Six Sigma

Each Six Sigma project follows a defined methodology and has specific value targets, such as reducing pollution or increasing customer satisfaction.

The term Six Sigma originates from statistical quality control, a reference to the fraction of a normal curve that lies within six standard deviations of the mean, used to represent a defect rate.

[5] By the late 1990s, about two thirds of the Fortune 500 organizations had begun Six Sigma initiatives with the aim of reducing costs and improving quality.

Formal Six Sigma programs adopt an elite ranking terminology similar to martial arts systems like judo to define a hierarchy (and career path) that spans business functions and levels.

Six Sigma identifies several roles for successful implementation:[12] According to proponents, special training is needed for all of these practitioners to ensure that they follow the methodology and use the data-driven approach correctly.

[9] As a result, the number of sigmas that will fit between the process mean and the nearest specification limit may well drop over time, compared to an initial short-term study.

[9] To account for this real-life increase in process variation over time, an empirically based 1.5 sigma shift is introduced into the calculation.

[9] This allows for the fact that special causes may result in a deterioration in process performance over time and is designed to prevent underestimation of the defect levels likely to be encountered in real-life operation.

The purpose of the sigma value is as a comparative figure to determine whether a process is improving, deteriorating, stagnant or non-competitive with others in the same business.

[5] According to industry consultants like Thomas Pyzdek and John Kullmann, companies with fewer than 500 employees are less suited to Six Sigma or need to adapt the standard approach to making it work for them.

The fact that an organization is not big enough to be able to afford black belts does not diminish its ability to make improvements using this set of tools and techniques.

[5] After its first application at Motorola in the late 1980s, other internationally recognized firms currently recorded high number of savings after applying Six Sigma.

[23] Although companies have considered common quality control and process improvement strategies, there's still a need for more reasonable and effective methods as all the desired standards and client satisfaction have not always been reached.

Other financial institutions that have adopted Six Sigma include GE Capital and JPMorgan Chase, where customer satisfaction was the main objective.

[23] In the supply-chain field, it is important to ensure that products are delivered to clients at the right time while preserving high-quality standards.

[24] This is a sector that has been highly matched with this doctrine for many years because of the nature of zero tolerance for mistakes and potential for reducing medical errors involved in healthcare.

[28] A review of academic literature [29] found 34 common failure factors in 56 papers on Lean, Six Sigma, and LSS from 1995-2013.

"[30] Quality expert Philip B. Crosby pointed out that the Six Sigma standard does not go far enough—customers deserve defect-free products every time.

[31] For example, under the Six Sigma standard, semiconductors, which require the flawless etching of millions of tiny circuits onto a single chip, are all defective.

The statement was attributed to "an analysis by Charles Holland of consulting firm Qualpro (which espouses a competing quality-improvement process)".

[34] The summary of the article is that Six Sigma is effective at what it is intended to do, but that it is "narrowly designed to fix an existing process" and does not help in "coming up with new products or disruptive technologies.

[37] The extensive reliance on significance testing and use of multiple regression techniques increase the risk of making commonly unknown types of statistical errors or mistakes.

A possible consequence of Six Sigma's array of p-value misconceptions is the false belief that the probability of a conclusion being in error can be calculated from the data in a single experiment without reference to external evidence or the plausibility of the underlying mechanism.

In a 2006 issue of USA Army Logistician an article critical of Six Sigma noted: "The dangers of a single paradigmatic orientation (in this case, that of technical rationality) can blind us to values associated with double-loop learning and the learning organization, organization adaptability, workforce creativity and development, humanizing the workplace, cultural awareness, and strategy making.

Dodge states[50] "excessive metrics, steps, measurements and Six Sigma's intense focus on reducing variability water down the discovery process.

He concludes "there's general agreement that freedom in basic or pure research is preferable while Six Sigma works best in incremental innovation when there's an expressed commercial goal."

A BusinessWeek article says that James McNerney's introduction of Six Sigma at 3M had the effect of stifling creativity and reports its removal from the research function.

It cites two Wharton School professors who say that Six Sigma leads to incremental innovation at the expense of blue skies research.

It has been argued that by relying on the Six Sigma criteria, management is lulled into the idea that something is being done about quality, whereas any resulting improvement is accidental (Latzko 1995).

Normal distribution underlies the statistical assumptions of Six Sigma. At , ( mu ) marks the mean , with the horizontal axis showing distance from the mean, denoted in units of standard deviation (represented as or sigma). The greater the standard deviation, the larger the spread of values; for the green curve, and . The upper and lower specification limits (USL and LSL) are at a distance of 6σ from the mean. Normal distribution means that values far away from the mean are extremely unlikely—approximately 1 in a billion too low, and the same too high. Even if the mean were to move right or left by 1.5 standard deviations (also known as a 1.5 sigma shift, colored red and blue), there is still a safety cushion.
Six Sigma symbol
DMAIC's five steps
DMADV's five steps
A control chart showing a process that experienced a 1.5 sigma drift in the process mean toward the upper specification limit starting at midnight. Control charts help identify when a process should be investigated in order to find and eliminate special-cause variation .