Control Shewhart

However, risk in the process world has an even more fundamental role that is far more just to fulfill the Agency s expectation about process understanding and product safety it is the basis and rational for MSPC. In fact, it is what the original intent of Walter Andrew Shewhart (1891-1967) had in mind when he invented the notion of statistical process control (SPC) and the control Shewhart chart. Although not couched in precisely the language of risk, it was at the heart of what he was trying to do at Bell labs at the time [11]. 

Shewhart, W. A. Economic Control of the Quality of Manufactured Products. Macmillan London, 1931. 

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

A form of this approach has long been followed by RT Corporation in the USA. In their certification of soils, sediments and waste materials they give a certified value, a normal confidence interval and a prediction interval . A rigorous statistical process is employed, based on that first described by Kadafar (1982,), to produce the two intervals the prediction interval (PI) and the confidence interval (Cl). The prediction interval is a wider range than the confidence interval. The analyst should expect results to fall 19 times out of 20 into the prediction interval. In real-world QC procedures, the PI value is of value where Shewhart (1931) charts are used and batch, daily, or weekly QC values are recorded see Section 4.1. Provided the recorded value falls inside the PI 95 % of the time, the method can be considered to be in control. So occasional abnormal results, where the accumulated uncertainty of the analytical procedure cause an outher value, need no longer cause concern. 

Shewhart W (1931) Economic Control of Quality of Manufactured Products. Van Nostrand, New... 

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. 

Shewhart WA (1931) Economic control of the quality of manufactured products. Van Nostrand, New York... 

Figure 6.6 Shewhart charts showing (a) data in control about the target value (b) data offset from the target value (c) drifting data (d) data with a step-change.

A Shewhart chart is used to monitor the variation of individual results over time, compared to a target value. Shewhart charts are useful for identifying when bias has entered a measurement system. Non-random patterns in the data, such as drift or step-changes, indicate that bias is present. A range chart is used to monitor the precision of a measurement system, regardless of whether there is any bias present. The data on both types of chart are best evaluated by setting control limits. 

Shewhart Control Charts , ISO 8258 1991, International Organization for Standardization (ISO), Geneva, Switzerland, 1991. 

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 cusum charts. Only these types are discussed here. The charts can also be used to monitor the performance of analytical methods in analytical laboratories. 

A typical pair of Shewhart charts, (a) Averages chart and (b) ranges chart. Point A shows a lack of control of averages only, point B of ranges only and point C of both together. 

Shewhart, W.A. (1939), Statistical Method from the Viewpoint of Quality Control, The Graduate School, The Agriculture Department, Washington, DC. 

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. 

This chart corresponds to the original Shewhart-chart. For tmeness control, standard solutions, synthetic samples or RM/CRM samples may be analysed. Calibration parameters (slope and intercept) can also be used in a X-chart to check the constancy of the calibration. 

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). 

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]. 

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]. 

Two aspects are important for IQC (1) the analysis of control materials such as reference materials or spiked samples to monitor trueness and (2) replication of analysis to monitor precision. Of high value in IQC are also blank samples and blind samples. Both IQC aspects form a part of statistical control, a tool for monitoring the accuracy of an analytical system. In a control chart, such as a Shewhart control chart, measured values of repeated analyses of a reference material are plotted against the run number. Based on the data in a control chart, a method is defined either as an analytical system under control or as an analytical system out of control. This interpretation is possible by drawing horizontal lines on the chart x(mean value), x + s (SD) and x - s, x + 2s (upper warning limit) and x-2s (lower warning limit), and x + 3s (upper action or control limit) and x- 3s (lower action or control limit). An analytical system is under control if no more than 5% of the measured values exceed the warning limits [2,6, 85]. 

The assessment of the quality of a result must be drawn from a number of observations of the laboratory, the personnel, the methods used, the nature of the result, and so on. The great leap forward in understanding quality came in the twentieth century when people such as Deming, Shewhart, Ishikawa, and Taguchi formulated principles based on the premise that the quality of a product cannot be controlled until something is measured (Deming 1982 Ishikawa 1985 Roy 2001 Shewhart 1931). Once measurement data are available, statistics can be applied and decisions made concerning the future. 

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