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.

1. Six Sigma. (2006). Wikipedia – the free encyclopedia. Retrieved June 19th 2006 from
2. Statistical Six Sigma Definition. (2006). iSixSigma LLC. Retrieved June 19th 2006 from
3. Saxena, S.K. (2006). Introduction to Six Sigma. Discover 6 Sigma. Retrieved June 19th 2006 from

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