Process stability is one of the most important concepts of the Six Sigma methodology, or any quality improvement methodology for that matter. Stability involves achieving consistent and, ultimately, higher process yields through the application of an improvement methodology. Does a process need to be stable in order for a black belt to improve it? Try educating your child when he/she is having fits of joy and emotional distress at the same time (as many youngsters do!).

To help your understand why process stability (also known as variation reduction) is important, here are a few illustrative concepts:

• Building widgets to be within customer specifications or tolerances: Some variation is inherent in the process, but the variation may not be wider than the specifications allow, otherwise the widget may not fit or function properly.
• Mailing welcome packages after signing up for an online trading account: Customers expect time delay in receiving welcome materials due to processing, printing, the postal service, etc. But customers have specified that they expect receipt within 10 business days — anything over indicates the process is unstable and the variation is too large. As an aside, anything over may also cause additional customer telephone calls to the processing center, which can add to customer service costs.
• Changing the oil in your car is important every 3000 miles (or now even longer): Adding too much or too little oil may cause malfunctioning of your engine under certain circumstances. Again, variation is inherent, but the specifications of high or low oil must be met.A loyal reader started a fabulous discussion on process stability (thanks Kim!), and I thought it would be useful to present some of the finer points. Below are a few posts from fellow quality professionals discussing how best to link quality to finances.

“Montgomery states in his book: Montgomery, Douglas. C. ‘Introduction to Statistical Quality Control’. Wiley & Sons, Inc. New York. 2001. 4th ed. Pg 372, that in 1991 the Automotive Industry Action Group (AIAG) was formed with one of their objectives being to standardize industry reporting requirements. He says that they recommend Cpk when the process is in control and Ppk when it isn’t. Montgomery goes on to get really personal and emotional about this which is unique to this page of this book and other books I have of his. He thinks Ppk is baloney as he states ‘Ppk is actually more than a step backwards. They are a waste of engineering and management effort – they tell you nothing

“While Montgomery gets frustrated over the use of Ppk, he does a poor job of explaining what a stable process is. I respect his works just the same as he alone has even attempted to try to explain the difference between a stable process and a non-stable one.

“This argument as well as numerous others that you can find on this site regarding use of Cpk / Ppk metrics, the validity of Six Sigma shifts, process capability, SPC, etc. all reflect our lack of definition for what is process control.

“So, how can we define process stability and or process control? Perhaps we can agree on some given amount of process shifting (1.5 sigma)? Perhaps we can agree that a stable process is that where it’s Cpk values are above 1.67? Perhaps some combination of these or other events needs to take place such as three consecutive Cpk samples over 1.67, etc.

“Until we can define what a stable process is, we are doomed to argue forever all use of any statistical metric. For the love of a all science, please help!”

Posted by: Ken K.

“A stable process in simple terms is a process of which all the causes of variations are known and are acted upon and the process is then governed by common causes of variations, where the output of the process is fairly predictable. management decision requires to further increase the capability of the process.”

Posted by: Samir Mistry

“I think that a process can be considered stable if- given the level of significance- the subgroup points are randomly distributed within control limits.

“Example: for a level of significance of 5% it is possible that in long run, one point out of 20, in average, may go out of limits without us jumping to conclusion that process is OOC, unless the randomness’ hypothesis was violated (previous points show some pattern).

“The Capability Coefficients are statistics that measure short- or long term capability. I think that if these two measures of capability are not significantly different for a certain period of time, then we can conclude that the process was also stable during that period of time.”

Posted by: Dragos Bandur

“So where are we now? The problem as I now see it with your help is that we are looking at a third order problem with first and second order thinking. There are obviously many different ways to convince many different people of what process stability is depending upon many different types of situations.

“We either need a universally acceptable solution supported by respectable organizations or some other more obvious third order solution (via breakthrough) that fits all types of situations. For example, ASQ and ASA could state that they support AIAG’s definition of process stability with one of ‘within 9 sigma regardless of distribution shape and or process mean shifting’. One third-order example could be some very descriptive formula that allows anyone who reads it to fully understand the process being described in comparison to any other process. For example, the phase xx process being measured with xx capability is xx% stable over xx time given xx number and xx types of distribution assumptions.

“Any other ideas?”

Posted by: Kim Niles

“And what about the usual tests of process stability? Several types of conditions exist that indicate that a process is out of control. We’ve all become familiar with the following control chart tests; answer ‘yes’ to any of them and your process is unstable:

• Are points on the control chart outside of the upper and lower limits?
• Do two out of three points land close to the upper and lower limits?
• Are 4 of 5 points above or below one sigma and the control limits?
• Are 8 points above or below the center line and the control limits?
• Do seven or more points trend in the same direction?
• Do all points hug the center line?”