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.
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.
["Why SPC?" British Deming Association SPC Press, Inc. 1992]Classical
Quality control was achieved by observing important properties of the finished product and accept/reject the finished product. As opposed to this
statistical process control uses
statistical tools to observe the performance of the production line to predict significant deviations that may result in reject products.
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.
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.
*
Total Quality Management*
Six Sigma*
Control charts
*
Process Capability*
Statistical Process Control Software*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 0521305535
*Oakland, J (2002)
Statistical Process Control ISBN 0750657669
*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 0486652327
*Wheeler, D J (2000)
Normality and the Process-Behaviour Chart ISBN 0945320566
*Wheeler, D J & Chambers, D S (1992)
Understanding Statistical Process Control ISBN 0945320132
*Wheeler, Donald J. (1999).
Understanding Variation: The Key to Managing Chaos - 2nd Edition. SPC Press, Inc. ISBN 0945320531.
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Statistical Process Control *
Two Types of SPC*
The Six Sigma Zone*
SPC: Process and Quality Views*
Failure Analysis: Reducing Your Downtime*
Statistical Process Control at Six Sigma First Website*
Ten Tricks to 'get' SPC - a beginner's tutorial*
SPC in the Semiconductor Industry - www.SiliconFarEast.com, good overview but has some mistakes in the details which are explained correctly e.g. in Wheeler, D J & Chambers, D S (1992)
Understanding Statistical Process ControlIt might be useful to refer to an older version when expanding the article.
