Multi-criteria ABC inventory classification using DEA-discriminant analysis to predict group membership of new items

Abstract

Inventory management plays a significant role in organisations' success or failure. ABC inventory classification is one of the most popular methods which are regularly applied in inventory management. Correct clustering of inventory items is an important issue of inventory management. The 'annual cost' is an important factor in most of previous studies which applied ABC inventory classification. Each item which has higher annual cost is placed in class A. This paper shows that other factors have significant role for classifying inventory items. We use data envelopment analysis (DEA) to classify inventory items into three groups as A, B, or C in the presence of weight restrictions. Weight restrictions allow for the integration of managerial preferences in terms of relative importance of various factors. Then, to predict group membership of new items, the DEA is incorporated with discriminant analysis (DA). To demonstrate applicability of proposed approach a case study is presented. © 2014 Inderscience Enterprises Ltd.