Statistical process control

Statistical Process Control (SPC) is a method of visually monitoring manufacturing processes. With the use of control charts and collecting few but frequent samples, this method can effectively detect changes in the process that may affect its quality. Under the assumption that a manufactured product has variation and this variation is affected by several process parameters, when SPC is applied to "control" each parameter the final result tends to be a more controlled product. SPC can be very cost efficient, as it usually requires collection and charting data already available, while "product control" requires accepting, rejecting, reworking and scrapping products that already went through the whole process.



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. 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]


General
Classical quality control was achieved by inspecting 100% of the finished product and accepting or rejecting each item based on how well the item met specifications. In contrast, statistical process control uses statistical tools to observe the performance of the production line to predict significant deviations that may result in rejected products.

The underlying assumption is that there is variability in any production process: The process produces 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, 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.


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