<![CDATA[Fluctuations in demand for processed banana products often lead to inaccurate inventory planning at the MSME scale, resulting in decreased operational efficiency and potential accounting inaccuracies in inventory valuation and the calculation of Cost of Goods Sold (COGS). The calculation of raw material stock forecasting for 2024-2025 produces the following predicted values: 124 bunches of bananas, 80 pieces of chocolate, 81 kg of cooking oil, and 42 kg of granulated sugar. This simple, fast, and accurate forecasting process enables producers to more accurately predict product demand, ultimately reducing the risk of overstocking or shortages. This study aims to optimize sales strategies and inventory forecasting for processed banana products through a conceptual framework that integrates economic efficiency. The method used is the Simple Moving Average (SMA) to forecast inventory needs based on historical sales data at the BananaChips MSME, by testing several variations of the forecasting period to obtain the most stable and representative results. Overall, the recapitulation results show that the Cooking Oil raw material has the highest forecasting accuracy, with the lowest MAPE of 1.81% (MAD 1.50, MSE 5.20). Meanwhile, Granulated Sugar raw material recorded the highest MAPE value of 5.08% (MAD 2.25, MSE 9.73), followed by Chocolate (MAPE 2.43%) and Banana (MAPE 2.18%). The implementation results show an increase in stock management efficiency of up to 20% and a 15% decrease in excess raw materials. These findings indicate that integrating SMA forecasting with an economic efficiency framework and accounting accuracy can improve the quality of inventory and sales decision-making, thereby strengthening the profitability and sustainability of the banana-processed product business at the Bananachips MSME]]>
