Control charts for beginners

I am going to attempt in this blog to keep the topic of control charts very simple.

In my view the very definitions of control charts can go a long way in determining ones approach to them. In my view

A complicated control chart is “A run chart with confidence intervals calculated and drawn in.  These Statistical control limits form the trip wires which enable us to determine when a process characteristic is operating under the influence of a special cause”.

When I mean by a simple definition is that, a control chart is a set of data points plotted chronologically in time sequence with five horizontal lines drawn on the chart; i.e.,

  • A centre line, drawn at the process mean
  • An upper control-limit (also called an upper natural process-limit drawn three standard deviations above the centre line;
  • A lower control-limit (also called a lower natural process-limit drawn three standard deviations below the centre line.

The above definition if for a X bar control chart or also called as a Shewart control chart (named after its inventor). The whole idea of such a chart using 3 sigma control limits is that data points will fall inside 3-sigma limits 99.7% of the time when a process is in control. Thus one could say that anything beyond the control limits requires investigation. So how do you draw one?

Let us assume you test weekly builds. And you have to finish by Friday EOD what you get on Monday. But you notice that sometimes you are late by 2 days. Sometimes early by a day. Let us say you have 30 weeks of data. How can you know how was your process variation. Every process varies. Thus you may say it is alright to be one day late or one day early. But mathematically, how can you say should it be one day or two days? And more importantly was there a week where there was something abnormal in your process which requires more investigation. This is where a control chart can help.

Click on the link above to see an example and know how to use excel without any special add on to draw a control chart.

sample_control_chart

From the chart you shall notice that there are two data points (two weeks) which are beyond the control limits and that is what you shall investigate further:

As you plot the chart for further weeks, your process is stable if all data points are within the control limits. Your process is capable if you are able to reduce the control limits (variation). As per the first 30 weeks data, you have such a poor variation that you may be 15 days late or early. Thus seems ridiculous. So you can draw the control chart again, removing the special causes (the two weeks) and find out how capable is your process and whether with time the variation is reducing.

This was just a precursor. Read more about types of control charts, common and special causes of variation. The internet is full of examples on the same. Apply to other metrics than schedule slippage to bring your testing process under control.

Krishna Iyer | CEO | Zen Test Labs

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