six sigma

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Purpose and Origin
Six Sigma (6 ) is a business-driven, multi-faceted approach to process improvement,
reduced costs, and increased profits. With a fundamental principle to improve customer
satisfaction by reducing defects, its ultimate performance target is virtually defect-free
processes and products (3.4 or fewer defective parts per million (ppm)). The Six Sigma
methodology, consisting of the steps "Define - Measure - Analyze - Improve - Control,"
is the roadmap to achieving this goal. Within this improvement framework, it is the
responsibility of the improvement team to identify the process, the definition of defect,
and the corresponding measurements. This degree of flexibility enables the Six Sigma
method, along with its toolkit, to easily integrate with existing models of software
process implementation.
Six Sigma originated at Motorola in the early 1980s in response to a CEO-driven
challenge to achieve tenfold reduction in product-failure levels in five years. Meeting this
challenge required swift and accurate root-cause analysis and correction. In the mid1990s, Motorola divulged the details of their quality improvement framework, which has
since been adopted by several large manufacturing companies. [Harry 00, Arnold 99,
Harrold 99 ]

Technical Detail
The primary goal of Six Sigma is to improve customer satisfaction, and thereby
profitability, by reducing and eliminating defects. Defects may be related to any aspect of
customer satisfaction: high product quality, schedule adherence, cost minimization.
Underlying this goal is the Taguchi Loss Function [Pyzdek 01], which shows that
increasing defects leads to increased customer dissatisfaction and financial loss. Common
Six Sigma metrics include defect rate (parts per million or ppm), sigma level, process
capability indices, defects per unit, and yield. Many Six Sigma metrics can be
mathematically related to the others.
The Six Sigma drive for defect reduction, process improvement and customer satisfaction
is based on the "statistical thinking" paradigm [ASQ 00], [ASA 01]:


Everything is a process



All processes have inherent variability



Data is used to understand the variability and drive process improvement
decisions

As the roadmap for actualizing the statistical thinking paradigm, the key steps in the Six
Sigma improvement framework are Define - Measure - Analyze - Improve - Control (see
Figure 1). Six Sigma distinguishes itself from other quality improvement programs
immediately in the "Define" step. When a specific Six Sigma project is launched, the
customer satisfaction goals have likely been established and decomposed into subgoals
such as cycle time reduction, cost reduction, or defect reduction. (This may have been
done using the Six Sigma methodology at a business/organizational level.) The Define
stage for the specific project calls for baselining and benchmarking the process to be
improved, decomposing the process into manageable sub-processes, further specifying

goals/sub-goals and establishing infrastructure to accomplish the goals. It also includes an
assessment of the cultural/organizational change that might be needed for success.
Once an effort or project is defined, the team methodically proceeds through
Measurement, Analysis, Improvement, and Control steps. A Six Sigma improvement
team is responsible for identifying relevant metrics based on engineering principles and
models. With data/information in hand, the team then proceeds to evaluate the
data/information for trends, patterns, causal relationships and "root cause," etc. If needed,
special experiments and modeling may be done to confirm hypothesized relationships or
to understand the extent of leverage of factors; but many improvement projects may be
accomplished with the most basic statistical and non-statistical tools. It is often necessary
to iterate through the Measure-Analyze-Improve steps. When the target level of
performance is achieved, control measures are then established to sustain performance. A
partial list of specific tools to support each of these steps is shown in Figure 1.

Note:
Many tools can be effectively used in multiple steps of the
framework. Tools that are not particularly relevant to software
applications have not been included in this list.

Figure 1: Six Sigma Improvement Framework and Toolkit
An important consideration throughout all the Six Sigma steps is to distinguish which
process substeps significantly contribute to the end result. The defect rate of the process,
service or final product is likely more sensitive to some factors than others. The analysis
phase of Six Sigma can help identify the extent of improvement needed in each substep
in order to achieve the target in the final product. It is important to remain mindful that
six sigma performance (in terms of the ppm metric) is not required for every aspect of
every process, product and service. It is the goal only where it quantitatively drives (i.e, is
a significant "control knob" for) the end result of customer satisfaction and profitability.
The current average industry runs at four sigma, which corresponds to 6210 defects per
million opportunities. Depending on the exact definition of "defect" in payroll
processing, for example, this sigma level could be interpreted as 6 out of every 1000
paychecks having an error. As "four sigma" is the average current performance, there are

