Support Vector Machines for Multi-Attribute ABC Analysis

Abstract

This paper examined the classification performance of Support Vector Machines (SVMs) on multi-criteria inventory analysis. The ABC analysis using the Simple Additive Weighting (SAW) method was employed to determine inventory classes of items held in inventory of a large scale automobile company operating in Turkey. The provided data set was analyzed with SVMs to obtain classification performance of the SVM learning algorithm. The results showed that SVM is highly applicable to the inventory classification problem.