Monitoring correlated variable and attribute quality characteristics based on NORTA inverse technique

In some statistical process control applications, quality of a process or a product is characterised and monitored based on a variable or an attribute quality characteristic. However, sometimes a vector of variables or attributes describes the quality of a process. Likewise, in some cases, quality of a process or a product is characterised by a combination of several correlated variables and attributes. To the best of our knowledge, there is no method in monitoring multivariate‐attribute processes in spite of numerous studies in multivariate and multi‐attribute control charts. This paper describes a method to monitor a process with multiple correlated variable and attribute quality characteristics. In the proposed method, we utilise NORTA inverse technique to design a scheme in monitoring multivariate‐attribute processes. First, NORTA inverse method transforms the data to a multivariate normal distribution, and then we apply multivariate control charts such as T² and MEWMA for transformed data. The performance of the proposed method considering both T² and MEWMA charts is investigated by using simulation studies in terms of average run length criterion.