Quality Control charts Shewhart

Continuous quality control is based on principles that firstly were used in the system of quality control charts (QCC, Shewhart [1931]). Today, admittedly the monitoring of the characteristics of a process or product in order to detect deviations from the target value is not tied to charts but is mostly done by computer, although it is frequently still called a control chart system. 

Many of the quality improvement goals for implementation of PAT in the pharmaceutical industry have been achieved by companies in other industries, such as automobile production and consumer electronics, as a direct result of adopting principles of quality management. The lineage of modern quality management can be traced to the work of Walter Shewhart, a statistician for Bell Laboratories in the mid-1920s [17]. His observation that statistical analysis of the dimensions of industrial products over time could be used to control the quality of production laid the foundation for modern control charts. Shewhart is considered to be the father of statistical process control (SPC) his work provides the first evidence of the transition from product quality (by inspection) to the concept of quality processes [18,19]. 

A control chart (quality control chart) is a graphical record of the results of the analysis of a quality control sample using a particular method. Monitoring these results over a period of time is one of the most useful ways of determining whether or not a method is in statistical control, i.e. it is performing in a consistent manner. It helps to indicate the reliability of the results. There are many forms of control chart,one of the most commonly used is the Shewhart Chart (Figure 4). 

The usage of quality control charts in the field of quality assurance is based on the assumption that the determined results are distributed normally. Typical control charts used in a LIMS for routine analysis are, for example, the Shewhart charts for mean and blank value control, the retrieval frequency control chart, and the range and single-value control chart [19]. Quality regulation charts can be displayed graphically in the system or exported to spreadsheet programs. 

Shewhart, W. A. (1927). Quality Control Charts, Bell System Technical Journal, Vol. 6, pp. 722-735. 

The use of Shewhart control charts with multiple decision rules provide a more sensitive and reliable quality control than Shewhart control with simple decision rules. However, it should be noticed that the multiple decision rules are only valid if the conventional true value pt and the standard deviation oy are known. In practice this means that (jl-t is traceable to a certified reference material (CRM) or established using a definitive method by a validated laboratory [5], and 0-7 is established by method evaluation with a sufficiently high number of method evaluation samples. 

The terms statistical process control SPC) and statistical quality control (SQC) refer to a collection of statistically-based techniques that rely on quality control charts to monitor product quality. These terms tend to be used on an interchangeable basis. However, the term SPC is sometimes used to refer to a broader set of statistical techniques that are employed to improve process performance as well as product quality (MacGregor, 1988). In this chapter, we emphasize the classical SPC techniques that are based on quality control charts (also called control charts). The simplest control chart, a Shewhart chart, merely consists of measurements plotted vs. sample number, and control limits that indicate the upper and lower limits for normal process operation. 

Westgard jo, Barry PL, Hunt MR (1981) A multi-rule Shewhart chart for quality control in clinical chemistry. Clin Chem 27 493-501. 

Shewhart firstly introduced control charts in 1931 for the control of manufacturing processes. Sudden occurring changes as well as gradual worsening of quality can easily be detected. Immediate interventions reduce the risk of producing rejects and minimize client complaints. 

Target control charts are control charts with fixed quality criterions. In the contrary to classical control charts of the Shewhart-type these control charts operate without statistically evaluated values. 

Both the EC50 values and the 3-pM point of the 2,3,7,8TCDD ealibration curve serve as quality criteria. For each participant, the results for both data points from all 96-well plates analyzed during the presented study were collected and reeorded in Shewhart control charts. The Shewhart control chart is used to identify variations on performanee of the DR CALUX bioassay brought about by unexpected or unassigned causes. The Shewhart eontrol chart shows the mean of the EC50 and 3-pM control point and the upper and lower eontrol limits. In Figure 2, a typical Shewhart control chart is shown. Over the analysis period, none of the participants exceeded the aetion levels (AVG 3 S). 

Precision data can be documented in bar charts or control charts such as Shewhart control charts (see the discussion of internal quality control in Section 8.2.3.5). Bar charts plot %RSD values with their corresponding confidence interval. Control charts plot the individual measurement results and the means of sets of measurements with their confidence level (or with horizontal lines representing limits, see below) as a function of the measurement number and the run number, respectively [15,55,56, 58,72, 85]. 

Chemical analysis finds important applications in the quality control of in dustrial processes. In an ideal situation a continuous analysis of the process stream is made and some aspects of this are discussed in Chapter 12. How ever, such continuous analysis is by no means always possible, and it is common to And a process being monitored by the analysis of separate samples taken at regular intervals. The analytical data thus obtained need to be capable of quick and simple interpretation, so that rapid warning is available if a process is going out of control and effective corrective action can be taken. One method of data presentation which is in widespread use is the control chart. A number of types of chart are used but where chemical data are concerned the most common types used are Shewhart charts and cusnm chans. Only these types are discussed here. The charts can also be used to monitor the performance of analytical methods in analytical laboratories. ... 

