Classifying ABC Inventory with Multicriteria Using a Data Envelopment Analysis Approach

Abstract

This paper presents a modified data envelopment analysis (DEA) model to address ABC inventory classification. The new model is derived from reduced DEA model, and some restricts are added to make classification results more reasonable. The evaluating process has two steps. Firstly, all criteria data for each item are normalized between [0, 1]. Then, the prior scores for all inventory items are computed using the proposed model. A simulation example is employed to verify the efficiency of the model. The results show that this model is an effective ABC classification tool and the prior score of each item make classification credible. Furthermore, the classification result is compared with that of traditional analytic hierarchy process (AHP) methodology