Statistical process control
Statistical Process Control (SPC) is an effective method of monitoring a process through the use of control charts. By collecting readily available data from samples, variations in the process that may affect the quality of the end product or service can be detected and corrected before any waste is produced. This method has a distinct advantage over more traditional quality methods such as inspection, as resource is used for prevention rather than detection. This has clear implication for businesses, as valuable resources are prevented from being used to manufacture defective products or to provide poor services.
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. Whie Dr. Shewhart drew from pure mathematical statistical theories, he understood that 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 (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).[1]
General
The following section relates to manufacturing rather than to the service industry, although the principles of SPC can be successfully applied to either.
In mass-manufacturing, the quality of the finished article was traditionally achieved through 100% inspection of the product; accepting or rejecting each article 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 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
- ^ "Why SPC?" British Deming Association SPC Press, Inc. 1992
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.
See also
External links
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