Developing a Data Envelopment Analysis Model for a Smart Sustainable Multi-Echelon Supply Chain Using Natural, Managerial, and Free Disposal Hulls: An Approach using Bootstrap Simulation

Data envelopment analysis (DEA) is a smart method used to assess the efficiency of decision-making units (DMUs) engaged in similar activities, without relying on specific assumptions. Despite providing a DEA smart applicable in real-world scenarios, the method’s accuracy may be affected by uncertainties surrounding population distribution. To address this concern, the study introduces two smart network DEA model combined with bootstrap simulation to evaluate sustainable supply chain (SC) performance. Therefore, important contribution of the present research is to provide a data envelopment analysis model to calculate the efficiency of a sustainable supply chain based on convex and non-convex principles. The research evaluates data from nine tomato paste companies using a three-stage DEA approach and the bootstrap method, considering natural, managerial, and free disposability principles. By applying the proposed model to an Iranian tomato paste SC network, the study identifies that only two out of the nine companies are efficient, while the others are deemed inefficient. Through bootstrap simulation, the model’s accuracy improves by adjusting parameters and reducing data noise. Additionally, a non-convex model based on free disposability principles is presented to further establish the effectiveness of the proposed approach. Sensitivity analysis on inefficient DMUs is conducted to determine the extent of inefficiency for each unit.