Quality control charts for short or long runs without a training phase. Part 2. Performances in the presence of a persistent systematic error and simultaneous small shifts in the mean and the variance

Alvarez-Prieto M., Páez-Montero R.S.

Sometimes analytical laboratories receive requests with a small number of determinations and/or a small number of samples, or outside the typical scope of analytical services. As a result, they may not have historical data on the performance of analytical processes and/or appropriate reference materials. Under these conditions it is difficult or uneconomical to use traditional or classic quality control charts. This is the so-called start-up problem of these charts. The Q charts seem appropriate charts under these conditions because they do not need any prior training or study phase. The fundamentals and the algebraic expressions of Q charts for the mean (four cases) and for the variance (two cases) are offered. This experimental study of Q charts for individual measurements was done with data from quality control for the evaluation of mass fraction of Ni and Al2O3 in a laterite CRM by ICP-OES. The performance of these Q charts is discussed where the analytical process is in the state of statistical control and in the presence of outliers at the start-up. In the first situation performance of Q charts are quite satisfactory and they behave properly. When outliers are collected at the beginning, the deformation of some charts is evident or the charts become useless. Severe outliers will corrupt the parameter estimates and the subsequent plotted points, or the charts will become insensitive and useless. The practitioner should take extreme care to assure that the initial values are obtained in the state of statistical control to have adequate sensitivity to detect parameter shifts.

Thompson M., Magnusson B.

Internal quality control (IQC) is an essential feature of routine analysis, serving to ensure that the uncertainty of results found during the validation of a procedure is maintained over long periods of time. The primary method of IQC is to analyse a surrogate material alongside the test materials in every run of analysis and thus address run-to-run precision (a subset of VIM3-defined ‘intermediate conditions’). This ‘control material’ must be as similar as practicable in composition to the routine test materials, although there are always some differences. Results from the control material (control values) are plotted on a control chart, and out-of-control results have to be investigated and problems rectified. Considerable care is needed in obtaining correct values of the parameters for determining statistical control limits, and these can be adequately estimated only during routine use of the analytical procedure. In contrast, target control limits have to be set on a fitness-for-purpose basis and are necessarily wider that statistical control limits. An additional type of internal quality control can be executed by the analysis of duplicate test portions of some of the actual test samples. This provides a realistic dispersion, but addresses only repeatability precision. A further complication of duplication is that the precision of results typically varies with concentration of the analyte.

Quesenberry C.P.

The sensitivity of four tests on Shewhart type Q charts and of specially designed EWMA and CUSUM Q charts 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 beyon..

Thompson M., Wood R.

Abstract

Howarth R.J.

Quesenberry C.P.

Classical control charts are designed for processes where data to estimate the process parameters and compute the control limits are available before a production run. For many processes, especially in a job-shop setting, production runs are not necessa..

Crowder S.V.

Plots of optimal smoothing parameters and control limit constants are given which make the design of exponentially weighted moving average charts simple. A design strategy is reviewed...

Hunter J.S.

The Shewhart and CUSUM control chart techniques have found wide application in the manufacturing industries. However, workpiece quality has also been greatly enhanced by rapid and precise individual item measurements and by improvements in automatic dynamic machine control. One consequence is a growing similarity in the control problems faced by the workpiece quality control engineer and his compatriot in the continuous process industries. The purpose of this paper is to exposit a control chart technique that may be of value to both manufacturing and continuous process quality control engineers: the exponentially weighted moving average (EWMA) control chart. The EWMA has its origins in the early work of econometricians, and although its use in quality control has been recognized, it remains a largely neglected tool. The EWMA chart is easy to plot, easy to interpret, and its control limits are easy to obtain. Further, the EWMA leads naturally to an empirical dynamic control equation.

Groth T., Hunt M.R., Barry P.L., Westgard J.O.