AI-Powered Risk Management Frameworks for Ensuring Supplier Quality in Carbon Capture and Energy Storage Supply Chains
Keywords:
AI-powered risk management, supplier quality, carbon capture, energy storage, machine learning, supply chain resilience, predictive analytics, supply chain risk, sustainability, operational improvementsAbstract
In response to the escalating demand for carbon capture and storage (CCS) and energy storage solutions, maintaining supplier quality within complex, globally dispersed supply chains has become crucial. This paper explores the potential of AI-powered risk management frameworks in assessing, monitoring, and mitigating supplier risks specific to CCS and energy storage supply chains. By employing advanced machine learning (ML) models, real-time data processing, and predictive analytics, AI-driven frameworks offer a proactive approach to ensuring high supplier quality standards. This paper synthesizes recent literature on AI in supply chain risk management, identifying primary risk categories, evaluating AI applications in supplier quality assessment, and discussing best practices for implementing these frameworks in CCS and energy storage contexts. Key findings suggest that AI frameworks reduce supplier-related risks, improve compliance with regulatory requirements, and enhance supply chain resilience. Data tables and figures are presented to illustrate AI model accuracy, operational improvements, and cost-effectiveness. This research contributes to both theoretical and practical discussions on enhancing sustainability and reliability in CCS and energy storage supply chains.
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