6 Sigma Quality Improvement
Concepts
What is Six Sigma
Quality Improvement Concepts, and why is it a hot management topic?
Six Sigma Quality Concept is a
movement that inherits directly from TQM, or Total Quality Management. It uses much the same toolset and the same
concepts. Six Sigma Quality has two new emphases which are its distinguishing characteristics:
1) Six Sigma Black Belts 
welltrained experts in quality, process improvement, and statistical process control  who work within companies
as "problemsolvers for hire". They lead process improvement projects, and focus on areas which will have the
highest impact on the bottom line.
2) A focus on reducing variation to
very low levels.
Jack Welch, the energetic chairman
of GE, has been Six Sigma's most influential advocate. Other companies, notably Motorola and Allied Signal, have
been incubators and proponents of the movement. Mikel Harry is its most colorful champion. The consulting and
training firm he founded, Six Sigma Academy, has, become the most wellknown educator of Black Belts. Many other
traditional quality consultancies have been quick to follow suit, including Six Sigma Qualtec, the Juran Institute,
and Oriel.
The name  Six Sigma  wants some
explaining. Imaging that you are weighing bags of potatoes as they come out of a bagging process. The bags are
supposed to weigh 10 pounds, but the actual weights will vary. If they are overweight, you are giving away
potatoes. If they are underweight, you are ripping people off. So...you record the weights, and use some software
to construct a histogram of the distribution. You would hope that the distribution would be centered on 10 lbs.,
and that there wouldn't be long tails on either side. If your specification calls for all bags to exceed 9.5 lbs.,
and to be less than 10.5 lbs., you can draw these spec limits on the histogram.
So... you're measuring along, and
plotting the histogram, and you come across a bag that weighs 9.4 lbs. It is out of spec. It may cause trouble with
customers if you ship it. What do you do? How many out of spec bags do you expect to find if you measure 1000 bags?
You need some way of predicting this. That's where statistics come in. You can find the average, or mean weight of
a bag of potatoes from all the ones you've weighed. You can calculate a standard deviation, too, which gives you an
idea of how much variation there is around the mean. If the standard deviation is high, that means you have a lot
of variation in the process. Here's where the sigma comes in...the Greek letter sigma is usually used to symbolize
the standard deviation in statistical equations.
With enough data, you can try to fit
a curve to your data  drawing the line that best approximates the mathematical function that really describes what
is going on in the process. The art of curvefitting is an arcane one, and not one we need to go into here. Let's
just take a normal curve as an example of one that we might decide to use, if we went through a curvefitting
exercise. If our histogram can be wellenough described by a normal distribution, then 68% of the bags we measure
will weigh within one standard deviation, or one sigma, of the mean value. If the mean is 10 lbs., and the standard
deviation is 0.2 lbs., then 68% of the bags would weigh between 9.8 lbs. and 10.2 lbs.
Again, if this is a normal
distribution, we would find that 95.5% of the bags weighed within 2 sigmas, or 0.4 lbs. of the mean. If we look at
the mean plus or minus 3 sigmas, or 10 +/ (3*0.2), we would find that 99.7% of all bags would weigh between 9.4
lbs, and 10.6 lbs. A very few, just 0.3% of all bags, would weigh less than 9.4 lbs or more than 10.6
lbs.
If this is the case, though, our
process is wider than our specification limits. Some of the bags weigh less than 9.5 lbs, and some weigh more than
10.5 lbs. Does that matter? It depends. With potato bags, maybe not. But what if we were measuring something with
fine tolerances, or something expensive, or something where precise mixtures were critical. We wouldn't want to be
finding instances where our process was not producing outputs that not in spec limits.
The Six Sigma Quality movement takes
this very much to heart. In fact, six sigma advocates believe that for many processes, there should be six sigmas
between the mean and the specification limits, so that the process is only making a few bad "parts" in every
million. You can, of course, do that by relaxing the specifications, but that isn't usually the way to please
customers. Instead, the variation in the process needs to be driven towards zero, so that the histogram gets
narrower, and fits more comfortably inside the spec limits.
Clearly, to get an accurate view of
your critical processes, you need to have people who understand variation and statistics. The Black Belt training
spends a lot of time on this. Software tools, such as control charts and histograms, are harnessed. So, too, are
the tools of quality improvement, teamwork, project management, and creative thinking. Root causes of variation are
explored, and the classic Deming PDCA cyle is used to plan improvements, try them, check to see if they worked, and
standardize on them if they did.
We don't see that a lot of the Six Sigma methodology is new. It combines elements of statistical quality control,
breakthrough thinking, and management science  all valuable, powerful disciplines. We are happy that in this new
movement, the timetested tools of quality and process improvement are getting renewed highprofile attention and
achieving excellent results.
