Sarker, Babul and Mishu, Kamana Parvej and Ahmed, Mohammad Tahmid and Papri, Nadira Kulsum and Sarker, Apurbaa and Ahmad, Md Yousuf (2023) An AI-Driven Optimization and Risk Mitigation Framework to Strengthen U.S. Supply Chain Resilience and National Economic Security. American Journal of Engineering, Mechanics and Architecture, 1 (10). pp. 418-427. ISSN 2993-2637

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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.

Item Type:	Article
Subjects:	A General Works > AI Indexes (General)
Depositing User:	admin eprints
Date Deposited:	07 May 2026 10:27
Last Modified:	07 May 2026 10:27
URI:	http://eprints.umsida.ac.id/id/eprint/16468

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