Six Sigma (6σ) is a set of techniques and tools for process improvement. It was introduced by American engineer Bill Smith while working at Motorola in 1986.
Six Sigma strategies seek to improve manufacturing quality by identifying and removing the causes of defects and minimizing variability in manufacturing and business processes. This is done by using empirical and statistical quality management methods and by hiring people who serve as Six Sigma experts. 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.
History
Motorola pioneered Six Sigma, setting a "six sigma" goal for its manufacturing business. It registered Six Sigma as a service mark on June 11, 1991 (); on December 28, 1993, it registered Six Sigma as a trademark. In 2005, Motorola attributed over $17 billion in savings to Six Sigma.
Honeywell and General Electric were also early adopters of Six Sigma. As GE's CEO, in 1995 Jack Welch made it central to his business strategy. In 1998, GE announced $350 million in cost savings thanks to Six Sigma, which was an important factor in the spread of Six Sigma (this figure later grew to more than $1 billion).
In , some practitioners have combined Six Sigma ideas with lean manufacturing to create a methodology named Lean Six Sigma. The Lean Six Sigma methodology views lean manufacturing, which addresses process flow and waste issues, and Six Sigma, with its focus on variation and design, as complementary disciplines aimed at promoting "business and operational excellence". Other standards have been created mostly by universities or companies with Six Sigma first-party certification programs.
Etymology
right|thumb|550px|[[Normal distribution underlies the statistical assumptions of Six Sigma. At <math display="inline">0</math>, <math display="inline">\mu</math> (mu) marks the mean, with the horizontal axis showing distance from the mean, denoted in units of standard deviation (represented as <math display="inline">\sigma</math> or sigma). The greater the standard deviation, the larger the spread of values; for the green curve, <math display="inline">\mu = 0</math> and <math display="inline">\sigma = 1</math>. 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.]]
The term Six Sigma comes from statistics, specifically from the field of statistical quality control, which evaluates process capability. Originally, it referred to the ability of manufacturing processes to produce a very high proportion of output within specification. Processes that operate with "six sigma quality" over the short term are assumed to produce long-term defect levels below 3.4 defects per million opportunities (DPMO). The 3.4 dpmo is based on a "shift" of ± 1.5 sigma explained by Mikel Harry. This figure is based on the tolerance in the height of a stack of discs.
Specifically, say that there are six standard deviations—represented by the Greek letter σ (sigma)—between the mean—represented by μ (mu)—and the nearest specification limit. As process standard deviation goes up, or the mean of the process moves away from the center of the tolerance, fewer standard deviations will fit between the mean and the nearest specification limit, decreasing the sigma number and increasing the likelihood of items outside specification. According to a calculation method employed in process capability studies, this means that practically no items will fail to meet specifications.
DMADV
thumb|right|upright=1.4|DMADV's five steps
Also known as DFSS ("Design For Six Sigma"), the DMADV methodology's five phases are:
- Executive Leadership includes the CEO and other members of top management. They are responsible for setting up a vision for Six Sigma implementation. They also empower other stakeholders with the freedom and resources to transcend departmental barriers and overcome resistance to change.
- Champions take responsibility for Six Sigma implementation across the organization. The Executive Leadership draws them from upper management. Champions also act as mentors to Black Belts.
- Master Black Belts, identified by Champions, act as in-house coaches on Six Sigma. They devote all of their time to Six Sigma, assisting Champions and guiding Black Belts and Green Belts. In addition to statistical tasks, they ensure that Six Sigma is applied consistently across departments and job functions.
- Black Belts operate under Master Black Belts to apply Six Sigma to specific projects. They also devote all of their time to Six Sigma. They primarily focus on Six Sigma project execution and special leadership with special tasks, whereas Champions and Master Black Belts focus on identifying projects/functions for Six Sigma.
- Green Belts are the employees who take up Six Sigma implementation along with their other job responsibilities, operating under the guidance of Black Belts.
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.
Some organizations use additional belt colors, such as "yellow belts", for employees that have basic training in Six Sigma tools and generally participate in projects, and "white belts" for those locally trained in the concepts but do not participate in the project team. "Orange belts" are also mentioned to be used for special cases.
