is the application of statistical
techniques to reduce variability in key parameters and ensure
quality improvement. Among these techniques, control charts are
extensively adopted as a tool for process monitoring in modern
industries and non-manufacturing sectors. For simplicity, the
development and evaluation of Phase-II control charts are usually
based on the indispensable assumption of known process parameters.
There are many real industrial situations in which the process
parameters are unknown and are estimated from an in-control reference
Phase-I dataset. When process parameters are estimated,
the control chart’s performance differs significantly from the
known-parameter case. This fact is attributed to the additional variability
of the estimates computed from the Phase-I process.