<![CDATA[Effective inventory management is a crucial aspect in ensuring the continuity of production processes and fulfilling customer demand optimally, especially in a dynamic and fluctuating market environment. PT ABC, as a manufacturing company producing filter components, faces challenges related to demand fluctuations and additional Purchase Orders (PO) from customers, which impact material availability. This study aims to analyze the planning and control of filter inventory by applying forecasting methods, specifically Moving Average and Linear Regression methods. Historical demand data were used to forecast the requirements for filters with diameters Ø50 mm and Ø72 mm. The results indicate that Linear Regression produces lower forecasting errors compared to Moving Average for both filter sizes. For the Ø50 mm filter, Linear Regression yields an MSE of 3,524,583.3, MAD of 1,576, and MAPE of 0.4121, all of which are lower than the values produced by the Moving Average method. A similar pattern is observed for the Ø72 mm filter, where Linear Regression results in an MSE of 1,182,480.374, MAD of 817, and MAPE of 0.2667, which are also lower than those generated by Moving Average. Therefore, this study concludes that Linear Regression is the recommended forecasting method for planning filter requirements at PT ABC. Keywords: Filter, Forecasting, Linier Regression, Material, Moving Average]]>
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