||In recent years, international raw material prices and labor costs boost continuously, rapid supply and demand changes, shorten of the product life cycle, and coupled with the rise of the red supply chain. Facing changes in the environment, how to reduce costs is the focus of business. In order to improve competitiveness, human-intensive traditional manufacturers began to import automation and emerging technologies, such as Internet technology, cloud computing and intelligent factories. With the sophistication of automatic equipment, customers quality requirements of raw materials will be more stringent, thus poor quality stability of the manufacturers, must bear sluggish inventory and delivery credit loss due to quality fails to match the customer specifications; therefore, how to predict product quality in advance so that create product meets customer specifications, has become an important issue of operational management.|
General production most seeks problem improvement after poor products had been manufactured, but this is too slow when facing fierce competition environment. If product quality can be predicted before manufactured, not only can reduce operation costs and efficiency losses but also to meet customer needs and enhance the company's brand reputation. In this study, TF is used as an example to analyze the accuracy of Flexible Copper Clad Laminates quality prediction by using the neural network and the multiple regression model. The optimal production schedule is arranged by accurate quality prediction. Output to meet customer quality specifications for the purpose of the product.
According to the results of this study, it is shown that the quality prediction of the FCCL is more accurate than the multiple regression model for the electronic material industry TF company. In addition, through the product quality prediction results to do the appropriate group induction in the machine and the quality of raw materials, so as to plan the best production scheduling portfolio to provide TF company in the production plan reference, which can effectively solve the production unstable quality caused by operational losses.