On the inventory performance of multi-criteria classification methods: empirical investigation

M.Z. Babai, T. Ladhari and I. Lajili

International Journal of Production Research, 2015, vol. 53, issue 1, 279-290

Abstract: A number of multi-criteria inventory classification (MCIC) methods have been proposed in the academic literature. However, most of this literature focuses on the development and the comparison of ranking methods of stock keeping units (SKUs) in an inventory system without any interest in the original and most important goal of this exercise which is the combined service-cost inventory performance. Moreover, to the best of our knowledge these MCIC methods have never been compared in an empirical study. Such an investigation constitutes the objective of this paper. We first present the inventory performance evaluation method that we illustrate based on an example commonly used in the relevant literature which consists of 47 SKUs. Then, we present the empirical investigation that is conducted by means of a large data-set consisting of more than 9086 SKUs and coming from a retailer in the Netherlands that sells do-it-yourself products. The results of the empirical investigation show that the MCIC methods that impose a descending ranking of the criteria, with a dominance of the annual dollar usage and the unit cost criteria, have the lowest combined cost-service performance efficiency.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2014.952791 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:53:y:2015:i:1:p:279-290

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2014.952791

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().