<![CDATA[An effective inventory prediction system is crucial in inventory management to improve efficiency and reduce costs. This study aims to develop an inventory prediction system using the Tsukamoto Fuzzy method, considering three main variables, namely demand, production, and sales. The Tsukamoto Fuzzy method was chosen because it can overcome the uncertainty and ambiguity of data that commonly occur in inventory management. This prediction system is designed to assist companies in making more accurate decisions in inventory management. The system's work process includes three main stages, namely fuzzification of input variables, inference based on 16 fuzzy rules, and defuzzification using the weighted average method. The final prediction result shows a value of 435.5 m3, indicating that this system is capable of producing estimates that are close to actual conditions. This research contributes to providing a fuzzy logic-based quantitative prediction model to support decision-making in logistics management and warehouse management.]]>
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