6 Sigma for Simpletons
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 I am a big fan of libraries. They are a fantastic resource for
people interested in Continuous Improvement.
I tend to always find myself in the non fiction section.
I don't know if you have come across the "Dummy" series of books. I think they are great way to
get some essential knowledge on a topic quickly.
There is even a book "6 Sigma for Dummies". Anyway I was at that
stage where I put down my books on the counter. You know
that stage where the librarian is secretly assessing what type of
person you are based on your book choices. I'm sure assistants at
supermarkets do the same thing.
Anyway as I put lay down my five Dummy books on the counter there was an embarrassing silence
as the librarian scanned them in. I had to speak.
"I promise you I am not really a Dummy." I said.
It was funny in my own brain.
The librarian smiled with a knowing look that only librarians can
have.
Anyway that is what prompted the title of this article. 6 Sigma
for Simpletons. I am not suggesting that you are a simpleton. Of
course there is a chance you may be, but because you have signed up
for this very informative, entertaining and modest newsletter then I
think the chances of you not being a simpleton are 99.99966%.
See what I did there. I nearly insulted you to get straight into
the guts of what six sigma is all about.
Where did the concept of 6 Sigma get popularised and so what?
Six Sigma really came to life as a business improvement strategy at
the Motorola Corporation in the USA in 1981. To cut a long story
short Motorola did the following.
They reviewed all of their product defects. They then picked the
defect that was the biggest problem. They then looked at the process involved in creating the
product.
Their goal was to try and eliminate the defect by reducing the
variation in the process and all of the potential reasons for the
defect. The idea was to bring the defect level down to 3.4 defects
per million. Another way of saying this is that 99.99966% of all
of the products produced are free of the defect.Wow! .Why 3.4 part
per million I hear you cry. 3.4 parts per million represents a 6
Sigma process.
Phew..
So when you hear people talking about 6 Sigma, who do not really
have a clue what it is, (and there are a lot of them about) you now
know more than them.
So if a process is 6 Sigma then it only produces 3.4 defects per 1,000,000 transactions.
Most organisations only have a 3 sigma process of 67,000 defects per million transactions.
(Stated even more simply this represents a 6.7% defect rate)
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Get Graham Ross and
Barry Jeffrey's handy "wee" book :
Tools for Success
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In a very portable and accessible format 'Tools for Success' places the most useful
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explanations of how and when to use them. And the style is also motivational into the
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Giles
Nightingale
I purchased this book a few
weeks ago and thought I would add some feedback. Personally I found it a great help
and guide in me being able to implement some lean tools and also understanding the
tools on a workforce that has never been exposed to this way of thinking and working.
One of the great things about this book is it does not go on and on about each tool
everything is explained in simple terms that are easy to understand and simple
examples are used to relate to each tool. If you are going to change the culture and
mindset of people they do not want to be blown away by all sorts of analytical
information in the early stages of implementing lean practices and this is where this
book excels. As a former employee of Toyota who worked on the coal face so to speak
it is also an excellent reminder/prompt to keep you on the right track. Its well
worth £8.50 of anyone's money.
Lee Cottam
Tools for
Success
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Let's go a bit deeper but not too deep
So, if you didn't know before, hopefully you now have got the idea that a 6 sigma process is one
that is pretty good!
In the next little section I am going to attempt, without the aid of a safety net, to explain to you the
mechanics of six sigma in really simple terms, without you having to becoming a Master Black Belt, own a
program called Mini Tab, and bore people at cocktail parties. Do people still have cocktail parties?
Everyone knows what an average is don't they. Of course they do. Remember when you were at school and you
measured everyone's height in the class .You added up all the heights and then divided the total by the number
of children in the class. That was the averageWell in fancy pants statistical engineering we call the average
the mean.
Why? I don't know. It is a shorter word. It rhymes with Lean? There is actually a reason but I will not go into
it for the fear of making you sleep.
So let's go back to the classroom and think about all of the children that were measured. There are a few
really tall children. There are few really really small children. Then there are a lot of children round about
the same height.
That is what we call a normal distribution. If we were to plot out all of the heights on a bit of graph paper
it would look a bit like a bell shaped curve.
The mean value would be in the middle of the bell shaped curve.(The diagram next door is not an actual bell
shaped curve, but it does have a similar type of shape.
So I hope you get the idea,and can imagine plotting the heights of the children)
You will need to sit up straight, rub your eyes and concentrate for the next bit.
There is an exam at the end.
It could affect your promotion prospects.
So please pay attention and take notes because I'm going to go through the next bit quite quickly, and I will
not be going over it again.
The Standard Deviation or Sigma (as in six sigma)
I'm not going to go into how to calculate the standard deviation. It is basically the mean of a mean
It shows how much variation there is from the "average". A low standard deviation indicates that the data
points tend to be very close to the average.
A high standard deviation tends to indicate that the data points have a bigger variation from the mean.
For instance say we gave 20 random car drivers the chance to race a formula one car around a race track. I
suspect the lap times would vary quite a bit.
If we then asked the top 20 formula one drivers in the world to go around the same track then the variation in
lap times would be very small. i.e. the standard deviation would be very small.
Going back to our classroom example, let's say that the mean height of the pupils was 140cms, with a standard
deviation of around 8 cm.
A fair number of pupils, about 68%, will have a height of between 132cm - 148cm.That is one standard deviation
from the mean.
The majority of pupils, 95% will have a height that falls within two standard deviations (16cm) or two sigma
from the mean. i.e. 124cm - 156cm.
Hope fully you are getting the idea...
So for a process to be 6 sigma 99.999976 % of the population need to fall within six standard deviations from
the mean!
So when we strive for a "6 Sigma" process we are looking to drive down the standard deviation or sigma. How we
do this is a topic for another day.
If you didn't before, I hope you now understand what "6 Sigma" is all about and have found my explanation
useful.
Hope this helps
Best regards,
Graham
Ross
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