Sustainable Supply Chain Management: Assessing Environmental Impact Using Data Analytics

Abstract

Organizations have begun to use data-driven decision-making techniques in response to the rising significance of sustainability in supply chain management. To evaluate and improve the environmental impact of supply chains, this study examines the use of data analytics, in particular the use of sophisticated regression models. This study looks at how various supply chain processes including transportation, procurement, and production affect the environment and examines the sustainability measures. This study uses novel regression methods, termed as multivariate adaptive regression splines (MARS) as well as generalized additive models (GAMs), to show the non-linear relationship among the environmental factors and supply chain choices. The results show that these models help with sustainability performance predictions and that they can aid businesses in making eco-friendly supply chain choices. By combining sustainability objectives with data analytics to enhance supply chain environmental performance, this study contributes to the expanding body of knowledge.