Statistical process control (SPC) requires statistical methodologies that detect changes in the pattern of data over time. The common methodologies, such as Shewhart, cumulative sum (cusum), and exponentially weighted moving average (EWMA) charting, require the in-control values of the process parameters, but these are rarely known accurately. Using estimated parameters, the run length behavior...

This paper considers statistical process control (SPC) of univariate processes, and tries to make two contributions to the univariate SPC problem. First, we propose a continuously variable sampling scheme, based on a quantitative measure of the likelihood of a process distributional shift at each observation time point, provided by the p-value of the conventional cumulative sum (CUSUM) charting...

Often in industry critical quality characteristics can be measured by more than one measurement system. Typically, in such a situation, there is a fast but relatively inaccurate measurement system that may be used to provide some initial information, and a more accurate and expensive, and possibly slower, alternative measurement device. In such circumstances, it is desirable to determine the mi...

• Statistical process control (SPC) is a management philosophy that relies on straightforward statistical tools to identify and solve process problems. • By systematically identifying potential problems in process control, managers can proactively make corrections before quality outcomes suffer. • SPC methods are useful in helping managers to measure whether their processes and products conform...