During the last two decades, many new methods have appeared in statistical process monitoring with synthetic-type control charts being a prominent constituent. These became popular due to their simplicity and proclaimed excellent change point detection performance. Synthetic are nothing more than application of well-known long established runs rules. We show that better performance can be obtai...

In the previous two Chapters, we proposed fraction nonconforming nonparametric control charts to monitor process location and process variability. It is also shown that performance of proposed control charts are superior to that of the Shewhart X and sign charts. If process is running in an in-control state for a long period, it will reach in steady-state mode. In order to characterize long-ter...

Nonparametric control charts can provide a robust alternative in practice to the data analyst when there is a lack of knowledge about the underlying distribution. A nonparametric exponentially weighted moving average (NPEWMA) control chart combines the advantages of a nonparametric control chart with the better shift detection properties of a traditional EWMA chart. A NPEWMA chart for the media...

Control charts are extensively used in processes and are very helpful in determining the special cause variations so that a timely action may be taken to eliminate them. One of the charting procedures is the Shewhart-type control charts, which are used mainly to detect large shifts. Two alternatives to the Shewhart-type control charts are the cumulative (CUSUM) control charts and the exponentia...

Diagnostic laboratories are increasingly required to meet stringent quality standards, and validated assays are needed to achieve formal accreditation. Validation of test methods is often considered to be finalised when the assay parameters and characteristics have been established. However, like any process, diagnostic assays are subject to random variation resulting in shifts in the mean test...

For an improved monitoring of process parameters, it is generally desirable to have ef?cient designs of control charting structures. The addition of Shewhart control limits to the cumulative sum (CUSUM) control chart is a simple monitoring scheme sensitive to wide range of mean shifts. To improve the detection ability of the combined Shewhart–CUSUM control chart to off-target processes, we deve...

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...

The control limits of an exponentially weighted moving average (EWMA) control chart should vary with time, approaching asymptotic limits as time increases. However, previous analytic analyses of EWMA charts consider only asymptotic control limits. In this article, the run length properties of EWMAs with time-varying control limits are approximated using nonhomogeneous Markov chains. Comparing t...

The sensitivity of four tests on Shewhart type Q-eharis and of specially designed EWMA and CUSUM Q-eharts to detect one-step permanent shifts of either a normal mean or standard deviation has been studied. The usual test that signals for one point beyond three standard deviations on a Shewhart chart is found to have poor sensitivity, generally. The test that signals when four out of five consec...