Assessing Sustainability of Supply Chain Performance using Machine Learning and Network Data Envelopment Analysis

Using network data envelopment analysis (NDEA) models to assess the efficiency of Decision Making Units (DMUs) is a widely accepted method in academic research. An ongoing challenge in this field involves the computation and implementation of enhancement solutions within homogeneous clusters utilizing Machine Learning techniques. The primary aim of this paper is to identify the optimal clustering algorithm for a two-stage sustainable supply chain within the petrochemical industry in Iran. Subsequently, the application of NDEA within each cluster aims to ascertain efficiency levels and devise improvement strategies to facilitate a more targeted development approach for inefficient units. This paper investigates the best clustering algorithms in the area of Machine Learning by using quality measurement indicators and using Network Data Envelopment Analysis (NDEA) for measuring the efficiency of DMUs with sustainability approach. Upon examination, it has been determined that the Deep Embedded Clustering algorithm yields the most favorable results when applied to the data set. Furthermore, the comparison of the clustering result with the standard NDEA model has demonstrated the utility of clustering and comparing units in homogeneous categories for the purpose of efficiency calculation and determining the distance to the efficient frontier. This article, showed that how to find the best algorithm for two-stage supply chain clustering. Also, by comparing the effect of clustering on measuring the distance of inefficient units to the efficiency frontier, it was shown that clustering of units can play a significant role in planning to reach a practical development plan in each cluster.