Lifia Citra Ramadhanti (1), Rakay Edhiargo Toyosito (2), Angga Widyas Swara (3), Abby Yazid Bustommy (4), Moch Eko Nugroho (5)
Demand forecasting plays a crucial role in production planning and inventory control, particularly in the food industry, which is characterized by fluctuating demand and limited product shelf life. This study aims to analyze and apply demand forecasting methods for MX food products in order to improve the accuracy of production planning and inventory control at PT XYZ. Historical sales data are utilized to generate demand forecasts by comparing several quantitative methods, including moving average, exponential smoothing, and linear regression. The accuracy of each forecasting method is evaluated using forecasting error measurements such as Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the method with the lowest forecasting error provides more accurate demand estimates, enabling the company to reduce excess inventory and stock shortages. Improved forecasting accuracy contributes to more efficient production planning, lower inventory costs, and better managerial decision-making at PT XYZ.
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