Reducing process variability is a core objective for process improvement initiatives because low variability helps you identify small changes in the process.
Here's a short example to illustrate this concept. Suppose you are measuring osmolality in your buffer solution and the values for the last 10 batch are as follows:
293, 295, 299, 297, 291, 299, 298, 292, 293, 296.
Then the osmolality of the 11th batch of buffer comes back at 301 mOsm/kg. Is this 301 result "anomalous" or "significantly different"?
It's hard to tell, right? I mean, it's the first value greater than 300, so that's something. But it is only 2 mOsm/kg greater than the highest previously observed while the measurement ranges from 291 to 299, an 8 mOsm/kg difference.
Let's try another series of measurements - this time, only 7 measurements:
295, 295, 295, 295, 295, 295, 295.
Then the measurement of the eighth batch is 297 mOsm/kg. Is this result anomalous or significantly different? The answer is yes. Here's why:
The process demonstrates no variability (within measurement error) and all of the sudden, there is a measurable difference. The 297 mOsm/kg is a distance of 2 mOsm/kg from the highest measured value. But the range is 0 (with all values measuring 295). The difference is infinitely greater than the range.
There are far more rigorous data analysis methods to better quantify the statistics comparing differences that will be discussed in the future, but you can see how variability reduction helps you detect differences sooner.
Also, remember that variability (a.k.a. standard deviation) is the denominator of the capability equation:
Reducing process variability increases process capability.
To summarize: reducing process variability helps in 2 ways:
- Deviations (or differences) in the process can be detected sooner.
- Capability of the process (a.k.a. robustness) increases.
Hitting the aforementioned two birds with the proverbial one stone (variability reduction) is a core objective of any continuous process improvement initiative. Applying the statistical tools to quantify process variability ought to be a weapon in every process engineer's arsenal.