Are estimated control charts in control?

Standard control chart practice assumes normality and uses estimated parameters. Because of the extreme quantiles involved, large relative errors result. Here simple corrections are derived to bring such estimated charts under control. As a criterion, suitable exceedance probabilities are used. \u

Estimation in Shewhart control charts

The influence of the estimation of parameters in Shewhart control charts is investigated. It is shown by simulation and asymptotics that (very) large sample sizes are needed to accurately determine control charts if estimators are plugged in. Correction terms are developed to get accurate control limits for common sample sizes in the in-control situation. Simulation and theory show that the new corrections work very well. The performance of the corrected control charts in the out-of-control situation is studied as well. It turns out that the correction terms do not disturb the behavior of the control charts in the out-of-control situation. On the contrary, for moderate sample sizes the corrected control charts remain powerful and therefore, the recommendation to take at least 300 observations can be reduced to 40 observations when corrected control charts are applied

Control of control charts

Although the Shewhart chart is widely used in practice because of its simplicity, applying this control chart to monitor the mean of a process may lead to two types of problems. The first concerns the typically unknown parameters involved in the distribution, while the second concerns the validity of the assumption of normality itself. The objective of the research is to study and find solutions for these problems. More specifically, our goal is to determine the most suitable control chart to be used in practice. For this, subsequently, so called normal, parametric, nonparametric and combined approaches are considered, leading to corresponding control charts

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Exceedance probabilities for parametric control charts

Common control charts assume normality and known parameters. Quite often these assumptions are not valid and large relative errors result in the usual performance characteristics, such as the false alarm rate or the average run length. A fully nonparametric approach can form an attractive alternative but requires more Phase I observations than are usually available. Sufficiently large parametric families then provide realistic intermediate models. In this paper the performance of charts based on such families is considered. Exceedance probabilities of the resulting stochastic performance characteristics during in-control are studied. Corrections are derived to ensure that such probabilities stay within prescribed bounds. Attention is also devoted to the impact of the corrections for an out-of-control process. Simulations are presented both for illustration and to demonstrate that the approximations obtained are sufficiently accurate for use in practice. \u

Ordered samples control charts for ordinal variables

The paper presents a new method for statistical process control when ordinal variables are involved. This is the case of a quality characteristic evaluated by an ordinal scale. The method allows a statistical analysis without exploiting an arbitrary numerical conversion of scale levels and without using the traditional sample synthesis operators (sample mean and variance). It consists of a different approach based on the use of a new sample scale obtained by ordering the original variable sample space according to some specific ‘dominance criteria' fixed on the basis of the monitored process haracteristics. Samples are directly reported on the chart and no distributional shape is assumed for the population (universe) of evaluations. Finally, a practical application of the method in the health sector is provided