A SWARA-VAHP and FCM Based Model Proposal for MCIC (extended abstract)

Abstract

ABC analysis is an efficient and easy-to-use methodology to classify inventory based on a single or multi-criteria basis that may consist of thousands of items. The evolution of the subject is now focused on multiple criteria instead of a single criteria. The proposed approach to the multi-criteria inventory classification (MCIC) problem integrates improved version of Stepwise Weight Assessment Ratio Analysis and Voting Analytic Hierarch Process (SWARA-VAHP) and Fuzzy-C-Means. This new approach is called AHP-SWARA-FCM-RVeto and proposed in this study for the first time. The proposed model integrates SWARA-VAHP which is an improved version of AHP. By using SWARA-VAHP, minimum information from experts will be sufficient. Also incorporating FCM will allow users to make clustering based on figures but also gives the flexibility to assign members between different clusters by using membership values. A real life example is used to show the application of the proposed model. In the real-life example, the main advantage of compliance with the Pareto principle of the proposed method is shown.