Six Sigma is
a highly structured program for improving business processes. It was created by
Mikel Harry and Richard Schroeder at Motorola in the early 1980's. Support Six
Sigma process with mind mapping.
Six Sigma in action:
Six Sigma with mind map:
Six Sigma with Mind Pad:
Basic methodology to improve existing processes Define: out of tolerance range. Measure: key internal processes
critical to quality. Analyze:
why defects occur. Improve:
the process to stay within tolerance. Control:
the process to stay within goals.
Basic methodology of introducing new processes. Define: the process and where it
would fail to meet customer needs. Measure:
and determine if process meets customer needs. Analyze: the options to meet customer needs. Design: in changes to the process to
meet customers needs. Verify:
the changes have met customer needs
Six Sigma is
a quality management program to achieve "six sigma" levels of
quality. It was pioneered by Motorola in the mid-1980s and has spread to many
other manufacturing companies. It continues to spread to service companies as
well. In 2000,
Six Sigma
aims to have the total number of failures in quality, or customer satisfaction,
occur beyond the sixth sigma of likelihood in a normal distribution of
customers. Here sigma stands for a step of one standard deviation; designing
processes with tolerances of at least six standard deviations will, on
reasonable assumptions, yield fewer than 3.4 defects in one million.
Achievement
of six-sigma quality is defined by Motorola in terms of the number of Defects
Per Million Opportunities (DPMO).
That is, fewer than four in one million customers will have a legitimate issue
with the company's products and service.
Many people believed that six-sigma quality was impossible, and settled for
three to four sigmas. However market leaders have measurably reached six sigmas
in numerous processes.
Anyone
looking at a table of probabilities for the normal (Gaussian) distribution will
wonder what six-sigma has to do with 3.4 defects per million thingies. Only one
billionth of the normal curve lies beyond six standard deviations, or two
billionths if you count both too-high and too-low values. Conversely, a mere
three sigma corresponds to just 2.6 problems in a thousand, which would seem a
good result in many businesses.
The
answer has to do with practical considerations for manufacturing processes.
(The following discussion is based loosely on the treatment by Robert V. Binder
in a discussion of whether six-sigma practices can apply to software .) Suppose
that the tolerance for some manufacturing step (perhaps the placement of a hole
into which a pin must fit) is 300 micrometres, and the standard deviation for
the process of drilling the hole is 100 micrometres. Then only about 1 part in
400 will be out of spec. But in a manufacturing process, the average value of a measurement is likely
to drift over time, and the drift can be 1.5 standard deviations in either
direction. At any time, 6.6% of the output will be off by 1.5 sigma in each
direction. Thus, when the process has drifted by 150 micrometres, 6.6% of the
product will be off by 150 + 150 or 300 micrometres, and therefore out of spec.
This is a high defect rate.
If you set the tolerance to six sigma, then a drift of 1.5 sigma
in the manufacturing process will still produce a defect only for parts that
are more than 4.5 sigma away from the average in the same direction. By the
mathematics of the normal curve, this is 3.4 defects per million.
There is another reason for six sigma: a manufactured item
probably has more than one part, and some of the parts will have to fit
together, which means that the total
error in two or more parts must be within tolerance. If each step is done to
three-sigma precision, an item with 100 parts will hardly ever be defect-free.
With six-sigma, even an object with 10,000 parts can be made defect-free 96% of
the time.
Clearly,
many things on which people rely (services, software products, etc.) are not
manufactured by machine tools to particular measurements. In these cases,
"six sigma" has nothing to do with statistical distributions, but
refers to a goal of very few defects per million, by analogy to a manufacturing
process. The usefulness of the analogy is controversial among those concerned
with quality in non-manufacturing processes.
Source: http://en.wikipedia.org/wiki/Six_Sigma