<![CDATA[Cooking oil is a strategic retail commodity; fluctuations in demand and supply often make it difficult for store managers to determine order quantities and maintain cash flow. This study aims to design and evaluate a cooking oil sales forecasting model at a C&C grocery store using a Single Moving Average (SMA) as a simple, transparent, and easy-to-operate method. Historical sales data is processed over a short-term horizon; several SMA window orders are tested to balance responsiveness to recent changes and signal slippage. Model performance is assessed using Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE); validation results show a low error rate and accuracy above 80%, making it adequate as a baseline for inventory decision-making. As an applied output, the study developed a web-based application (PHP–MySQL) that facilitates data input, SMA calculation, trend visualization, and report printing, thus facilitating store staff in conducting periodic forecasts and setting reorder points. These findings confirm the suitability of SMA for the MSME/grocery store context, while also opening up opportunities for further development—for example, comparison with WMA/SES or integration with a safety stock module—to make ordering decisions more adaptive to market dynamics.]]>
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