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Encyclopedia > Statistical process control

Statistical process control (SPC) is a method for achieving quality control in manufacturing processes. It is a set of methods using statistical tools such as mean, variance and others, to detect whether the process observed is under control. In engineering and manufacturing, quality control and quality engineering are involved in developing systems to ensure products or services are designed and produced to meet or exceed customer requirements. ... In statistics, mean has two related meanings: the average in ordinary English, which is also called the arithmetic mean (and is distinguished from the geometric mean or harmonic mean). ... In probability theory and statistics, the variance of a random variable (or equivalently, of a probability distribution) is a measure of its statistical dispersion, indicating how its possible values are spread around the expected value. ... Process (lat. ...

Contents

History

Statistical process control was pioneered by Walter A. Shewhart and taken up by W. Edwards Deming with significant effect by the Americans during World War II to improve industrial production. Deming was also instrumental in introducing SPC methods to Japanese industry after that war. Dr. Shewhart created the basis for the control chart and the concept of a state of statistical control by carefully designed experiments. While Dr. Shewhart drew from pure mathematical statistical theories, he understood data from physical processes never produce a "normal distribution curve" (a Gaussian distribution, also commonly referred to as a "bell curve"). He discovered that observed variation in manufacturing data did not always behave the same way as data in nature (Brownian motion of particles). Dr. Shewhart concluded that while every process displays variation, some processes display controlled variation that is natural to the process, while others display uncontrolled variation that is not present in the process causal system at all times.[1] Walter Andrew Shewhart (March 18, 1891 - March 11, 1967) was a physicist, engineer and statistician, sometimes known as the father of statistical quality control. ... William Edwards Deming (October 14, 1900 - December 20, 1993) was an American statistician, college professor, author, and lecturer widely credited with improving production in the United States during World War II. However, Deming is perhaps best known for his work in Japan; where from 1950 onward he taught top management... Combatants Major Allied powers: United Kingdom Soviet Union United States Republic of China and others Major Axis powers: Nazi Germany Italy Japan and others Commanders Winston Churchill Joseph Stalin Franklin Roosevelt Harry Truman Chiang Kai-Shek Adolf Hitler Benito Mussolini Hideki Tojo Casualties Military dead: 17,000,000 Civilian dead... The normal distribution, also called Gaussian distribution (although Gauss was not the first to work with it), is an extremely important probability distribution in many fields. ... Probability density function of Gaussian distribution (bell curve). ... The graph of the probability density function of the normal distribution is sometimes called the bell curve or the bell-shaped curve; see normal distribution. ... Three different views of Brownian motion, with 32 steps, 256 steps, and 2048 steps denoted by progressively lighter colors. ...


General

Classical Quality control was achieved by observing important properties of the finished product and accept/reject the finished product. As opposed to this technique, statistical process control uses statistical tools to observe the performance of the production line to predict significant deviations that may result in rejected products. In engineering and manufacturing, quality control and quality engineering are involved in developing systems to ensure products or services are designed and produced to meet or exceed customer requirements. ... For Wikipedia statistics, see m:Statistics Statistics is the science and practice of developing human knowledge through the use of empirical data expressed in quantitative form. ...


The underlying assumption in the SPC method is that any production process will produce products whose properties vary slightly from their designed values, even when the production line is running normally, and these variances can be analyzed statistically to control the process. For example, a breakfast cereal packaging line may be designed to fill each cereal box with 500 grams of product, but some boxes will have slightly more than 500 grams, and some will have slightly less, producing a distribution of net weights. If the production process itself changes (for example, the machines doing the manufacture begin to wear) this distribution can shift or spread out. For example, as its cams and pulleys wear out, the cereal filling machine may start putting more cereal into each box than it was designed to. If this change is allowed to continue unchecked, product may be produced that fall outside the tolerances of the manufacturer or consumer, causing product to be rejected. In mathematics and statistics, a probability distribution, more properly called a probability density, assigns to every interval of the real numbers a probability, so that the probability axioms are satisfied. ... The cross of the war memorial and a menorah for Hanukkah coexist in Oxford. ...


By using statistical tools, the operator of the production line can discover that a significant change has been made to the production line, by wear and tear or other means, and correct the problem - or even stop production - before producing product outside specifications. An example of such a statistical tool would be the Shewhart control chart, and the operator in the aforementioned example plotting the net weight in the Shewhart chart. The control chart, also known as the Shewhart chart or process-behaviour chart is a statistical tool intended to assess the nature of variation in a process and to facilitate forecasting and management. ...


CSP SPC Control


See also

Total Quality Management (TQM) is a management strategy aimed at embedding awareness of quality in all organizational processes. ... The introduction to this article provides insufficient context for those unfamiliar with the subject matter. ... The control chart, also known as the Shewhart chart or process-behaviour chart is a statistical tool intended to assess the nature of variation in a process and to facilitate forecasting and management. ... The Process Capability Study answers the question, is my process good enough? This is quite different from the question answered by a Control chart, which is, has my process changed? Properly, use of a Control Chart to establish that a process is stable and predictable precedes the use of a... There are a number of software programs designed to aid in statistical process control (SPC). ... Corrective and Preventive Action or Corrective and Preventative Action (CAPA) is a concept within Good Manufacturing Practice (GMP). ...

Bibliography

  • Deming, W E (1975) On probability as a basis for action, The American Statistician, 29(4), pp146-152
  • Deming, W E (1982) Out of the Crisis: Quality, Productivity and Competitive Position ISBN 0-521-30553-5
  • Oakland, J (2002) Statistical Process Control ISBN 0-7506-5766-9
  • Shewhart, W A (1931) Economic Control of Quality of Manufactured Product ISBN 73890760
  • Shewhart, W A (1939) Statistical Method from the Viewpoint of Quality Control ISBN 0-486-65232-7
  • Wheeler, D J (2000) Normality and the Process-Behaviour Chart ISBN 0-945320-56-6
  • Wheeler, D J & Chambers, D S (1992) Understanding Statistical Process Control ISBN 0-945320-13-2
  • Wheeler, Donald J. (1999). Understanding Variation: The Key to Managing Chaos - 2nd Edition. SPC Press, Inc. ISBN 0-945320-53-1.

Notes

  1. ^ "Why SPC?" British Deming Association SPC Press, Inc. 1992

External links

leading, enterprise-grade, fully automatic process evaluation software can increase your yields better than ANY others]

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STATISTICAL PROCESS CONTROL (4166 words)
SPC techniques need not be restricted to the present, i.e., planning for the insertion of new technology later in the life cycle should also plan for the use of SPC to ensure that processes are controlled and reliability of the resulting software artifacts is optimized.
Statistical Process Control (SPC) is used to identify and remove variations in processes that exceed the variation to be expected from natural causes.
When the process is stabilized within acceptable limits, the project’s defined software process, the associated measurements, and the acceptable limits for the measurements are established as a baseline and used to control process performance quantitatively.” [Paulk, et.
statistical process control (271 words)
According to Shewhart, acknowledged father of control charts: "A phenomenon will be said to be controlled, when through the use of past experience, we can predict, at least within limits, how the phenomenon may be expected to behave in the future." Statistical process control (spc) is concerned with operating on target with minimum variance.
In statistical process control (spc) there are a number of different control charts each of which performs best for a particular kind of data.
Design of experiments can often speed the improvement or optimization process in major steps while statistical process control (spc) and control charts assure that the gains that have be made are not lost while continuing incremental improvement.
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