THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN AND LOGISTICS: ENHANCING PREDICTIVE FORECASTING, ROUTE OPTIMIZATION, AND REAL-TIME DEMAND MANAGEMENT

This study investigates the transformative role of artificial intelligence (AI) in supply chain management (SCM) and logistics, focusing on predictive forecasting, route optimization, and real-time demand management. Employing a mixed-methods approach including a systematic literature review of 45 empirical studies and empirical analysis using real-world datasets from Kaggle's DataCo Smart Supply Chain and Smart Logistics Supply Chain the research demonstrates AI's capacity to reduce forecasting errors by up to 47%, optimize routes for 25-35% cost savings, and enable real-time demand adjustments with 92% accuracy. Key findings reveal that machine learning models like LSTM outperform traditional methods, while reinforcement learning excels in dynamic routing. The study bridges a critical research gap by integrating these three domains, offering actionable insights for practitioners. Implications include enhanced resilience, sustainability, and competitiveness in volatile markets. Limitations such as data quality and computational demands are discussed, with recommendations for future hybrid AI-blockchain frameworks. This work contributes to SCM theory by extending dynamic capabilities theory and provides a blueprint for AI adoption in logistics.