AI-DRIVEN DEMAND FORECASTING: REVOLUTIONIZING MODERN INVENTORY MANAGEMENT - Chaitanya Teja Musuluri, (INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY (IJCET), 2024)
Integrating artificial intelligence and machine learning technologies has fundamentally transformed traditional supply chain management paradigms. This comprehensive article explores the revolutionary impact of AI-driven systems across various aspects of supply chain operations, including inventory management, demand forecasting, and operational optimization. The article examines the technical foundations of these systems, highlighting the convergence of multiple machine-learning models and sophisticated data processing capabilities. The implementation architecture details the evolution of real-time processing pipelines and cyber-physical systems integration, while optimization algorithms demonstrate advanced mathematical modeling enhanced by blockchain and IoT technologies. Business impact metrics reveal significant improvements in operational efficiency, resource utilization, and financial performance across different sectors. The article also addresses critical integration considerations and explores emerging capabilities, providing insights into future developments in quantum computing applications and autonomous systems for supply chain management.