Hindawi Publishing Corporation
Journal of Industrial Engineering
Volume 2013, Article ID 827274, 7 pages http://dx.doi.org/10.1155/2013/827274 Research Article
Multiple Criteria ABC Analysis with FCM Clustering
Gulsen Aydin Keskin1 and Coskun Ozkan2
1
2

Department of Industrial Engineering, Faculty of Engineering, Kocaeli University, Umuttepe Campus, 41380 Kocaeli, Turkey
Department of Industrial Engineering, Faculty of Engineering, Yildiz Technical University, 34349 Istanbul, Turkey

Correspondence should be addressed to Gulsen Aydin Keskin; gaydin@kocaeli.edu.tr
Received 13 August 2012; Revised 29 October 2012; Accepted 12 November 2012
Academic Editor: Josefa Mula
Copyright © 2013 G. Aydin Keskin and C. Ozkan. is 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.
e number of stock keeping units (SKUs) possessed by organizations can easily reach quite a few. An inventory management policy for each individual SKU is not economical to design. ABC analysis is one of the conventionally used approaches to classify
SKUs. In the classical method, the SKUs are ranked with respect to the descending order of the annual dollar usage, which is the product of unit price and annual demand. e few of the SKUs that have the highest annual dollar usage are in group A and should be taken into account mostly; the SKUs with the least annual dollar usage are in group C and should be taken into account least; the remaining SKUs are in group B. In this study, we proposed fuzzy c-means (FCM) clustering to a multicriteria ABC analysis problem to help managers to make better decision under fuzzy circumstancse. e obtained results show that the FCM is a quite simple and an easily adaptable method to inventory management.

1. Introduction
Inventory control is a well-known problem in operations research. Several models have been developed to solve inventory problems. In business, companies have hundreds of different types of materials. erefore, it is easy to lose control of managing the materials. Inventory classi�cation using ABC analysis is one of the most widely used techniques in organizations. ABC classi�cation allows an organization to separate stock keeping units (SKUs) into three groups: A, the most important; B, important; and C, the least important.
e purpose of classifying items into groups is to establish appropriate levels of control over each item [1, 2]. e major advantage of ABC analysis is the easiness of use and simplicity to understand. e items are classi�ed according to the annual use value, which is the product of annual demand and the average unit price [3].
e classi�cation of items into A, B, and C groups has generally been implemented according to one criterion. For inventory items, the criterion is frequently the annual dollar usage of the item. However, it has been generally recognized that the traditional ABC analysis has a serious drawback that may inhibit the effectiveness of the procedure in some

situations. Using one criterion only may create problems of signi�cant �nancial loss. For example, class C items with long lead time or class A items prone to obsolescence may incur
�nancial losses due to a possible interruption of production and/or huge inventory levels. erefore, it has been proposed that multicriteria ABC classi�cation, such as lead time, criticality of a stockout of the item, the rate of obsolescence, the scarcity, substitutability, and order size requirement of the item, can provide a more comprehensive managerial control and to take other important criteria into consideration [2, 4,
5].
Complex computational tools are needed for traditional multicriteria ABC classi�cation. One of them is the matrixbased methodology. At this methodology, a joint criteria matrix is developed in the