An Intelligence-Based Model for Supplier Selection Integrating Data Envelopment Analysis and Support Vector Machine

Abstract

The importance of supplier selection is nowadays highlighted more than ever as companies have realized that efficient supplier selection can significantly improve the performance of their supply chain. In this paper, an integrated model that applies Data Envelopment Analysis (DEA) and Support Vector Machine (SVM) is developed to select efficient suppliers based on their predicted efficiency scores. In the first step, fuzzy linguistic variables are changed to crisp data as initial dataset for DEA. Actual efficiency scores are then calculated for each Decision Making Unit (DMU) using CCR-DEA model. Afterwards, suppliers' performance-related data are used for training SVM-DEA model. A numerical example representing an actual case is provided to indicate the applicability of the model.