<![CDATA[PT EPK is a company engaged in the printing and packaging industry, producing cardboard, paper pallets, and polyfoam. One of its products, Outer Generic Uli, experiences unstable demand, making it difficult for the company to accurately forecast market needs. Inaccurate forecasting can lead to stock overages or shortages and increased storage costs. Moreover, selecting the most appropriate forecasting method remains a challenge. This study aims to compare three forecasting methods—moving average, double exponential smoothing, and linear regression—to determine the most accurate model. Forecast accuracy is evaluated using tracking signal and mean square error (MSE). The results show that linear regression yields the most accurate forecast with the lowest MSE of 642,460.32. In comparison, double exponential smoothing has an MSE of 991,250, while moving average results in an MSE of 2,832,031.25. The significant difference in MSE indicates that linear regression outperforms the other methods. Implementing this method is expected to optimize inventory management, reduce the risk of stock imbalance, and enhance both supply chain efficiency and decision-making accuracy.. Keywords: Demand, Double Exponential Smoothing, Forecasting, Linear Regression, Moving Average]]>
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