Inventory management is a critical component of supply chain operations, impacting a company’s ability to meet customer demand while controlling costs. Traditional inventory management methods, such as Economic Order Quantity (EOQ) and Reorder Point, offer foundational strategies for optimizing inventory levels. However, these methods often fall short in dynamic and unpredictable market conditions. This chapter introduces an innovative fuzzy decision-making algorithm designed to enhance inventory management by incorporating real-time data and fuzzy logic principles. By adapting to fluctuating demand and supply chain variables, the algorithm aims to reduce the risks of stock outs and overstocking. Through a detailed examination of the algorithm’s steps-fuzzification, rule base construction, fuzzy inference, and defuzzification we demonstrate how businesses can achieve more responsive and efficient inventory control. Additionally, we provide a numerical example and a practical application scenario for a retail chain, illustrating the algorithm’s effectiveness in optimizing inventory levels. The chapter concludes with a discussion on the benefits of integrating fuzzy logic into inventory management and suggestions for future research and implementation.