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All Journal International Journal of Business, Law and Political Science
Patel, Mulayam Singh
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Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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DEVELOPMENT OF AI-DRIVEN PREDICTIVE ANALYTICS SYSTEMS TO IMPROVE SUPPLY CHAIN RESILIENCE AND STRENGTHEN THE STABILITY OF CRITICAL U.S. INDUSTRIES Singh, Priyanka; Patel, Mulayam Singh
International Journal of Business, Law and Political Science Vol. 2 No. 12 (2025): International Journal of Business, Law and Political Science
Publisher : PT. Antis International Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ijblps.v2i12.461

Abstract

Objective: This paper presents a comprehensive framework for developing AI-driven predictive analytics systems designed to enhance supply chain resilience in critical U.S. industries. Method: The proposed methodology integrates machine learning algorithms with real-time data processing capabilities to forecast disruptions, optimize inventory management, and strengthen supply chain stability. Results: Our experimental results demonstrate significant improvements in demand forecasting accuracy (up to 23%) and reduction in supply chain disruption response time by 35%. Novelty: The findings contribute to the growing body of knowledge on intelligent supply chain management and provide practical insights for industry practitioners seeking to leverage AI technologies for operational excellence.
Co-Authors Singh, Priyanka