Demand Forecasting with Five Parameter Exponential Smoothing

Abstract

Changes in technology has affected many aspects on economic development. One of them is Newspaper Industry. Reports indicate that globally the newspaper is passing through its hardest time ever. Therefore, newspaper industry need to be more creative in order to be able to deal with change and maintain its existence. In supply chain management newspaper industry, forecasting is the most important part to predict the future demand, minimize waste of product, scheduling production, optimize inventory level and resources. The exponential smoothing methods are simple but the best approach and popular methods used for forecasting. This study aimed to implement five parameter exponential smoothing to predict number of newspaper demand in the future with various method to get the best forecast result. The perfomance of method is evaluated using a newspaper demand daily time series. MSD and MSE used to determining and selecting the best forecasting method. The result show that additive Holt winters method with damped trend (DAHWM) is suitable for demand forecasting in newspaper industry.