<![CDATA[Small and medium-sized enterprises (SMEs) frequently face demand uncertainty and fluctuations, which complicate production scheduling and raw material planning. In the souvenir industry, inaccurate demand estimation may lead to overstocking or material shortages that disrupt production activities. This study aims to forecast the sales of souvenir goodie bags at Depouch Bag Souvenir using time series forecasting methods and to integrate the forecasting results into Material Requirement Planning (MRP) to improve production planning efficiency. This research evaluates three forecasting techniques, namely the 3-period Moving Average (MA), Exponential Smoothing (ES), and Weighted Moving Average (WMA). Forecasting accuracy is assessed using the Mean Absolute Percentage Error (MAPE). The results show that the Moving Average method produces the highest forecasting accuracy with a MAPE value of 15%, followed by Exponential Smoothing with 17%, and Weighted Moving Average with 20%. Based on these results, the Moving Average method is selected as the most appropriate model for predicting future demand. The forecasting results are then used as input in the Material Requirement Planning (MRP) system to determine the quantity and timing of raw material procurement required to meet production demand. The findings indicate that integrating demand forecasting with MRP can support better inventory control and reduce the risk of material shortages in production processes. The novelty of this study lies in the integration of time series demand forecasting and Material Requirement Planning within the operational context of SMEs experiencing fluctuating demand, providing a practical approach that enhances production planning and inventory management efficiency in small-scale manufacturing environments.]]>
