<![CDATA[Inventory is a critical component in business operations, particularly in the Food and Beverages (F&B) sector, which demands accurate management to mitigate the risks of stockouts and overstocking. However, many MSMEs still rely on conventional methods based on subjective assumptions, which are prone to errors and unable to accommodate demand fluctuations. This research aims to develop a web-based raw material stock forecasting information system integrated with the Single Moving Average (SMA) method to enhance inventory management efficiency. The system development follows a prototyping approach, encompassing data collection, quick design using UML, prototype construction using PHP Laravel and Python, evaluation, and system refinement. The dataset utilized consists of 12 periods of monthly financial reports from Benjiro Sushi, Lamper branch. Accuracy evaluation was conducted using MAPE, MSE, and RMSE metrics. The results indicate that the application of SMA without data pre-processing resulted in a high error rate (MAPE 77.25%). However, after implementing pre-processing techniques—including backward interpolation, outlier capping, and iterative smoothing—the accuracy improved significantly, with MAPE values ranging between 20.4% and 21.0%. The developed system provides automated and real-time stock predictions, facilitating more precise, efficient, and structured procurement decision-making. Thus, this system is considered effective in addressing inventory challenges within the F&B sector.]]>
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