# Statistical process control

Statistical process control

Statistical Process Control (SPC) is an effective method of monitoring a process through the use of control charts. Control charts enable the use of objective criteria for distinguishing background variation from events of significance based on statistical techniques. Much of its power lies in the ability to monitor both process center and its variation about that center. By collecting data from samples at various points within the process, variations in the process that may affect the quality of the end product or service can be detected and corrected, thus reducing waste as well as the likelihood that problems will be passed on to the customer. With its emphasis on early detection and prevention of problems, SPC has a distinct advantage over quality methods, such as inspection, that apply resources to detecting and correcting problems in the end product or service.

In addition to reducing waste, SPC can lead to a reduction in the time required to produce the product or service from end to end. This is partially due to a diminished likelihood that the final product will have to be reworked, but it may also result from using SPC data to identify bottlenecks, wait times, and other sources of delays within the process. Process cycle time reductions coupled with improvements in yield have made SPC a valuable tool from both a cost reduction and a customer satisfaction standpoint.

History

Statistical Process Control was pioneered by Walter A. Shewhart in the early 1920s. W. Edwards Deming later applied SPC methods in the United States during World War II, thereby successfully improving quality in the manufacture of munitions and other strategically important products. Deming was also instrumental in introducing SPC methods to Japanese industry after the war had ended.

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 that data from physical processes seldom produces 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 (for example, Brownian motion of particles). Dr. Shewhart concluded that while every process displays variation, some processes display controlled variation that is natural to the process (common causes of variation), while others display uncontrolled variation that is not present in the process causal system at all times (special causes of variation). ["Why SPC?" British Deming Association SPC Press, Inc. 1992]

General

The following description relates to manufacturing rather than to the service industry, although the principles of SPC can be successfully applied to either. For a description and example of how SPC applies to a service environment, refer to Roberts (2005). [Roberts, Lon (2005). SPC for Right-Brain Thinkers: Process Control for Non-Statisticians. Quality Press. Milwaukee.] SPC has also been successfully applied to detecting changes in organizational behavior with Social Network Change Detection introduced by McCulloh (2007).

In mass-manufacturing, the quality of the finished article was traditionally achieved through post-manufacuting inspection of the product; accepting or rejecting each article (or samples from a production lot) based on how well it met its design specifications. In contrast, Statistical Process Control uses statistical tools to observe the performance of the production process in order to predict significant deviations that may later result in rejected product.

Two kinds of variations occur in all manufacturing processes: both these process variations cause subsequent variations in the final product. The first are known as natural or common causes of variation and may be variations in temperature, specifications of raw materials or electrical current etc. These variations are small, and are generally near to the average value. The pattern of variation will be similar to those found in nature, and the distribution forms the bell-shaped "normal distribution curve". The second kind are known as special causes, and happen less frequently than the first.

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, in accordance with a distribution of net weights. If the production process, its inputs, or its environment changes (for example, the machines doing the manufacture begin to wear) this distribution can change. For example, as its cams and pulleys wear out, the cereal filling machine may start putting more cereal into each box than specified. If this change is allowed to continue unchecked, more and more product will be produced that fall outside the tolerances of the manufacturer or consumer, resulting in waste. While in this case, the waste is in the form of "free" product for the consumer, typically waste consists of rework or scrap.

By observing at the right time what happened in the process that led to a change, the quality engineer or any member of the team responsible for the production line can troubleshoot the root cause of the variation that has crept in to the process and correct the problem.

SPC indicates when an action should be taken in a process, but it also indicates when NO action should be taken. An example is a person who would like to maintain a constant body weight and takes weight measurements weekly. A person who does not understand SPC concepts might start dieting every time his or her weight increased, or eat more every time his or her weight decreased. This type of action could be harmful and possibly generate even more variation in body weight. SPC would account for normal weight variation and better indicate when the person is in fact gaining or losing weight.

References

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 0-87389-076-0
*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.

*Process control
*Process capability
*Quality assurance
*Quality control
*ANOVA Gauge R&R
*Sampling (statistics)
* Electronic design automation
* Reliability engineering
* Six sigma

* [http://statistical-process-control.org/ Statistical Process Control (SPC): the Founders' Way]
* [http://www.managers-net.com/statistical_process_control.html Statistical Process Control]
* [http://deming.eng.clemson.edu/pub/den/files/2spcs.txt Two Types of SPC]
* [http://reliability.sandia.gov/Manuf_Statistics/Statistical_Process_Control/statistical_process_control.html Manufacturing Systems - Statistical Process Control]

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