Applying Hierarchical Forecasting Methodology to Seasonal Data Forecast

Accuracy of demand information could be lower by decreasing the variance of demand. Hierarchical forecasting methodology is a useful way to decrease the variance of demand. According to the diversification of product and reduction of product life cycle, forecasting all the product items for gaining more information from the items will spend huge cost and time in deriving forecasting model..n this study we discover multiple variables of transfer function can make the model much accuracy in prediction. According to the revised deposition method, we discover the total average deposition is the best way to deposit data. After analyzing each level, TT (forecasting top-level directly) is the best prediction way in top-level. BM (the prediction value of Bottom-level up to middle level) is the best prediction way in middle-level. MB (the prediction value of middle-level down to bottom-level) by using total average deposition is the best prediction way in bottom-level. When we use TB (the prediction value of top-level down to bottom-level) method cross two levels to generate prediction value, we discover it will increase the prediction error. Observing the correlation of the data, we discover when the data has negative relation we can use the upper integration method to generate better prediction value. Enterprise can observe correlation of the data and deices which level is suited for up-integration method. As considering the cost factor, the enterprise must constrain the forecasting model. The company also can use the best deposition method to decrease the prediction errors. The pre-analysis steps can reduce the complex of deriving the forecasting model and make it much convenience and accuracy.