Enhancing Supply Chain Sustainability Using AI for Carbon Footprint Analysis and Optimization.

Abstract

The global push for sustainable operations has compelled organizations to re-evaluate their supply chains to reduce environmental impacts. Among the most pressing challenges is the accurate measurement and reduction of carbon footprints across complex, multi-tiered supply networks. Artificial Intelligence (AI) has emerged as a transformative tool in enabling organizations to enhance supply chain sustainability through precise carbon footprint analysis and optimization. This paper explores how AI-driven technologies such as machine learning, deep learning, natural language processing, and predictive analytics can be integrated into supply chain management systems to identify emission hotspots, simulate low-carbon alternatives, and implement optimization strategies. We review the role of AI in gathering and interpreting emissions data from diverse sources, providing real-time visibility into Scope 1, 2, and 3 emissions. Case studies are presented to illustrate how leading firms are leveraging AI to reduce emissions, optimize routes, and transition to sustainable logistics models. The paper also discusses the challenges of data availability, ethical considerations, and technological infrastructure, offering recommendations for businesses and policymakers. Through this exploration, the research underscores the potential of AI not only in reducing environmental impact but also in aligning with global sustainability frameworks such as the UN Sustainable Development Goals (SDGs) and Science Based Targets initiative (SBTi). The study concludes with future directions, emphasizing the need for interdisciplinary collaboration and regulatory support to fully harness AI's potential for a greener global supply chain.