• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.69-78
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• 2010

Multi-criteria ABC inventory classification is one of the most widely employed techniques for efficient inventory control, and it considers more than one criterion for categorizing inventory items into groups of different importance. Recently, Ramanathan (2006) proposed a weighted linear optimization (WLO) model for the problem of multi-criteria ABC inventory classification. The WLO model generates a set of criteria weights for each item and assigns a normalized score to each item for ABC analysis. Although the WLO model is considered to have many advantages, it has a limitation that many items can share the same optimal efficiency score. This limitation can hinder a precise classification of inventory items. To overcome this deficiency, we propose a context-dependent DEA based method for multi-criteria ABC inventory classification problems. In the proposed model, items are first stratified into several efficiency levels, and then the relative attractiveness of each item is measured with respect to less efficient ones. Based on this attractiveness measure, items can be further discriminated in terms of their importance. By a comparative study between the proposed model and the WLO model, we argue that the proposed model can provide a more reasonable and accurate classification of inventory items.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.4
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• pp.358-366
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• 2011

Multi-criteria ABC inventory classification, which aims to classify inventory items by considering more than one criterion, is one of the most widely employed techniques for inventory control. The weighted linear optimization (WLO) model proposed by Ramanathan (2006) solves the problem of multi-criteria ABC inventory classification by generating a set of criterion weights for each inventory item and assigning a normalized score to the item for ABC analysis. However, the WLO model has some limitations. First, many inventory items can share the same optimal score, which can hinder a precise classification of inventory items. Second, the model allows too much flexibility in weighting multiple criteria; each item is allowed to choose its own weights so that it can maximize its score. As a result, if an item dominates the others in terms of a certain criterion, it may be classified into a higher class regardless of other criteria by assigning an overwhelming weight to the criterion. Consequently, an item with a high value in an unimportant criterion and low values in others may be inappropriately classified as class A, leading to an inaccurate classification of inventory items. To overcome these shortcomings, we extend the WLO model by using the cross-efficiency method in data envelopment analysis. We claim that the proposed model can provide a more reasonable and accurate classification of inventory items by mitigating the adverse effect of flexibility in the choice of weights and yielding a unique ordering of inventory items.