A network data envelopment analysis to evaluate the performance of a sustainable supply chain using bootstrap simulation

Abstract

Data envelopment analysis (DEA) is a non-parametric method to calculate the efficiency of decision-making units (DMU) under evaluation that perform the same activity. The frontier obtained by this method is a relative frontier accessible in the real world. Due to the uncertainty of the population distribution, the accuracy of the achieved efficiency is questioned. Therefore, this research aims to present a network data envelopment analysis model to evaluate the performance of a sustainable supply chain using bootstrap simulation. In this research, using the two-step approach of data envelopment analysis and the bootstrap method, the information collected from 25 tomato paste companies for the year 2021 has been analyzed. To illustrate the proposed method, a real case study is considered in the Iranian tomato paste supply chain network. The findings showed that using definitive data, 16 companies are efficient and 9 companies are inefficient, and using bootstrap simulation data, 4 companies are efficient and 21 companies are inefficient. Using the proposed framework, the overall efficiency value has been calculated in two cases using DEA and the bootstrap model. In addition, the efficiency of the stage is calculated separately. Based on the calculated results, if a DMU is considered efficient, its efficiency score is equal to 1 in each of the stages. Otherwise, the cause of the inefficiency of each DMU is identified. Also, based on the comparisons made between the proposed model and the basic models based on sensitivity analysis, the accuracy of the proposed bootstrap-based model in introducing the number of efficient units has been better than the basic models. Therefore, the accuracy of the used method can be concluded.