<![CDATA[In the fashion retail industry, inventory management is a major challenge due to fluctuating and unpredictable customer demand. Errors in inventory planning may lead to overstocking or stockouts, increased storage costs, and decreased customer satisfaction. This study aims to develop a decision support system using the Sugeno fuzzy logic method to optimize clothing inventory. The input variables consist of initial stock, incoming goods, and outgoing goods, which are processed through fuzzification, inference, and defuzzification stages to produce the predicted final stock. Experimental results show that the Sugeno fuzzy model achieves better accuracy compared to conventional methods, with a Mean Absolute Percentage Error (MAPE) of 17.99%, equivalent to a prediction accuracy of 82.01%. The main contribution of this research lies in the application of the Sugeno fuzzy method to local fashion retail inventory management, which has generally been carried out manually. This approach enables the system to provide more precise stock recommendations, thereby helping stores reduce the risk of overstocking and stockouts, improve operational efficiency, and enhance business competitiveness.]]>
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