In product and process optimization, it is common to have multiple responses to be optimized. This is called multi-response optimization (MRO). When optimizing multiple responses, it is important to consider variability as well as mean of the multiple responses. The authors call this problem as extended MRO (EMRO) where both of mean and variability of the multiple responses are optimized. In this article, they propose a data mining approach to EMRO. In these days, analyzing a large volume of operational data is getting attention due to the development of data processing techniques. Traditional MRO methods takes a model-based approach. However, this approach has limitations when dealing with a large volume of operational data. The authors propose a particular data mining method by modifying patient rule induction method for EMRO. The proposed method obtains an optimal setting of the input variables directly from the operational data where mean and standard deviation of multiple responses are optimized. The authors explain a detailed procedure of the proposed method with case examples.
Optimizing Mean and Variance of Multiresponse in a Multistage Manufacturing Process Using a Patient Rule Induction Method
