SPC developed in 1920s in Bell Laboratories by Sir Walter Shewart is the best known amalgamation of statistics and engineering. Control Charts are most widely used method of understanding process behaviour with or without the customer specification.
They help distinguish between specific, identifiable causes or conditions that are directly responsible for process shifts (special or assignable-cause variation) and the collection of conditions inherent in any process that, taken together, result in cyclic variation of output around the process mean common-cause or chance variation.) A control chart consists of a center line and statistically determined upper and lower control limit lines against which time-sequenced data points are plotted.
Plotting data on a run chart could reveal trends that may otherwise hide in what looks like normal variation. But run charts cannot tell you the capabilities of a process or whether it is statistically in control.
To do that you need a control chart. If all the values plotted on a control chart are within the
control limit lines and there are no trends or tendencies that indicate otherwise, the process is
considered in control. In control means that only common-cause or chance variation is affecting
the process. If a process is in control, it is possible to predict what specification limits can continually
be met without improving the process.
The strategies for attacking assignable-cause variation and for reducing common-cause variation
are different. Control charts can help distinguish assignable-cause from common-cause variation and, so, can help you determine which strategy to use.
Control charts are especially valuable in the control phase of Six Sigma methodology.
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