Some estimators of a finite population mean using auxiliary information
Applied Mathematics and Computation
Computers and Operations Research
Monitoring process variability using auxiliary information
Computational Statistics
An EWMA chart for monitoring the process standard deviation when parameters are estimated
Computational Statistics & Data Analysis
Monitoring process mean and variability with generally weighted moving average control charts
Computers and Industrial Engineering
Shewhart-type control charts for variation in phase I data analysis
Computational Statistics & Data Analysis
A process variability control chart
Computational Statistics - Proceedings of DSC 2007
A multivariate control chart for simultaneously monitoring process mean and variability
Computational Statistics & Data Analysis
New EWMA control charts for monitoring process dispersion
Computational Statistics & Data Analysis
A sum of squares double exponentially weighted moving average chart
Computers and Industrial Engineering
Enhancing the performance of CUSUM scale chart
Computers and Industrial Engineering
Efficient power computation for r out of m runs rules schemes
Computational Statistics
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In Statistical Process Control (SPC), monitoring of the process dispersion has a major impact on the performance of processes like manufacturing, management and services. Control charts act as the most important SPC tool, used to differentiate between common and special cause variations in the process. The use of auxiliary information can enhance the detection ability of control charts and hence an efficient monitoring of process parameter(s) can be done. This study deals with the Shewhart type variability control charts based on auxiliary characteristics for the non-cascading processes, assuming stability of auxiliary parameters. The control chart structures of these variability charts are provided and their performance evaluations are carried out in terms of average run length (ARL), relative average run length (RARL) and extra quadratic loss (EQL) under the normal and t distributed process environments. The comparisons have been made among different variability charts and superiorities are established based on their detection abilities for different amounts of shifts in process dispersion. An illustrative example is also provided in support of the theory, and finally the study ends with concluding remarks and suggestions for future research.