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Advances in Decision Sciences
Volume 2015 (2015), Article ID 645746, 11 pages
http://dx.doi.org/10.1155/2015/645746
Research Article

A Common Weight Linear Optimization Approach for Multicriteria ABC Inventory Classification

1Faculty of Engineering, Shahrekord University, Rahbar Boulevard, P.O. Box 115, Shahrekord 34141-88186, Iran
2School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 14174, Iran

Received 6 May 2014; Revised 22 October 2014; Accepted 11 December 2014

Academic Editor: Roger Z. Ríos-Mercado

Copyright © 2015 S. M. Hatefi and S. A. Torabi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Organizations typically employ the ABC inventory classification technique to have an efficient control on a huge amount of inventory items. The ABC inventory classification problem is classification of a large amount of items into three groups: A, very important; B, moderately important; and C, relatively unimportant. The traditional ABC classification only accounts for one criterion, namely, the annual dollar usage of the items. But, there are other important criteria in real world which strongly affect the ABC classification. This paper proposes a novel methodology based on a common weight linear optimization model to solve the multiple criteria inventory classification problem. The proposed methodology enables the classification of inventory items via a set of common weights which is very essential in a fair classification. It has a remarkable computational saving when compared with the existing approaches and at the same time it needs no subjective information. Furthermore, it is easy enough to apply for managers. The proposed model is applied on an illustrative example and a case study taken from the literature. Both numerical results and qualitative comparisons with the existing methods reveal several merits of the proposed approach for ABC analysis.