I first got into control charts back in the early eighties, and we used them to help keep track of errors in hardware and software testing using multiple products – all of them on test at the same time.
Don’t believe for a minute that they are only of use in manufacturing.
Control charts are used to measure the results of a particular process over a period of time and plot the results as a graph.
They are a great way to measure variances within a particular process to determine if this process is either in control, or out of control.
If a process is statistically in control, then it does not need to be corrected. If it is out of control then this shows that variations are sufficient evidence to show that it needs to be brought back into control by taking some form of corrective action.
Control charts use standard deviation to measure the variances.
Quality control says that the processes in control (that is, the measurements fall within the control limits), If the system or process is performing within plus or minus three standard deviations.
Statistically, this means that 99.73 percent of the parts going through the process, fall within an acceptable range of the mean.
The ‘mean’ of the data points is represented by a straight line that is drawn through the average of all the data points of the chart.
If a part falls outside this range, then it should be investigated to determine if some form of corrective action is needed. Let’s have a look and one at a picture of control charts:
Note that a control chart can show not just plus or minus three standard deviation points, but also an upper and lower control limit -giving you a form of tolerance within which to control the process.
As shown on my diagram, there is a useful called the rule of seven. This states that if seventh or more consecutive data points fall on one side of the mean, then they should be investigated even if such data points all fall within the control limits
Let me remind you, although these are traditionally used in manufacturing, they can be used to monitor any output, in my case above, I was using it to determine variances from functionality when testing up to 100 products simultaneously.
Within PMBOK, you could use this technique to plot schedule variances, cost variances, or even the frequency or number of scope changes. Another use could be to use the upper and lower limits of a customer’s specification, within which the control chart would indicate that they are acceptable to the customers quality specifications.
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David spent 25 years as a senior project manager for US multinationals and now develops a wide range of project-related downloadable video training products under the Primer brand. In addition, David runs training seminars across the world, and is a prolific writer on the many topics of project management. He currently lives in Spain with his wife Jude.