Alamri, Adel A., Harris, Irina ORCID: https://orcid.org/0000-0003-0622-5123 and Syntetos, Aris A. ORCID: https://orcid.org/0000-0003-4639-0756 2016. Efficient inventory control for imperfect quality items. European Journal of Operational Research 254 (1) , pp. 92-104. 10.1016/j.ejor.2016.03.058 |
Preview |
PDF
- Accepted Post-Print Version
Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (894kB) | Preview |
Abstract
In this paper, we present a general EOQ model for items that are subject to inspection for imperfect quality. Each lot that is delivered to the sorting facility undertakes a 100 per cent screening and the percentage of defective items per lot reduces according to a learning curve. The generality of the model is viewed as important both from an academic and practitioner perspective. The mathematical formulation considers arbitrary functions of time that allow the decision maker to assess the consequences of a diverse range of strategies by employing a single inventory model. A rigorous methodology is utilised to show that the solution is a unique and global optimal and a general step-by-step solution procedure is presented for continuous intra-cycle periodic review applications. The value of the temperature history and flow time through the supply chain is also used to determine an efficient policy. Furthermore, coordination mechanisms that may affect the supplier and the retailer are explored to improve inventory control at both echelons. The paper provides illustrative examples that demonstrate the application of the theoretical model in different settings and lead to the generation of interesting managerial insights.
Item Type: | Article |
---|---|
Date Type: | Publication |
Status: | Published |
Schools: | Business (Including Economics) |
Subjects: | H Social Sciences > HD Industries. Land use. Labor |
Publisher: | Elsevier |
ISSN: | 0377-2217 |
Date of First Compliant Deposit: | 5 April 2016 |
Date of Acceptance: | 30 March 2016 |
Last Modified: | 10 Nov 2024 17:45 |
URI: | https://orca.cardiff.ac.uk/id/eprint/88842 |
Citation Data
Cited 51 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
Edit Item |