| 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. GE Aircraft Engines operates at Nine Sigma
levels of quality. It continues to spread to service companies as well. In 2000, Fort Wayne, Indiana
became the first city to implement the program in a city government.
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. (See below for those assumptions.)
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.
It is currently used in a number of large companies, such as Microsoft and Motorola to reduce the number of product defects.
Organizations such as the International Charter also include principles from it in their Business
Certification (http://www.icharter.org/certification/) programmes such as
IC9700 (http://www.icharter.org/certification/ic9700) for large companies, and to a lesser extent
IC9200 (http://www.icharter.org/certification/ic9200) which is for small businesses.
Why six?
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 things. 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 [1] (http://www.rbsc.com/pages/sixsig.html).) 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.
The 1.5 sigma shift is very problematic, to say the least.
Common practice is to represent a truly 4.5 sigma process as a 6 sigma process. This is the reverse of what you would expect,
if you were "derating" your sigma number, to account for unobserved, but expected variation. If you were doing that, you would
represent a 4.5 sigma process as a 3 sigma process, not a 6 sigma process.
It has been suggested that one of the early practitioners of six sigma invented or adopted the 1.5 sigma shift purely for
marketing reasons. It was unrealistic to expect to reduce defect to the few parts per billion level, and he didn't want to sell a
program named "4.5 Sigma", so a 1.5 sigma shift was necessary, to get an attractive name.
However, according to original training material and a handout dated 1985 from Motorola, Six Sigma is actually a Cpk of 1.5
and a Cp of 2.0. Based on a 1200 parts/step process, and using a 3 sigma design margin, ‘fewer than 4 units out of every
100 would go through the entire manufacturing process without a defect’ and thus, we can see that for a product to be built
virtually defect-free, it must be designed to accept characteristics which are significantly more than +/- 3 sigma away from the
mean.
'A design specification width of +/- 6 Sigma and a process width of +/- 3 Sigma yields a Cp of 12/6 = 2. However, the process
mean can shift. When the process mean is shifted with respect to design mean, the Capability Index, (Cp), is adjusted with a
factor k, and becomes Cpk.' The important difference here is Design vs. Process.
Nonetheless it is the case that processes drift over time due to noise factors, and a shift of +/-1.5 standard deviations is the limit at which the shift becomes detectable
with a sample size of 4, prompting investigation of an "out of
control" process.
When many parts have to fit together, tolerances actually work in the favor of the manufacturer. It is quite possible to make
six sigma assemblies out of three sigma parts, since it is highly unlikely that all parts will simultaneously be at one extreme
of the tolerance range. Intelligently allocating variation is called "Statistical Tolerancing", and is a useful part of Design
for Six Sigma.
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.
DMAIC
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.
DMADV
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
See also:
External links
|
