<![CDATA[Forecasting stock requirements is vital for grocery businesses to optimize inventory management and meet market demand efficiently. This study addresses the challenges faced by Toko Amah, a grocery store relying on manual stock recording prone to errors, by developing a web-based forecasting system. Utilizing the Trend Moment method, the system analyzes historical sales data to predict future stock needs with greater accuracy. The study aims to enhance inventory decision-making processes, reduce human error, and improve overall business efficiency. The system was evaluated by comparing forecasted stock with actual inventory data, yielding a Mean Absolute Percentage Error (MAPE) of 20.89%. While the results indicate acceptable accuracy, further refinement is needed to minimize prediction errors. This study concludes that the Trend Moment method provides a practical solution for stock forecasting, supporting more systematic and reliable inventory management in grocery businesses.]]>
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