<![CDATA[This study aims to enhance the efficiency of inventory control for uPVC AW 3/4inch pipe products at PT XYZ by applying time series forecasting methods using historical sales data. Three forecasting approaches were tested, Double Exponential Smoothing, Holt Winters Multiplicative, and Seasonal Autoregressive Integrated Moving Average (SARIMA). The accuracy of each model was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and Mean Squared Error (MSE) to determine the most reliable forecasting result. The analysis showed that SARIMA model produced the smallest error value, indicating the best level of accuracy. The forecasting output from this model was then used as a basis for calculating safety stock, reorder point, and maximum stock levels to improve inventory control decisions. The finding indicate that databased forecasting helps PT XYZ align production quantities with actual market demand, reducing overstock conditions and optimizing warehouse capacity utilization. This research highlights the importance of applying quantitative forecasting methods to strengthen data driven decision making in inventory management and provides practical insights for manufacturing companies seeking to improve operational efficiency.]]>
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