Certification
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General Electric and Motorola developed certification programs as part of their Six Sigma implementation. Following this approach, many organizations in the 1990s started offering Six Sigma certifications to their employees. In 2008 Motorola University later co-developed with Vative and the Lean Six Sigma Society of Professionals a set of comparable certification standards for Lean Certification. Criteria for Green Belt and Black Belt certification vary; some companies simply require participation in a course and a Six Sigma project. The American Society for Quality, for example, requires Black Belt applicants to pass a written exam and to provide a signed affidavit stating that they have completed two projects or one project combined with three years' practical experience in the body of knowledge.
Tools and methods
Within the individual phases of a DMAIC or DMADV project, Six Sigma uses many established quality-management tools that are also used outside Six Sigma. The following list shows an overview of the main methods used.
Software
Role of the 1.5 sigma shift
Experience has shown that processes usually do not perform as well in the long term as they do in the short term. Mikel Harry, the creator of Six Sigma, based the 1.5 sigma shift on the height of a stack of discs. He called this "Benderizing". He claimed that based on his stack, all processes shift 1.5 sigma every 50 samples. According to this idea, a process that fits 6 sigma between the process mean and the nearest specification limit in a short-term study will in the long term fit only 4.5 sigma – either because the process mean will move over time, or because the long-term standard deviation of the process will be greater than that observed in the short term, or both.
These figures assume that the process mean will shift by 1.5 sigma toward the side with the critical specification limit. In other words, they assume that after the initial study determining the short-term sigma level, the long-term C<sub>pk</sub> value will turn out to be 0.5 less than the short-term C<sub>pk</sub> value. So, now for example, the DPMO figure given for 1 sigma assumes that the long-term process mean will be 0.5 sigma beyond the specification limit (C<sub>pk</sub> = −0.17), rather than 1 sigma within it, as it was in the short-term study (C<sub>pk</sub> = 0.33). Note that the defect percentages indicate only defects exceeding the specification limit to which the process mean is nearest. Defects beyond the far specification limit are not included in the percentages.
The formula used here to calculate the DPMO is thus
<math display="block">\text{DPMO} = 1,000,000 \cdot (1 - \phi(\text{level} - 1.5))</math>
{| class="wikitable"
|-
!Sigma level
!Sigma (with 1.5σ shift)
!DPMO
!Percent defective
!Percentage yield
!Short-term C<sub>pk</sub>
!Long-term C<sub>pk</sub>
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|-
| 1
| −0.5
| 691,462
| 69%
| 31%
| 0.33
| −0.17
|-
<!--
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| 2
| 0.5
| 308,538
| 31%
| 69%
| 0.67
| 0.17
|-
<!--
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| 3
| 1.5
| 66,807
| 6.7%
| 93.3%
| 1.00
| 0.5
|-
<!--
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| 4
| 2.5
| 6,210
| 0.62%
| 99.38%
| 1.33
| 0.83
|-
<!--
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| 5
| 3.5
| 233
| 0.023%
| 99.977%
| 1.67
| 1.17
|-
<!--
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| 6
| 4.5
| 3.4
| 0.00034%
| 99.99966%
| 2.00
| 1.5
|-
<!--
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| 7
| 5.5
| 0.019
| 0.0000019%
| 99.9999981%
| 2.33
| 1.83
|}
In practice
Six Sigma, mostly finds application in large organizations. Six Sigma, however, contains a large number of tools and techniques that work well in small to mid-size organizations. 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. The infrastructure described as necessary to support Six Sigma is a result of the size of the organization rather than a requirement of Six Sigma itself.
Engineering and construction
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. There is still a need for an essential analysis that can control the factors affecting concrete cracks and slippage between concrete and steel. After conducting a case study on Tinjin Xianyi Construction Technology, it was found that construction time and construction waste were reduced by 26.2% and 67% accordingly after adopting Six Sigma. Similarly, Six Sigma implementation was studied at one of the largest engineering and construction companies in the world: Bechtel Corporation, where after an initial investment of $30 million in a Six Sigma program that included identifying and preventing rework and defects, over $200 million were saved.
Healthcare
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. The goal of Six Sigma in healthcare is broad and includes reducing the inventory of equipment that brings extra costs, altering the process of healthcare delivery in order to make it more efficient and refining reimbursements. A study at the MD Anderson Cancer Center, which recorded an increase in examinations with no additional machines of 45% and a reduction in patients' preparation time of 40 minutes; from 45 minutes to 5 minutes in multiple cases.
