<![CDATA[Parts distribution companies face challenges in balancing stock availability and operational cost efficiency. The low Inventory Turnover Ratio (ITR) of 2.46–2.67 in 2024 reflects inefficiencies in inventory planning and control, which leads to high storage costs and slow-moving accumulation of goods. This research aims to identify the root cause and optimal solutions through a mixed method approach. Qualitative data was obtained from semi-structured interviews with demand planners and branch heads, while quantitative data was analyzed using historical data for 2024. Accuracy evaluation was carried out on the Moving Average, Weighted Moving Average, and Exponential Smoothing forecasting methods using MAPE, MAD, RMSE, and Tracking Signal. Current Reality Tree (CRT) is used to map the root of the problem, and ABC classification to focus on high-value parts. The Fixed-Order Quantity (Q) and Fixed-Time Period (P) inventory models were also compared. The results showed that the Weighted Moving Average (6–7 months) and Exponential Smoothing improved the forecasting accuracy by 43.93%. The integration of this method with the Q and P models is able to reduce the risk of overstock and stockout, and results in an annual inventory cost efficiency of 0.33%. The increase in the ITR ratio close to the company's target underscores the importance of using predictive analytics and integrated inventory management systems in supporting operational efficiency and financial performance. This study recommends forecasting optimization and system integration as a strategic step for parts distribution companies.]]>
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