ROUGH MULTI- PERIOD NETWORK DATA ENVELOPMENT ANALYSIS FOR EVALUATION OF SUPPLY CHAIN: A CASE STUDY OF SKILL TRAINING IN IRAN

The existence of a comprehensive and complete model, along with accurate and reliable data, helps to evaluate the performance of the supply chain. Given the different layers and various performances in designing the supply chain, a method that can analyze and evaluate such network structure is required. Moreover, data and conditions’ uncertainty highlight the need for a method that can also include uncertainty in evaluation. In this paper, designing a multi-period network is carried out with rough data to embed in various layers and levels of supply chain. The supply chain performance evaluation is performed using rough network data envelopment analysis. Rough Network Data Envelopment Analysis (RNDEA) is a proper method since it analyzes all the current factors in evaluation; besides, it provides efficiency scores for inefficient decision-making units and boundary forecasting for these units on an efficient border. The study’s outcomes reveal the efficiency of different factors in the designed network. On the other hand, unlike common data envelopment analysis that indicates the maximum of a factor efficiency, the efficiency priority is calculated in the proposed rough network model, and divisional efficiency also is determined in each step.