A model for evaluating sustainable supply chain performance using network data envelopment analysis and bootstrap simulation

The field of data envelopment analysis in the realm of data science and analytics focuses on examining and analyzing various aspects of data within a dataset. The main objective of this analysis is to investigate and analyze different and diverse dimensions of the data. Data envelopment analysis is employed to gain a better understanding of the data, enhance transparency, and achieve a deeper comprehension of patterns, relationships, and characteristics within the data. The primary goal of data envelopment analysis is to discover hidden and unknown information within the data and extract knowledge from it. By utilizing data envelopment analysis methods, one can observe and comprehend patterns, relationships, differences, and variations in the data, leading to the provision of new and potentially valuable insights regarding the data and its associated processes. Therefore, in this article, the bootstrap simulation process has been utilized to improve the accuracy of achieved performance. The main objective of this article is to present a framework for measuring the performance of evaluated units under the bootstrap scenario. To accomplish this, overall and stage-wise performance scores are separately determined for each unit through simulated values for inputs and outputs. Thus, the most significant innovation of this research is the presentation of a framework for determining the performance scores of interconnected units using bootstrap-data envelopment analysis. To demonstrate the proposed method, a real case study in the tomato supply chain network has been considered. According to the results, the accuracy of the employed method has been demonstrated.