AI-Driven Supply Chain Resilience at National Scale: Deep Learning for Demand Forecasting, Inventory Optimization, and Industrial Cost Minimization

Abstract

The increasing complexity of national supply chains, driven by global disruptions, pandemics, geopolitical uncertainties, and rapidly changing demand patterns, has created significant challenges for maintaining operational stability and resilience. Traditional forecasting methods and inventory management approaches often lack the predictive capability, adaptability, and real-time responsiveness required for large-scale supply chain systems. This research proposes an AI-driven national supply chain resilience framework that integrates deep learning-based demand forecasting, predictive analytics, intelligent inventory optimization, and cost minimization strategies to improve supply chain performance. The proposed approach utilizes advanced deep learning models for accurate demand prediction, reinforcement learning algorithms for adaptive inventory control, and data-driven analytics for proactive disruption management. A resilience evaluation framework is incorporated to assess improvements in forecasting accuracy, inventory efficiency, operational cost reduction, and recovery capability during supply chain disruptions. The study demonstrates how artificial intelligence can transform conventional supply chain operations into adaptive, predictive, and self-optimizing systems capable of supporting industrial decision-making at national scale. The proposed framework contributes to reducing demand uncertainty, minimizing excess inventory and operational expenses, improving resource allocation, and strengthening supply chain recovery mechanisms. This research highlights the strategic role of AI technologies in developing resilient Industry 4.0 supply ecosystems capable of responding effectively to future disruptions.