If the initial calculation is shown to contain a value more than 3 standard deviations from the mean, the value should be rejected and the mean and standard deviation recalculated. The standard deviation obtained may be used as the target standard deviation (s ) in preparation of Shewhart charts or a desired standard deviation can be used which might be related to medical need or to a previous period of satisfactory performance. For a practical guide to statistical techniques used in quality control of analytical methods and the preparation of cusum... 

It is clear that the manual preparation and continual updating of the charts shown in Fig. 2 for a multilevel, multi-analyte quality control system involves a great deal of work. However, it is possible in a multilevel control system to represent all individual values at different levels on one chart which is a variant of the Shewhart mean plot. The difference of an individual value (e.g. from the target mean (x ) is divided by the target standard deviation (sQ and thus the position of the individual value is represented relative to the target mean in standard deviation intervals 1), see Fig. 1. The bias of each value, irrespective of its analyte concentration, is therefore represented on the same standard deviation scale. This is very convenient for manual and computer plotting as complex scaling is avoided. Fig. 4 shows an example of this... 

Fig. 2. Shewhart mean and range charts for valproic acid using the data shown in Fig. 1 and similar data from high, mid, and low pools of quality control serum, against occasion of analysis. -----------------=95% limits ---------------=99% limits...

Internal quality control (e.g., using Shewhart control charts)... 

Normally, CRMs are used for the verification of accuracy, precision, and reliability of the results of analysis carried out in a laboratory (i.e., for checking the quality of its routine work). The CRM is analyzed at specific intervals and the results obtained are used to draw control charts (e.g., Shewhart chart) [63]. This allows visual assessment of the measurement system, the emergence of systematic errors, etc. Application of CRMs for the constmction of control charts is advantagous because of the homogeneity and stability of CRMs, and the ability to assess the accuracy of the results obtained in the laboratory by comparison with the certified value. 

The literature of industrial quality control is now very great. The standard work is W. A.. Shewhart s Economic Control of Quality of Manufactured Product (Macmillan r 1931). Short accounts are British Standard 600R Qualitj Control Charts, B. P. Dudding and W. J. Jennett, 1942 British Standard 1008 Quality Control A First Guide to Quality Control for F-ngineers, E. H. Sealy, H.M.S.O., 1946. A fuller account is L. E. Simon s An Engineer s Manual of Statistical Methods (Chapman Hall, 1941). 

Figure 19-15 Power functions for Westgard muitirule control procedure. A, Random error. B, Systematic error. (From Westgard JO, Barry PL, Hunt MR, Groth T. A multi-rule Shewhart chart for quality control in clinical chemistry. Clin Chem 1981 27 493-501.)...

Westgard JO, Groth T, Aronsson T, et al. Combined Shewhart-cusum control chart for improved quality control in chnical chemistry. Clin Chem 1977 23 1881-7. 

Another important characteristic is that of precision. This becomes evident only when repeat measurements are made, because precision refers to the amount of agreement between repeated measurements (the standard deviation around the mean estimate). Precision is subject to both random and systematic errors. In industrial quality control and chemical analysis, Shewhart Control Charts provide a means of assessing the precision of repeat measurements but these approaches are rarely used in ecotoxicity testing. The effect is that we generally understand little about either the accuracy or the precision of most bioassays. 

A conventional response to issues of variability in bioassays is to construct Shewhart Control Charts based on the results achieved in repeat tests within a laboratory using a reference toxicant. This effectively describes the range of results typically found within the laboratory and hence can be used to define limits within which the laboratory normally expects to operate. However, there is a flaw in such internal quality control because the more variable a laboratory s reference toxicant test results are, the wider the limits of acceptability will be. Indeed, it can serve merely to reinforce high variability or bias. 

From a historical perspective, with the introduction of univariate control charts by Walter A. Shewhart [267] of Bell Labs, the statistical quality control (SQC) has become an essential element of quality assurance efforts in the manufacturing industry. It was W.E. Deming who championed Shew-hart s use of statistical measures for quality monitoring and established a series of quality management principles that resulted in substantial business improvements both in Japan and the U.S. [52]. 

J. O. Westgard, P. L. Barry, and M. R. Hunt, A Multi-Rule Shewhart Chart for Quality Control in Clinical Chemistry, Clin. Chem., 27 (1981) 493. 

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