The point about variability is that when you take a set of measurements will always get a bell-shaped, standard distribution curve. Any group of measurements exhibits variability which can be described in statistical expressions. Because of this we are able use statistical methods to monitor and control quality processes. We take samples from process and measure them, plotting and interpreting the resulting chart. This step allows seeing whether the process is under control or not. It provides indications about how problem can be fixed. Although statistical process control is frequently applied to production processes it can also be applied to other processes. The main thing is to measure the critical variables in the process and then to monitor them for signs of future troubles.
Proper tracking is done by a process control chart or graph. The graph shows how the output varies over time. The graph tells you whether the variability in process output is due to normal variation or whether there are any special causes responsible. To improve that process you should eliminate the special causes first, so that the variations are only due to the usual random factors.
Once the complete process is under control and the special causes have been eliminated, you can only make additional improvements by improving the whole process. The improvement target will either be to decrease the variation or to decrease the total value of the factor being measured. For a production process the target will be to tighten up on the tolerances so no defects are produced. These statistical process controls will help you to increase total quality management. Quality can be increased by other process controls too.
I hope in this article you will get some useful information about how to improve quality.
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Published by Nick Mutt
Nick Mutt is founder and CEO of Elite Informatics, an Internet Advertising Company. He is also an active writer and blogger on health related topics. View profile
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