The concepts surrounding the drive to Six Sigma quality are essentially those of statistics and probability. In simple language, these concepts boil down to, “How confident can I be that what I planned to happen actually will happen?” Basically, the concept of Six Sigma deals with measuring and improving how close we come to delivering on what we planned to do.
Anything we do varies, even if only slightly, from the plan. Since no result can exactly match our intention, we usually think in terms of ranges of acceptability for whatever we plan to do. Those ranges of acceptability (or tolerance limits) respond to the intended use of the product of our labors-the needs and expectations of the customer.
Here's an example. Consider how your tolerance limits might be structured to respond to customer expectations in these two instructions:
“Cut two medium potatoes into quarter-inch cubes.” and “Drill and tap two quarter-inch holes in carbon steel brackets.”
What would be your range of acceptability-or tolerances-for the value quarter-inch? (Hint: a 5/16” potato cube probably would be acceptable; a 5/16”threaded hole probably would not.)Another consideration in your manufacture of potato cubes and holes would be the inherent capability of the way you produce the quarter inch dimension-the capability of the process. Are you hand-slicing potatoes with a knife or are you using a special slicer with preset blades?
Are you drilling holes with a portable drill or are you using a drill press? If we measured enough completed potato cubes and holes, the capabilities of the various processes would speak to us. Their language would be distribution curves.
Distribution curves tell us not only how well our processes have done; they also tell us the probability of what our process will do next. Statisticians group those probabilities in segments of the distribution curve called standard deviations from the mean. The symbol they use for standard deviation is the lower-case Greek letter sigma.
For any process with a standard distribution (something that looks like a bell-shaped curve), the probability is 68.26% that the next value will be within one standard deviation from the mean. The probability is 95.44% that the same next value will fall within two standard deviations. The probability is 99.73% that it will be within three sigma; and 99.994% that it will be within four sigma.
If the range of acceptability, or tolerance limit, for your product is at or outside the four sigma point on the distribution curve for your process, you are virtually assured of producing acceptable material every time-provided, of course, that your process is centered and stays centered on your target value.
Unfortunately, even if you can center your process once, it will tend to drift. Experimental data show that most processes that are in control still drift about 1.5 sigma on either side of their center point over time.
This means that the real probability of a process with tolerance limits at four sigma, producing acceptable material is actually more like 98.76%, not 99.994%.
To reach near-perfect process output, the process capability curve must fit inside the tolerances such that the tolerances are at or beyond six standard deviations, or Six Sigma, on the distribution curve. That is why we call our goal Six Sigma quality.
Quality makes us strong
In the past, conventional wisdom said that high levels of quality cost more in the long run than poorer quality, raising the price you had to ask for your product and making you less competitive. Balancing quality with cost was thought to be the key to economic survival. The surprising discovery of companies which initially developed Six Sigma, or world-class, quality is that the best quality does not cost more. It actually costs less. The reason for this is something called cost-of-quality. Cost-of-quality is actually the cost of deviating from quality-paying for things like rework, scrap and warranty claims. Making things right the first time-even if it takes more effort to get to that level of performance-actually costs much less than creating then finding and fixing defects.
Note: Please see Part 2 for an example using and illustrative fable
About the Author:
Steven Bonacorsi is a Certified LSS MBB instructor and coach. He has trained hundreds of MBBs, Black Belts, Green Belts, and Project Sponsors and Executive Leaders in Lean Six Sigma DMAIC and Design for Lean Six Sigma process improvement methodologies. He has led some of the largest deployments in the world and has saved hundreds of millions with his project teams results.
The AIT Group, Inc.
Steven Bonacorsi, Vice President,
MBB
sbonacorsi@comcast.net
http://www.theaitgroup.com
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375
Date Published :
Mar 25 2009