Distance-based consensus method for ABC analysis

A distance-based multi-criteria consensus framework on the concepts of ideal and negative-ideal solutions is presented for the ABC analysis of inventory items. This article demonstrates a way of classifying inventory items using the TOPSIS ('Technique for Order Preference by Similarity to Ideal Solution') model. The methodology has been applied in a pharmaceutical company located in the heart of Kolkata, India. The technique takes into account various conflicting criteria having incommensurable units of measurement. Unit cost, lead time, consumption rate, perishability of items and cost of storing of raw materials have been considered for the case study. By using TOPSIS, the items are ranked in categories A, B and C. The suitability, practicability and effectiveness of the TOPSIS method used in ABC classification have been judged using the analysis of variance (ANOVA) technique. A simulation model has been used to compare the proposed model with that of the traditional ABC classification technique.