Control charts are powerful tools used to monitor the quality of processes. In practice, control
chart limits are often calculated using parameter estimates from an in-control Phase I …

Control charts are widely used in industries to monitor a process for quality improvement.
Evaluation of the average run length (ARL) or average time to signal (ATS) plays an …

We propose a statistical process control scheme that can be implemented in industrial
practice, in which the quality of a process can be characterized by a general linear profile …

When in-control process parameters are estimated, Phase II control chart performance will
vary among practitioners due to the use of different Phase I data sets. The typical measure of …

A control chart based on the change-point model is proposed that is able to monitor linear
profiles whose parameters are unknown but can be estimated from historical data. This chart …

Nonparametric control charts are useful in statistical process control (SPC) when there is a
lack of or limited knowledge about the underlying process distribution, especially when the …

To use control charts in practice, the in‐control state usually has to be estimated. This
estimation has a detrimental effect on the performance of control charts, which is often …

Statistical process control (SPC) involves ongoing checks to ensure that neither the mean
nor the variability of the process readings has changed. Conventionally, this is done by pairs …

The CUSUM control chart is a popular method used to monitor the performance of
production processes. The performance of the CUSUM is generally evaluated with the …

Nonparametric control charts are useful when the underlying process distribution is not likely
to be normal or is unknown. In this paper, we propose two nonparametric analogs of the …