Integrate Neural Network with Grey Theory to Design a Prediction Model

• Main Authors: Chi-Fang Wu, 吳綺芳
• Other Authors: 陳雲岫
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/62666087570730218510

碩士 === 元智大學 === 工業工程與管理學系 === 94 === Nowadays the environment of keen competition, the rapid development of in-formation technology and the weeding out the old and bringing forth the new of products result in enterprises face one is internal departments have to request high quality, low cost and high efficiency, and another is about external surroundings we must to open up our market share and seek the partners among enterprises to collabo-rate. Doing this in order to reduce the whole operating cost and raise the enterprises core competency. Thus, business is facing the global competition and challenge, too. The accurate and timely forecast for the products demand in market is playing an important role in business management. Business administrator looks for the goal holding the first chance to be a leader on market. So administrator must draft the best tactics conscientiously in order to deal with the complicated competition on market. Forecast has the ability to look into the future and it becomes the tool that helps en-terprises increase profits and strengthen the competitive advantage. Now the manufacturing industry globalizes and in the keen competition enter-prises surroundings, the each function of business is more important. This research developed a more correct and faster the forecast method in order to provide enter-prises could predict their supply and demand on market effectively. This research of-fered a Collaborative Planning Forecasting and Replenishment (CPFR) model to manufacturers and retailers collaborate for each other and predict the change of sales on market in order to supply products for customers rapidly and raise customer satis-faction. This research integrated back-propagation network (BPN) and grey prediction model to establish a forecast model. Make the manufacturers and retailers can forecast the market trend in the future to response the market changes.