Contemporarily, enterprises strive to continuously enhance quality which is a
basis of customer satisfaction. Numerous advancements to the control charting scheme
have been made to enhance process monitoring. In this thesis, the variable parameters
chart with auxiliary information (abbreviated as VP-AI) is proposed. The VP-AI chart
is designed with a regression estimator that has an improved precision due to the use
of auxiliary variable to estimate the population mean. By adopting the Markov chain
method, the average time to signal (ATS) and expected ATS (EATS) formulae are
derived for known and unknown shift sizes. The findings show that the VP-AI chart
prevails over the basic VP chart and justifies the integration of auxiliary information
to improve the sensitivity of the VP chart. A comparison of the VP-AI chart with its
competing charts shows that, for all shifts, the performance of the VP-AI chart
surpasses the Shewhart AI (SH-AI), synthetic AI (SYN-AI) and variable sample size
and sampling interval AI (VSSI-AI) charts considerably. Additionally, for most shifts,
the VP-AI chart has a superior performance in comparison with the exponentially
weighted moving average AI (EWMA-AI) and run sum AI (RS-AI) charts. The
application of the VP-AI chart is shown using an illustrative example based on a real
dataset. In many situations, the process is multivariate in nature, where more than one
quality characteristic has to be monitored simultaneously. Furthermore, many
companies have adopted the short production runs technique to be more flexible and
specialized. Hence, in this thesis, the fixed sample size (FSS) 2 T short-run chart is
develope
