 |
Journal | International Journal of Production Research |
Volume | 53 |
Issue number | 16 |
Early online date | 18 Nov 2014 |
DOIs | |
Publication status | Published - 2015 |
Bibliographical note
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 23/12/14, available online: http://wwww.tandfonline.com/10.1080/00207543.2014.993047Keywords
- case study
- decision support system
- genetic algorithm
- vendor-managed inventory