Criticism
While there are many advocates for a Six Sigma approach for the reasons stated above, more than half of projects are unsuccessful: in 2010, the Wall Street Journal reported that more than 60% of projects fail. A review of academic literature found 34 common failure factors in 56 papers on Lean, Six Sigma, and LSS from 1995 to 2013. Among them are (summarized):
- Lack of top management attitude, commitment, and involvement; lack of leadership and vision
- Lack of training and education; lack of resources (financial, technical, human, etc.)
- Poor project selection and prioritization; weak link to strategic objectives of the organization
- Resistance to culture change; Poor communication; Lack of consideration of the human factors
- Lack of awareness of the benefits of Lean/Six Sigma; Lack of technical understanding of tools, techniques, and practices
Others have provided other criticisms.
Lack of originality
Quality expert Joseph M. Juran described Six Sigma as "a basic version of quality improvement", stating that "there is nothing new there. It includes what we used to call facilitators. They've adopted more flamboyant terms, like belts with different colors. I think that concept has merit to set apart, to create specialists who can be very helpful. Again, that's not a new idea. The American Society for Quality long ago established certificates, such as for reliability engineers."
Inadequate for complex manufacturing
Quality expert Philip B. Crosby stated that the Six Sigma standard does not go far enough and that customers deserve defect-free products every time. According to Crosby, because the Six Sigma standard allows for 3.4 defects per million opportunities and microchips contain millions of microscopic circuits that must be etched flawlessly, Six Sigma permits all microchips to be defective.
Role of consultants
The use of "Black Belts" as itinerant change agents has fostered an industry of training and certification. Critics have argued there is overselling of Six Sigma by too great a number of consulting firms, many of which claim expertise in Six Sigma when they have only a rudimentary understanding of the tools and techniques involved or the markets or industries in which they are acting.
Potential negative effects
A Fortune article stated that "of 58 large companies that have announced Six Sigma programs, 91% have trailed the S&P 500 since". The statement was attributed to "an analysis by Charles Holland of consulting firm Qualpro (which espouses a competing quality-improvement process)". 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."
Over-reliance on statistics
More direct criticism targets the "rigid" nature of Six Sigma, arguing that it has an over-reliance on methods and tools. In most cases, more attention is paid to reducing variation and searching for any significant factors, and less attention is paid to developing robustness in the first place (which can altogether eliminate the need for reducing variation). 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. One of the most serious but all-too-common misuses of inferential statistics is to take a model that was developed through exploratory model building and subject it to the same sorts of statistical tests that are used to validate a model that was specified in advance.
Another comment refers to the oft-mentioned Transfer Function, which seems to be a flawed theory if looked at in detail. Since significance tests were first popularized many objections have been voiced by prominent and respected statisticians. The volume of criticism and rebuttal has filled books with language seldom used in the scholarly debate of a dry subject. Much of the first criticism was already published more than 40 years ago (see ).
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."
1.5 sigma shift
The statistician Donald J. Wheeler has dismissed the 1.5 sigma shift as "goofy" because of its arbitrary nature. Its universal applicability is seen as doubtful.
The 1.5 sigma shift has also become contentious because it results in stated "sigma levels" that reflect short-term rather than long-term performance: a process that has long-term defect levels corresponding to 4.5 sigma performance is, by Six Sigma convention, described as a "six sigma process". The accepted Six Sigma scoring system thus cannot be equated to actual normal distribution probabilities for the stated number of standard deviations, and this has been a key bone of contention over how Six Sigma measures are defined. He concludes that "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." This phenomenon is further explored in the book Going Lean, which describes a related approach known as lean dynamics and provides data to show that Ford's 6 Sigma program did little to change its fortunes.
Lack of documentation
One criticism voiced by Yasar Jarrar and Andy Neely from the Cranfield School of Management's Centre for Business Performance is that while Six Sigma is a powerful approach, it can also unduly dominate an organization's culture; and they add that much of the Six Sigma literature – in a remarkable way (six-sigma claims to be evidence, scientifically based) – lacks academic rigor: