Developing Data Envelopment Analysis Model for Performance Evaluation of Green Supply Chain Management

In recent times, there has been a growing concern regarding environmental issues. This has resulted in increased pressure on companies and producers from government regulations, while also striving to maintain customer satisfaction by addressing environmental concerns. Green Supply Chain Management (GSCM) has emerged as a means to enhance efficiency and reduce environmental impact for firms collaborating with clients and suppliers. GSCM encompasses various aspects such as green purchasing, design, manufacturing, distribution, packaging, marketing, and reverse logistics within supply chains, to improve environmental performance. The use of nonparametric models, specifically Data Envelopment Analysis (DEA), has been prevalent in assessing the efficiency and proficiency of supply chains as decisionmaking units (DMUs). However, the earliest research on efficiency fulfilment in GSCM has not thoroughly explored the combined effect of economic and environmental factors, such as service level, CO2 emissions, and supply chain size (arcs), on the overall efficiency of the supply system. These principles are crucial as they can impact a manager's capability to accurately evaluate the performance of a green supply chain. Therefore, it is imperative to evaluate GSCM efficiency using DEA models while incorporating green principles to identify efficient DMUs and potential DMUs that can be improved with less cost and effort. This study aims to address this research gap by developing a benchmark approach to identify efficient DMUs and potentially efficient DMUs, which can be enhanced through minor adjustments. The study utilizes DEA standard models to determine benchmarks and potentially efficient DMUs and modifies their inputs to achieve an efficient status. Additionally, the impact of green elements on the efficiency of DMUs is assessed using Tobit regression analysis pre and post adjustment. Pragmatic outcomes obtained from the case study demonstrate the practicality of the proposed procedure in prioritizing potential DMUs for modification. Keywords: Green supply chain management, Performance evaluation, Efficiency, Benchmarking, Data envelopment analysis, Tobit regression