Across the many training sessions conducted one question that keeps raging on is “How do we deal with special causes of variation or assignable causes”. Although theoretically a lot of trainers have found a way of answering this situation, in the real world and especially in Six Sigma projects this is often an open deal. Through this article, I try to address this from a practical paradigm.

Any data you see on any of your charts will have a cause associated with it. Try telling me that the points which make your X MR, IMR or XBar R Charts have dropped the sky and I will tell you that you are not shooting down the right ducks. Then, the following causes seem possible for any data point to appear on the list.

  • A new operator was running the process at the time.
  • The raw material was near the edge of its specification.
  • There was a long time since the last equipment maintenance.
  • The equipment maintenance was just performed prior to the processing.

 The moment any of our data points appear due to some of the causes mentioned below, a slew of steps are triggered. Yeah – Panic! Worse still, these actions below which may have been a result of a mindless brain haemorrhage backed by absolute lack of data, results in more panic!

  • Operators get retraining.
  • Incoming material specifications are tightened.
  • Maintenance schedules change.
  • New procedures are written.

My question is --- Do you really have to do all of this, if you have determined that the cause is a common or a special cause of variation! Most Six Sigma trainers will tell you that a Control chart will help you identify special cause of variation. True – But did you know of a way you could validate your finding!

  1. Check the distribution first. If the data is not normal, transform the data to make it reasonably normal. See if it still has extreme points. Compare both the charts before and after transformation. If they are the same, you can be more or less sure it has common causes of variation.
  2. Plot all of the data, with the event on a control chart.  If the point does not exceed the control limits, it is probably a common-cause event.  Use the transformed data if used in step 1.
  3. Using a probability plot, estimate the probability of receiving the extreme value.  Consider the probability plot confidence intervals to be like a confidence interval of the data by examining the vertical uncertainty in the plot at the extreme value.   If the lower confidence boundary is within the 99% range, the point may be a common-cause event.  If the lower CI bound is well outside of the 99% range, it may be a special cause.  Of course the same concept works for lower extreme values.
  4. Finally, turn back the pages of the history. See how frequently these causes have occurred. If they have occurred rather frequently, you may want to think these are common causes of variation. Why – Did you forget special causes don’t really repeat themselves?

 The four step approach you have taken may still not be enough for you to conclude if it is a common or a special cause of variation. Note – Any RCA approach may not be good enough to reduce or eliminate common causes. They only work with special causes in the truest sense.

So, what does that leave us with! A simple lesson that an RCA activity has to be conducted when you think even with a certain degree of probability that it could be a special cause of variation. To ascertain that if the cause genuinely was a Special cause all you got to do is look back into the history and see if these causes repeated. If they did, I don’t think you would even be tempted to think it to be a special cause of variation.

Remember one thing – While eliminating special causes is considered goal one for most Six Sigma projects, reducing common causes is another story you’d have to consider. The biggest benefit of dealing with common causes is that you can even deal with them in the long run, provided they are able to keep the process controlled and oh yes, the common causes don’t result in effects.


Merely by looking at a chart, I don’t think I have ever been able to say if the point has a Special cause attached to it or not. Yes – This even applies to a Control chart which is by far considered to be the best Special cause identification tool. The best way out is a diligently applied RCA and a simple act of going back and checking if the cause repeated or not.

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