An improved method for predicting and ranking suppliers efficiency using data envelopment analysis

Abstract

Supplier selection problem (SSP) is a problem to select the best among suppliers based on input and output data of the suppliers. Since different uncontrollable and unpredictable parameters are affecting selection, choosing the best supplier is a complicated process. Data Envelopment Analysis (DEA) is a method for measuring efficiency and inefficiencies of Decision Making Units (DMUs). DEA has been employed by many researchers for supplier selection and widely used in SSP with inputs for supplier evaluation. However, the DEA still has some disadvantages when it is solely used for SSP. Hence, in this paper, a combination of DEA and Neural Network (NN), DEA-NN, is proposed for SSP. We also develop a model for SSP based on Support Vector Regression (SVR) to improve the stability of DEA-NN. The proposed method was evaluated using small and large data sets. The experimental results showed that, the proposed method solve the problems connected to the previous methods. The results also showed that stability of proposed method is significantly better than DEA-NN method. In addition, CCR-SVR model overcome shortcomings such as instability and improves computational time and accuracy for predicting efficiency of new small and large DMUs.