Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS)

Abstract

An effective way for managing and controlling a large number of inventory items or stock keeping units (SKUs) is the inventory classification. Traditional ABC analysis which based on only a single criterion is commonly used for classification of SKUs. However, we should consider inventory classification as a multi-criteria problem in practice. In this study, a new method of Evaluation based on Distance from Average Solution (EDAS) is introduced for multi-criteria inventory classification (MCIC) problems. In the proposed method, we use positive and negative distances from the average solution for appraising alternatives (SKUs). To represent performance of the proposed method in MCIC problems, we use a common example with 47 SKUs. Comparing the results of the proposed method with some existing methods shows the good performance of it in ABC classification. The proposed method can also be used for multi-criteria decision-making (MCDM) problems. A comparative analysis is also made for showing the validity and stability of the proposed method in MCDM problems. We compare the proposed method with VIKOR, TOPSIS, SAW and COPRAS methods using an example. Seven sets of criteria weights and Spearman’s correlation coefficient are used for this analysis. The results show that the proposed method is stable in different weights and well consistent with the other methods.