SMART SUPPLY CHAINS: THE ROLE OF ARTIFICIAL INTELLIGENCE IN INVENTORY OPTIMIZATION

Abstract

Efficient inventory management is a cornerstone of modern supply chain operations, directly impacting operational costs, service levels, and overall business performance. Traditional inventory management methods often struggle to handle complex, dynamic, and large-scale data, leading to stockouts, overstocking, and inefficiencies. This study explores the integration of artificial intelligence (AI) techniques into inventory management systems to enhance decision-making, predictive accuracy, and operational efficiency. By leveraging machine learning algorithms, predictive analytics, and real-time data processing, AIenabled inventory systems can forecast demand, optimize stock levels, and automate replenishment strategies. Experimental evaluations on real-world inventory datasets demonstrate that AI-based solutions significantly reduce wastage, improve order fulfillment rates, and provide actionable insights for proactive decision-making. The findings highlight the transformative potential of AI in creating smart, resilient, and adaptive inventory management systems that can meet the evolving demands of modern supply chains.