FORECASTING PERIODIC SERIES TO REDUCE THE BULLWHIP EFFECT IN SUPPLY CHAIN SYSTEMS USING MOVING AVERAGE AND EXPONENTIAL SMOOTHING - Alivia Fazricha Muzamil Putri; Nurfadillah, Suryani; Ekowati, Titik (Agric, 2025)
Demand forecasting is one of the key components in supply chain management, particularly in the food and beverage industry, which has dynamic and fluctuating demand levels. This study aimed to analyze the occurrence of the bullwhip effect in the production of Parijoto (Medinilla speciosa) syrup of CV Seleksi Alam Muria. and to analyze the best forecasting method to minimize the bullwhip effect. The benefits of this research were to serve as a reference for development efforts aimed at reducing the bullwhip effect in production, thereby optimizing the supply chain in a company. The forecasting methods used were Moving Average and Exponential Smoothing. Minitab Software assisted with the forecasting calculations in this study. The study results showed that the initial bullwhip effect value (1.043) was higher than the parameter value (1.005), indicating the occurrence of the bullwhip effect in the production of Parijoto syrup. Furthermore, this study also found that the Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) values for the Moving Average method were lower compared to the Exponential Smoothing method. The forecasting result using the Moving Average method shows that the bullwhip effect value is significantly lower if it follows the recommended values derived from this forecasting method. Applying the Moving Average method indirectly minimizes the risk of amplification or overproduction.