This study investigates the transformative impact of artificial intelligence (AI) on inventory management systems, highlighting its potential to optimize operations, enhance decision-making, and reduce costs in an increasingly complex supply chain landscape. Traditional inventory management practices often struggle with issues such as stockouts, overstocking, and inaccurate demand forecasting, leading to inefficiencies and customer dissatisfaction. By leveraging AI technologies—such as machine learning, predictive analytics, and automation—businesses can analyze vast datasets to derive actionable insights that improve inventory accuracy and responsiveness. This research explores various applications of AI in inventory management, including demand forecasting, inventory optimization, and automated replenishment processes. Through a comprehensive review of existing literature and case studies, the study demonstrates how AI can facilitate real-time visibility, enhance inventory turnover, and ultimately contribute to a more agile and efficient supply chain. The findings underscore the significance of integrating AI into inventory management systems, offering valuable recommendations for organizations seeking to enhance their operational effectiveness and competitive advantage in the market.

This study investigates the transformative impact of artificial intelligence (AI) on inventory management systems, highlighting its potential to optimize operations, enhance decision-making, and reduce costs in an increasingly complex supply chain landscape.