Optimizing Carbon Capture Supply Chains with AI-Driven Supplier Quality Management and Predictive Analytics

Abstract

As the need for sustainable practices grows, carbon capture and storage (CCS) systems have become critical in mitigating environmental impact by reducing carbon emissions. This study explores the role of artificial intelligence (AI) in enhancing the CCS supply chain, with a specific focus on supplier quality management and predictive analytics. By integrating AI technologies, companies can optimize their supply chains, minimize operational costs, and improve supplier quality performance. Supply chain managers can better forecast disruptions, identify potential risks, and enhance decision-making using predictive analytics. This paper synthesizes recent research on AI applications in CCS, assessing its impact on supplier quality management and operational efficiency. Key findings indicate that AI-driven supplier management systems significantly enhance carbon capture efficiency, reducing overall emissions and facilitating more streamlined CCS operations.