Predictive Supply Chain Analytics: MIS-Integrated AI Models for U.S. Manufacturing Resilience

The U.S. manufacturing sector faces unprecedented challenges in sustaining operational resilience amidst global disruptions, demand fluctuations, and logistical uncertainties. Traditional supply chain management techniques often fail to provide real-time adaptability, resulting in inefficiencies and revenue losses. This paper proposes an AI-driven predictive analytics framework integrated with Management Information Systems (MIS) to enhance decision-making and improve supply chain resilience. Using machine learning (ML) and advanced statistical modeling, the proposed approach enables real-time demand forecasting, risk assessment, and inventory optimization. The research highlights experimental results derived from U.S.-based manufacturing datasets, demonstrating a 35% improvement in demand prediction accuracy and a 22% reduction in operational delays. The findings establish that integrating MIS with AI-powered predictive models significantly enhances supply chain visibility, agility, and overall manufacturing resilience.