Abstract:
Multivariate statistical process control charts are used for process monitoring and control of two or more variables simultaneously for quality and quality improvement. A popular multivariate control chart is used to monitor the mean vector of the process. A usual problem in the use of the multivariate control chart is the identification and interpretation of variable(s) responsible for out-of-control signal that occurred in the chart. This has brought many developed techniques from many researchers to aid in finding the variable(s) responsible for the out-of-control signal in the multivariate control chart. Thus, the work is aimed at a comparative study of some developed techniques for identifying and interpreting out-of-control signal in the multivariate control chart when applied to the cable production process. The techniques are Mason-Tracy-Young, Donganaksoy-Faltin-Tucker, Univariate -chart using Bonferroni control limits by Alt and Principal component analysis by Jackson. A performance criterion, the power of a test was used to ascertain the most satisfactory technique that explained the out-of-control signal that occurred in the multivariate control chart. From the results and discussions, Mason Tracy-Young and Doganaksoy-Faltin-Tucker techniques are the most satisfactory for identifying and interpreting out-of-control signal in the multivariate control chart for the datasets considered.
