<![CDATA[This study analyzes the effectiveness and accuracy of a demand forecasting based production system to reduce excess stock at the Ayam Geprek Bu Novi culinary business on Jl. Bukit Barisan. Using a quantitative approach, the research applies time series forecasting techniques consisting of Moving Average (MA), Weighted Moving Average (WMA), and Exponential Smoothing (ES). Weekly demand data from February to May 2025 were collected and analyzed to measure prediction accuracy and to evaluate the impact on raw material efficiency. The results show that the 3 week WMA method provides the highest responsiveness to rising demand trends and produces forecasts that are closest to actual sales. In comparison, the 6 week MA method and ES with alpha 0.2 provide more stable performance when demand fluctuations are relatively low. The application of these simple Excel based forecasting models enables micro, small, and medium culinary enterprises (MSMEs) to align production more accurately with actual demand, thereby reducing overstock and improving operational efficiency. This study demonstrates that data driven decision making using easy to use forecasting tools can support MSMEs in optimizing inventory control and maintaining production sustainability. The findings offer practical insights for small fast food businesses that need adaptive inventory management solutions without complex systems or high implementation costs.]]>
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