Abstract

In the modern business landscape, supply chain management has become increasingly complex, with organizations seeking innovative ways to improve operational efficiency, reduce costs, and meet ever-evolving customer demands. One of the most transformative advancements in this domain is the use of supply chain analytics, a data-driven approach that leverages advanced technologies such as machine learning, artificial intelligence, and big data analytics to optimize inventory management. This paper evaluates the effectiveness of supply chain analytics in enhancing inventory management practices by examining its impact on key areas such as demand forecasting, stock optimization, supplier performance management, and real-time inventory visibility. The paper explores how predictive, descriptive, and prescriptive analytics can help businesses improve decision-making, reduce the risk of stockouts and overstocking, minimize inventory holding costs, and improve overall supply chain efficiency. Furthermore, it discusses the challenges faced by organizations in implementing supply chain analytics, including data integration issues, the complexity of advanced tools, and the high initial investment costs. Despite these challenges, the paper concludes that the integration of supply chain analytics offers significant benefits, including improved forecasting accuracy, enhanced operational efficiency, cost reduction, and a more agile supply chain. As companies continue to navigate the complexities of a globalized market, the adoption of supply chain analytics will be a key factor in maintaining competitive advantage and achieving long-term sustainability.