The entire concept of 6-σ (Six Sigma) is based on
satisfying customers. Although the methodology is said to have been first
proposed by Bill Smith of Motorola company (Wiki, 2006), statisticians have
been aware of this concept long ago. Smith only was the first to apply it in
production processes in 80's. Since then, the methodology has been used in
many organizations and had saved millions (Wiki, 2006).
The principle revolves around putting the stress on measuring and harnessing
dispersion in the production process (opposed to stressing the importance of
mean average) since dispersion is the determinant of the number of defective
outcomes. (I would not delve into statistical aspect since it is irrelevant
to the scope of this paper, but I will have to mention some though.) Thus,
the methodology is aimed at increasing the number of standard deviations
(generally marked σ) so that 6 these standard deviations fall within the
acceptable limits from each side of target mean. In other words, supporters
of the methodology strive to mold the production process in such a way that
over 99.99% of the outcomes are within the acceptable limits, or to be more
precise, 3.4 defects per million opportunities (Saxena, 2006). This, in turn
can be achieved by either of the two options. First, which is very costly
but beneficial in long term, is to control the production process so that
the sample of many output units (products or services) forms a much higher
and thinner bell-shaped curve, this way naturally decreasing dispersion and
concentrating most outcomes near the mean. The other option, which is the
target of this paper, is to shift the planned specifications or acceptable
limits as well as the target mean so that more outcomes are within the
limits. (This technique is surely much more complex than what is covered in
this paper.)
From the point of view of the producer - the one who applies 6-σ - the
methodology is rather beneficial, because it reduces the number of defects
and makes the operations more stable. However, from the point of view of the
consumer, this might not be as good as it seems, although 6-σ is aimed at
customer satisfaction. The problem, however, is that for customers the
target expected outcome is satisfied in fewer number of times because the
producer is oriented at reducing variance by shifting the entire production.
So, even though vast majority of outcomes are within the specified customer
requirements, the best desired outcome occurs significantly fewer times.
The ethical problem is that customers still want the best desired
product/service more than simply satisfactory. And so, instead of simply
maintaining the production at a certain level and focusing on quality and
the target mean, the producers shift the target mean but put more stress on
keeping production within acceptable consumer specifications. Therefore, the
price the producer pays for reducing defects (barring the financial
expenses) is that consumers receive less products of their target expected
quality. Now, why can't the producers simply make the product as they did
before, focus on the target expected outcome, and reduce unacceptable
variations using some other techniques? Instead, producers save millions
while consumers less frequently receive the best product/service though with
only 3.4 defects per million outcomes. Thus, it is an ethical issue whether
producers have a moral right to turn to such saving techniques, taking into
account that clients benefit quantitatively, but not qualitatively.
Bibliography
1. Six Sigma. (2006). Wikipedia - the free encyclopedia. Retrieved June 19th
2006 from http://en.wikipedia.org/wiki/Six_Sigma
2. Statistical Six Sigma Definition. (2006). iSixSigma LLC. Retrieved June
19th 2006 from www.isixsigma.com/library/content/c010101a.asp
3. Saxena, S.K. (2006). Introduction to Six Sigma. Discover 6 Sigma.
Retrieved June 19th 2006 from
www.discover6sigma.org/post/2005/10/introduction-to-six-sigma/
Author Bio
Sean Priestley is master administrator and manager of NeWavEssays
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