<![CDATA[Demand for carton boxes is a critical aspect of the industrial supply chain, particularly in ensuring the smooth flow of production and distribution processes at PT PDPPM. Inaccuracies in forecasting carton box demand have led to several issues, such as excess inventory or packaging material shortages, which disrupt operations and increase storage costs. This study aims to forecast the demand for cardboard boxes at PT PDPPM using two analytical approaches: Moving Average and Linear Regression. Moving Average is applied to identify demand patterns by smoothing fluctuations in historical data, while Linear Regression is used to analyze the relationship between independent variables that influence demand. The results of this study indicate that the Moving Average approach provides stable predictions and tends to be more suitable for data with seasonal patterns, whereas Linear Regression offers additional insights into the influence of external factors on demand. The evaluation was conducted quantitatively using statistical indicators such as Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), where the Linear Regression method demonstrated the lowest error rate. The combination of these two methods is expected to provide a more accurate forecasting outlook, beneficial for production planning and inventory management at PT PDPPM. Keywords: Forecasting, Moving Average, Regresi Linear]]>
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