Association Rule Mining for Optimizing Inventory Management in Retail: Improving Stock Control and Reducing Costs Using ML Model

Retail stock management will therefore remain a fundamental business strategy as it affects customers' experiences, the business processes and the bottom-line. The use of association rule mining (ARM) as data-driven approach to the management of inventory is the theme of this research. ARM helps retailers discover profound patterns and links in the sales data so that they may effectively manage their stocks, avoid overstock and understock conditions, and reduce expenses. The research also examines how ARM can be implemented using transactional data, either incidentally or in association with conventional inventory management techniques. They provide the bases for decisions concerning restocking of the current stocks, location of shelf space and planning for promotions. Further, the study examines the engagement of ARM in demand forecasting, supplier management aimed at timely supply chain and supply chain improvement. The practical analysis of various case studies and simulation prove the decrease of inventory carrying costs and stockouts, showing the effectiveness of ARM in the real conditions of the retail industry. This means that the use of the proposed approach offers a scalable solution that can effectively fulfill retailers' goal of improving customer experience while increasing operational effectiveness. The findings suggest that ARM has the potential to revolutionize current inventory management mechanisms and provide a competitive strategy for firms in the evolving retail context.