With development of the database, there are too many data on process variables and the manufacturing process for the traditional statistical process control methods to identify the process variables related with assignable causes. Data mining is useful in

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manuf

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manufacturing process. The purpose of this paper is to model the recognition of defect type patterns and prediction of each defect type before it occurs in manufacturing process. The proposed model consists of data handling, defect type analysis, and defect type prediction stages. The performance measurement shows that it is higher in prediction accuracy than logistic regression model.

In the era of customer satisfaction(CS), managers in the service industry have been using new strategies and a new reinforcement of service quality in order to increase their competitiveness. In this paper, the Service-QFD model is based on SERVQUAL and Quality Function Deployment models. The service quality in each stage of service delivery process is measured by SERVQUAL model and the relationship is represented by the QFD model. We propose the effective model that is consistent with preference of users considering of weighting scale, which seriously affects technical importance rating.