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).
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).
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Last Updated: Sep 2009
Last Updated: Sep 2009
What is Statistical Process Control SPC?
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.
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.
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.
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.
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.
