An AI-Driven Optimization and Risk Mitigation Framework to Strengthen U.S. Supply Chain Resilience and National Economic Security

Babul Sarker; Kamana Parvej  Mishu; Mohammad Tahmid  Ahmed; Nadira Kulsum  Papri; Apurbaa  Sarker; Md Yousuf  Ahmad

Authors

Babul Sarker College of Graduate and Professional Studies, Master of Science in Business Analytics (MSBAN), Trine University, 1 University Avenue, Angola, IN-46703, USA
Kamana Parvej Mishu College of Graduate and Professional Studies, Master of Science in engineering management, Trine University, 1 University Avenue, Angola, IN 46703
Mohammad Tahmid Ahmed College of Graduate and Professional Studies, Master of Science in Business Analytics, Trine University, 1 University Avenue, Angola, IN-46703, USA
Nadira Kulsum Papri Graduate Information Technology/Graduate (M.S), Master of Science in Information Technology, University of the Cumberlands, 6178 College Station Drive, Williamsburg, KY 40769, USA
Apurbaa Sarker Graduate Information Technology/Graduate (M.S), Master of Science in Information Technology University name: University of the Cumberlands, 6178 College Station Drive Williamsburg, KY 40769, USA
Md Yousuf Ahmad College of Graduate and Professional Studies, Master of Science in Business Analytics (MSBAN), Trine University, 1 University Avenue, Angola, IN-46703, USA

Keywords:

Supply Chain Resilience, AI Optimization, National Economic Security, Risk Mitigation, Predictive Logistics, Supply Chain Governance

Abstract

The COVID-19 pandemic, geopolitical tensions, and escalating trade conflicts have exposed critical vulnerabilities in U.S. supply chains, threatening national economic security and industrial competitiveness. This paper develops the Strategic AI Supply Chain Optimization and Resilience (SASCOR) framework, an integrated approach that leverages artificial intelligence to enhance supply chain visibility, predictive capacity, and adaptive response mechanisms. Drawing from recent advances in machine learning for demand forecasting, predictive analytics for logistics optimization, and AI-driven risk detection, this study presents a comprehensive governance structure for securing critical supply networks. The framework addresses four critical dimensions: real-time visibility enhancement, predictive risk mitigation, dynamic optimization algorithms, and collaborative ecosystem governance. By synthesizing insights from cross-industry implementations including last-mile delivery optimization, manufacturing ERP integration, and cybersecurity threat detection this research provides actionable strategies for project managers and policymakers to fortify U.S. supply chain infrastructure against systemic disruptions while maintaining competitive advantage in global markets.