DEVELOPMENT OF AI-DRIVEN PREDICTIVE ANALYTICS SYSTEMS TO IMPROVE SUPPLY CHAIN RESILIENCE AND STRENGTHEN THE STABILITY OF CRITICAL U.S. INDUSTRIES

• Priyanka Singh

  Department of MSIT, WestCliff University, California, USA

• Mulayam Singh Patel

  Department of MSIT, WestCliff University, California, USA

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.

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