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Abstract

This paper explores the transformative impact of advanced artificial intelligence (AI) techniques on real-time inventory optimization within dynamic supply chains. The introduction highlights the significance of inventory optimization and the limitations of traditional methods, setting the stage for the integration of AI. A comprehensive literature review summarizes existing research on AI applications in supply chain management, identifying key gaps and areas for further exploration. The paper then delves into various AI techniques, including machine learning, deep learning, and reinforcement learning, detailing their application in predicting demand and supply, and the benefits and challenges of implementing these technologies in real-time inventory systems. The analysis of AI-driven inventory optimization reveals significant improvements in supply chain responsiveness, efficiency, and risk management, alongside discussions on scalability and adaptability across different industries and organizational sizes. The conclusion synthesizes key findings and provides recommendations for future research and practical implementation, emphasizing the need for enhanced data quality, model transparency, and ethical considerations. By harnessing AI, businesses can achieve more efficient, responsive, and resilient supply chains, positioning themselves competitively in an increasingly complex global market..
Keywords: Real-Time Inventory Optimization, Dynamic Supply Chains, Artificial Intelligence, Machine Learning, Deep Learning.