Evaluating performance supply chain by a new non-radial network DEA model with fuzzy data

Abstract

Data Envelopment Analysis (DEA) is a non-parametric technique for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. Evaluating performance supply chain is one of the uses of DEA. But hence, the traditional DEA models treat with each DMU as a “black box”, thus, the performance measurement may be not effective. So, there are necessities for network DEA models. The primary condition for the use of DEA models is that the data are exact. But in the real word, we are often conformed to vague and uncertain data and performance evaluation by usual methods in the presence such data may lead errors in decision-making process, so for making applied decision and more adaptive to real word, it is undeniable need for fuzzy logic to evaluate the efficiency of unit. In this paper, at first, a new non-radial network DEA model for evaluating performance supply chain is introduced, by considering intermediate production. Its optimal solution can separate inefficient and strong efficient DMUs, and finally we solve this model when the all data are fuzzy numbers.