<![CDATA[This research aims to analyze and develop a sales forecasting model for sugar products at PT Rajawali Nusantara Indonesia (Persero) by applying the time series method to support the company’s strategic decision-making. A quantitative research approach with a descriptive design is utilized, in which secondary data are collected from monthly sales records of sugar products spanning from 2022 to 2024. The analysis begins with the identification of historical patterns through the visualization of time series graphs and autocorrelation testing (ACF) using Minitab software to reveal the underlying trend and seasonal components within the sales data. Three forecasting methods are implemented—Three Moving Average, Double Exponential Smoothing, and Multiplicative Decomposition—and their performance is evaluated using error metrics such as Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and Mean Squared Error (MSE). The evaluation results indicate that the Multiplicative Decomposition method provides the best performance, with a MAPE of 44.62%, MAD of 7,327.83, and MSE of 96,813,267.86. Subsequently, the forecasting model is developed as a Microsoft Excel template programmed using VBA to automate sales projections for the next two years. This model is expected to enhance production planning, inventory management, and marketing strategies.]]>
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