industry sectors running above and below this value. Internal Revenue Service (IRS)
phone-in tax advice, for instance, runs at roughly two sigma, which corresponds to
308,537 errors per million opportunities. Again, depending on the exact definition of
defect, this could be interpreted as 30 out of 100 phone calls resulting in erroneous tax
advice. ("Two Sigma" performance is where many noncompetitive companies run.) On
the other extreme, domestic (U.S.) airline flight fatality rates run at better than six sigma,
which could be interpreted as fewer than 3.4 fatalities per million passengers - that is,
fewer than 0.00034 fatalities per 100 passengers [Harry 00], [Bylinsky 98], [Harrold 99].
As just noted, flight fatality rates are "better than six sigma," where "six sigma" denotes
the actual performance level rather than a reference to the overall combination of
philosophy, metric, and improvement framework. Because customer demands will likely
drive different performance expectations, it is useful to understand the mathematical
origin of the measure and the term "six-sigma process." Conceptually, the sigma level of
a process or product is where its customer-driven specifications intersect with its
distribution. A centered six-sigma process has a normal distribution with mean=target and
specifications placed 6 standard deviations to either side of the mean. At this point, the
portions of the distribution that are beyond the specifications contain 0.002 ppm of the
data (0.001 on each side). Practice has shown that most manufacturing processes
experience a shift (due to drift over time) of 1.5 standard deviations so that the mean no
longer equals target. When this happens in a six-sigma process, a larger portion of the
distribution now extends beyond the specification limits: 3.4 ppm.
Figure 2 depicts a 1.5 -shifted distribution with "6 " annotations. In manufacturing, this
shift results from things such as mechanical wear over time and causes the six-sigma
defect rate to become 3.4 ppm. The magnitude of the shift may vary, but empirical
evidence indicates that 1.5 is about average. Does this shift exist in the software process?
While it will take time to build sufficient data repositories to verify this assumption
within the software and systems sector, it is reasonable to presume that there are factors
that would contribute to such a shift. Possible examples are declining procedural
adherence over time, learning curve, and constantly changing tools and technologies
(hardware and software).
Assumptions:



Normal Distribution



Process Mean Shift of 1.5



Process Mean and Standard Deviation are known



Defects are randomly distributed throughout units



Parts and Process Steps are Independent



For this discussion, original nominal value = target

from Nominal is Likely

Key
= standard deviation
µ = center of the distribution
(shifted 1.5 from its original , on-target location)

+/-3 & +/-6
target

show the specifications relative to the original

Figure 2: Six Sigma Process with Mean Shifted from Nominal by 1. 5

Usage Considerations
In the software and systems field, Six Sigma may be leveraged differently based on the
state of the business. In an organization needing process consistency, Six Sigma can help
promote the establishment of a process. For an organization striving to streamline their
existing processes, Six Sigma can be used as a refinement mechanism.
In organizations at CMM® level 1-3, "defect free" may seem an overwhelming stretch.
Accordingly, an effective approach would be to use the improvement framework
('Define-Measure-Analyze-Improve-Control') as a roadmap toward intermediate defect
reduction goals. Level 1 and 2 organizations may find that adopting the Six Sigma
philosophy and framework reinforces their efforts to launch measurement practices;
whereas Level 3 organizations may be able to begin immediate use of the framework. As
organizations mature to Level 4 and 5, which implies an ability to leverage established
measurement practices, accomplishment of true "six sigma" performance (as defined by
ppm defect rates) becomes a relevant goal.
Many techniques in the Six Sigma toolkit are directly applicable to software and are
already in use in the software industry. For instance, "Voice of the Client" and "Quality
Function Deployment" are useful for developing customer requirements (and are relevant
measures). There are numerous charting/calculation techniques that can be used to
scrutinize cost, schedule, and quality (project-level and personal-level) data as a project
proceeds. And, for technical development, there are quantitative methods for risk analysis
and concept/design selection. The strength of "Six Sigma" comes from consciously and
methodically deploying these tools in a way that achieves (directly or indirectly)
customer satisfaction.
As with manufacturing, it is likely that Six Sigma applications in software will reach
beyond "improvement of current processes/products" and extend to "design of new
processes/products." Named "Design for Six Sigma" (DFSS), this extension heavily
utilizes tools for customer requirements, risk analysis, design decision-making and
inventive problem solving. In the software world, it would also heavily leverage re-use
libraries that consist of robustly designed software.

Maturity
Six Sigma is rooted in fundamental statistical and business theory; consequently, the
concepts and philosophy are very mature. Applications of Six Sigma methods in
manufacturing, following on the heels of many quality improvement programs, are
likewise mature. Applications of Six Sigma methods in software development and other
'upstream' (from manufacturing) processes are emerging.

Costs and Limitations
Institutionalizing Six Sigma into the fabric of a corporate culture can require significant
investment in training and infrastructure. There are typically three different levels of
expertise cited by companies: Green Belt, Black Belt Practitioner, Master Black Belt.
Each level has increasingly greater mastery of the skill set. Roles and responsibilities also
grow from each level to the next, with Black Belt Practitioners often in team/project
leadership roles and Master Black Belts often in mentoring/teaching roles. The
infrastructure needed to support the Six Sigma environment varies. Some companies
organize their trained Green/Black Belts into a central support organization. Others
deploy Green/Black Belts into organizations based on project needs and rely on
communities of practice to maintain cohesion.

