علیرضا فلاح پور - Department of Management, Farvardin Institute of Higher Education, Qaemshahr, Mazandaran, Iran
نیما کاظمی - Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
محمد مولانی - Innovation and Management Research Center, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran
سینا نیری - Innovation and Management Research Center, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran
مجتبی احسانی - Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran

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.

Supplier Selection, Support vector Machine, Data Envelopment Analysis, supplier efficiency, Artificial Intelligence