An Application of Back-Propagation Neural Network in Sales Forecasting: A Case Study of a TFT-LCD company

• Main Authors: Pin-Sheng Hsu, 徐品勝
• Other Authors: 張百棧
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/04385376913766490463

碩士 === 元智大學 === 工業工程與管理學系 === 95 === TFT-LCD ( Thin Film Transistor-Liquid Crystal Display；TFT-LCD) is a newly developed industry in Taiwan. Owing to the rapid changes in the supply-and-demand environment, there have been many studies conducted on the sales forecasting to solve the unbalance of supply and demand. Through establishing a precise sales forecasting model, the upcoming demand can be evaluated in advance to achieve the production balance and decrease the inventory cost. The research is divided into three major phases based on the data collected from a famous TFT-LCD manufacturer in Taiwan: (1) 9 indices are compiled and put into the Back-Propagation Neural Network model. (2) Stepwise Regression Analysis is utilized to sort out factors with higher prediction impact and then Winter’s Exponential Smoothing method is adopted for seasonal time series data analysis to set up a new model. (3) To evolve and equip the optimal model generated from phase 2 with a complete error feedback mechanism. Finally, MAPE, MAD and RMSE are used to compare the index effectiveness of these three models. The results indicate that the hybrid model has the best prediction ability compared to the other two models; hence, it is recommended for business to apply in sales forecasting.