Alternatives
In past years, there have been many instances and evolutions of quality improvement
programs. Scrutiny of the programs will show much similarity and also clear distinctions
between such programs and Six Sigma. Similarities include common tools and methods,
concepts of continuous improvement, and even analogous steps in the improvement
framework. Differences have been articulated as follows:


Six Sigma speaks the language of business. It specifically addresses the concept
of making the business as profitable as possible.



In Six Sigma, quality is not pursued independently from business goals. Time and
resources are not spent improving something that is not a lever for improving
customer satisfaction.



Six Sigma focuses on achieving tangible results.



Six Sigma does not include specific integration of ISO900 or Malcolm Baldridge
National Quality Award criteria.



Six Sigma uses an infrastructure of highly trained employees from many sectors
of the company (not just the Quality Department). These employees are typically
viewed as internal change agents.



Six Sigma raises the expectation from 3-sigma performance to 6-sigma. Yet, it
does not promote "Zero Defects" which many people dismiss as "impossible."

Sources: [Pyzdek 2-01, Marash 99, Harry 00]

Complementary Technologies
It is difficult to concisely describe the ways in which Six Sigma may be interwoven with
other initiatives (or vice versa). The following paragraphs broadly capture some of the
possible interrelationships between initiatives.
Six Sigma and improvement approaches such as CMM‚, CMMISM, PSPSM/TSPSM are
complementary and mutually supportive. Depending on current organizational, project or
individual circumstances, Six Sigma could be an enabler to launch CMM®, CMMISM,

PSPSM, or TSPSM. Or, it could be a refinement toolkit/methodology within these
initiatives. For instance, it might be used to select highest priority Process Areas within
CMMISM or to select highest leverage metrics within PSPSM.
Examination of the Goal-Question-Metric (GQM), Initiating-Diagnosing-EstablishingActing-Leveraging (IDEALSM), and Practical Software Measurement (PSM) paradigms,
likewise, shows compatibility and consistency with Six Sigma. GQ(I)M meshes well with
the Define-Measure steps of Six Sigma. IDEAL and Six Sigma share many common
features, with IDEALSM being slightly more focused on change management and
organizational issues and Six Sigma being more focused on tactical, data-driven analysis
and decision making. PSM provides a software-tailored approach to measurement that
may well serve the Six Sigma improvement framework.

Index Categories
This technology is classified under the following categories. Select a category for a list of
related topics.
Name of technology

Six Sigma

Application category

Detailed Design (AP.1.3.5)
Code (AP.1.4.2)
Unit Testing (AP.1.4.3.4)
Component Testing (AP.1.4.3.5)

Quality measures category

Reliability (QM.2.1.2)
Availability (QM.2.1.1)
Maintenance Control (QM.5.1.2.3)
Productivity (QM.5.2)

Computing reviews category

Management (D.2.9)

References and Information Sources
[Arnold Arnold, Paul V. Pursuing the Holy Grail [online]. Available WWW <URL:
99]
http://www.mrotoday.com/mro/archives/Editorials/editJJ1999.htm > (1999).
[ASQ
00]

ASQ Statistics Division. Improving Performance Through Statistical Thinking. Milwaukee, WI: ASQ
Quality Press, 2000.

[ASA
01]

American Statistical Association, Quality & Productivity Section. Enabling Broad Application of Statistical
Thinking [online]. Available WWW <URL: http://web.utk.edu/~asaqp/thinking.html> (2001).

[Bylins Bylinsky, Gene. How to Bring Out Better Products Faster [online]. Available WWW <URL:
ky 98] http://www.amsup.com/media/fortune.htm> (1998).
[Harrol Harrold, Dave. Designing for Six Sigma Capability [Online]. Available WWW <URL:
d 99] http://www.controleng.com/archives/1999/ctl0101.99/01a103.htm> (1999).
[Harry Harry, Mikel. "Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top
00]
Corporations." New York, N.Y. Random House Publishers, 2000.

[Lahiri Lahiri, Jaideep. The Enigma of Six Sigma [online]. Available WWW <URL: http://www.india99]
today.com/btoday/19990922/cover.html> (1999).
[Marash Marash, Stanley A. Six Sigma: Passing Fad or a Sign of Things to Come? [online]. Available WWW <URL:
99]
http://www.thesamgroup.com/sixsigmaarticle.htm> (1999).
[Pyzdek
Pyzdek, Thomas. The Six Sigma Handbook. New York, N.Y.: McGraw-Hill Professional Publishing, 2001.
01]
[Pyzdek Pyzdek, Thomas. Six Sigma and Beyond: Why Six Sigma Is Not TQM [online]. Available WWW <URL:
2-01] http://www.qualitydigest.com/feb01/html/sixsigma.html> (2001).

Current Author/Maintainer
Jeannine Siviy, SEI

External Reviewers
Anita Carleton, SEI
Wolfhart Goethert, SEI
David Zubrow, SEI

Modifications
1 May 2001 (original)

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