PT XYZ is a chemical industry company producing products such as sulfuric acid, aluminum sulfate, and ammonium sulfate fertilizer. In practice, fluctuating demand has caused inaccuracies in forecasting results, leading to company’s excessive production. Additionally, the company's minimum order quantity policy contributes to overstocking issues. Therefore, this research aims to calculate product forecasts using time series method and provide a proposed quantity for each order of product raw materials with the Minimum-Maximum Stock approach through a Minimum-Maximum Stock approach. Based on these problems, calculations were conducted using several forecasting methods, namely the Double Exponential Smoothing and Linear Regression methods to predict usage in the next period. The research employed a quantitative descriptive method by presenting calculations based on monthly product demand data for 2 years from 2022 to 2023. The results of data processing indicate that the Linear Regression method provides the most accurate forecast, with the lowest Mean Square Error (MSE). The forecast results were then used to determine product inventory. Based on the company’s service level of 90%, the research found discrepancies between the Min-Max Stock calculations and the company’s current policy, with differences of 89% for safety stock, 36% for minimum stock, and 28% for maximum stock.

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