Control, statistical quality

The principles of quality assurance are commonly related to product and process control in manufacturing. Today the field of application greatly expanded to include environmental protection and quality control within analytical chemistry itself, i.e., the quality assurance of analytical measurements. In any field, features of quality cannot be reproduced with any absolute degree of precision but only within certain limits of tolerance. These depend on the uncertainties of both the process under control and the test procedure and additionally from the expense of testing and controlling that may be economically justifiable. 

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Statistical quality control is used to first measure and then continuously improve product quality. For example, The Dow Chemical Company s average 1989 performance compared to the typical sales specification were purity, = 99.65 wt % color, APHA = 4 acid (as HCl) = 7.3 ppm and water = 26 ppm. Averages of properties were based on rail car and tank tmck shipment samples during 1989. 

More details on statistical process control are available in several textbooks (Grant and Leavenworth, Statistical Quality Control, McGraw-HiU, New York, 1980 Montgomery, Introduction to Statistical Quality Control, Wiley, New York, 1985). 

Statistical process control (SPC) is an important on-line method in real time by which a production process can be monitored and control plans can be initiated to keep quality standards within acceptable limits. Statistical quality control (SQC) provides off-line analysis of the big picture such as what was the impact of previous improvements. It is important to understand how SPC and SQC operate. 

Rodbard, D. et al. Statistical Quality Control of Radioimmunoassays.. 1. Clin. Endocrinol, and Metab. (1968),... 

Rodbard, D. Statistical Quality Control and Routine Data... 

Of the various methods of data presentation, the one with which starting analysts may be least familiar is trend analysis and statistical quality control. In an industrial environment, analysis is often centered around the production of batches of material. The properties of those batches may change over time due to random effects or to subtle changes in the production process. In either case, the quality of the product may change. Analysis is used to track the change in the properties of batches over time. Industrial analytical methods, therefore, need to be extremely rugged. Millions of dollars may depend on the analyst s judgment as to batch equivalence. 

Statistical Quality Control Using the SAS System by Dennis W. King... 

On the one hand, statistical quality control is an important tool for quality assurance within analytical chemistry itself (monitoring of test methods), and on the other for quality control of processes and products by means of analytical methods. 

Montgomery DC (1985) Introduction to statistical quality control. Wiley, New York... 

As well as the usual meaning of the control of quality this phrase is particularly used with reference to manufactured products. See Statistical Quality Control. 

Montgomery, D.C. (1985). Introduction to Statistical Quality Control. Wiley, New York. 

Grant, E.L., and Leavenworth, R.S. (1988), Statistical Quality Control, 6th ed., McGraw-Hill, New York, NY. Green, P.E. (1976), Mathematical Tools for Applied Multivariate Analysis Student Edition, Academic Press, New York, NY. 

Small, Bonnie B. (1956), Statistical Quality Control Handbook, Western Electric, Indianapolis, IN. 

Operator Quality Control Foreman Quality Control Full-time Inspectors Statistical Quality Control Total Quality Control Total Quality Management... 

Section 9.2 will review traditional statistical process control/statistical quality control (SPC/SQC) techniques used in quality control. Section 9.3 will follow this review with a discussion of techniques based primarily on an experiential rule base and expert system technology. Section 9.4 will discuss control strategies that use an on-line process model a variety of models can be used in such model predictive control. Section 9.5 will discuss this variety of models. Section 9.6 will summarize this chapter and discusses future trends in the field. 

The traditional approach to quality control is to generate charts of various kinds to monitor the performance of a production unit. At a superficial level, statistical process control (SPC) and statistical quality control (SQC) [9] are terms used interchangeably to describe traditional... 

One approach for using DOE on more complex processes is to do the majority of the process development on smaller, representative sections of material, such as test panels, rather than on full-scale parts, and then to scale up with a more limited experimental matrix. There is no guarantee that experience on small-scale test panels will directly translate to large parts because dimensions and thickness of the part are important variables in their own right. Another way to save on costs is to start with a satisfactory process and to continue, via careful monitoring of process variations and results, to extend the range of experience. This method is variously called statistical process control or statistical quality control. 

SPC or statistical quality control (SQC) is similar to DOE in that it is a statistical, rather than mechanistic, method. Both SPC/SQC and DOE rely on the theory that there is a direct relationship between variations in process controls and resulting changes in product quality. In SPC, however, the experiments are not forced on the process like they are in DOE. The variations in product quality and the random process variations are traced over time instead. The variations in end product are then correlated, if possible, with changes in the process that have occurred during that time. SPC techniques are usually applied to the process after some baseline process has been established by other methods. 

Grant, E.L., Statistical Quality Control, McGraw-Hill. 

Ledolter, J, and Burrill, C. W. (1998), Statistical Quality Control Strategies and Tools for Continual Improvement, Wiley, New York. 

Western Electric Corporation (1956), Statistical quality control handbook (Indianapolis, IN AT T technologies). 

It is assumed that before the analyst has considered using the assay for patient samples, it has been analytically validated (i.e. the recovery, sensitivity, linearity and reproducibility have been determined) and that the method is able to reproduce results, under ideal conditions, so that its variation is sufficiently predictable to apply statistical quality control. 

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