Let’s try to understand Process Variation using below examples. Take two shooters Joe and Max and understand their shooting. Joe on average is dead on whereas Max on average is way off, as shown below.
Who is more likely to be consistently on target over the long haul? How do we improve their shooting?
If Joe’s first shot were high, what might you be tempted to do with his site? Adjust it down, right?
But if his next shot would have been below the target due to his natural shooting variation, look at what would happen if you had adjusted the sight (see next picture).
The amount of process adjustment actually moves the second shot an equal amount further from where it would have been without the adjustment. This harmful and unnecessary adjusting is called tampering.
Lets take another example of two bowlers, Jane and Pat and see their bowling scores in the below picture. If X is the bowling score, then X (“x-bar”) is the average score.
Although Jane’s average score (x-bar) of 140 is lower than Pat’s, she is at least more consistent. When there is a lack of consistency between measured responses, there is less certainty (i.e., more risk) about what you can expect over time.
In this example lets take a chef who works in his kitchen all day. On average, is the chef comfortable with the temperature?
If X is temperature, what is average temperature?
Avg(x) = (130 + 10) / 2 = 70
So the average doesn’t explain his discomfort. What would be a better measure?
The range, or difference between the largest and smallest values would be a better measure, indicating the amount of variation:
R = 130 – 10 = 120
That large of amount of variation explains why he is so uncomfortable.