<![CDATA[Inventory uncertainty is one of the main operational challenges faced by retail businesses, including automotive battery stores. Inaccurate inventory planning can lead to overstock and stockout conditions, both of which negatively affect operational efficiency and customer satisfaction. This study aims to forecast battery sales using the Autoregressive Integrated Moving Average (ARIMA) model as the basis for determining Safety Stock and Reorder Point (ROP) at Toko Aki Restu. The research adopts the CRISP-DM framework consisting of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment stages. Historical monthly sales data from October 2024 to October 2025 were analyzed using the ARIMA method. The results showed that ARIMA(1,0,2) was the best forecasting model based on the smallest Akaike Information Criterion (AIC) value. The evaluation process generated MAE of 9.85, RMSE of 12.27, and MAPE of 5.3%, indicating that the forecasting model had very high accuracy. Forecasting results were then utilized to calculate Safety Stock and Reorder Point values. The study produced a Safety Stock recommendation of 10 units and a Reorder Point of 54 units. Furthermore, the forecasting and inventory recommendations were visualized through a web-based dashboard to support operational decision-making. The implementation of this study is expected to help the company minimize inventory risks and improve stock management efficiency.]]>
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