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Complete
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
mid-1990s, 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 ]
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.
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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.
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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).
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Assumptions:
- Normal Distribution
- Process Mean Shift of 1.5
from Nominal is Likely
- 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
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Key
= standard deviation
µ = center of the distribution
(shifted 1.5from
its original , on-target location)
+/-3
& +/-6
show the specifications relative to the original
target
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Figure 2: Six Sigma Process with Mean Shifted from Nominal by
1. 5
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.
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.
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.
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]
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-Establishing-Acting-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.
This technology is classified under the following categories.
Select a category for a list of related topics.
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[Arnold 99]
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Arnold, Paul V. Pursuing the Holy
Grail [online]. Available WWW <URL:
http://www.mrotoday.com/mro/archives/Editorials/editJJ1999.htm
> (1999).
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[ASQ 00]
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ASQ Statistics Division. Improving
Performance Through Statistical Thinking. Milwaukee, WI:
ASQ Quality Press, 2000.
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[ASA 01]
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American Statistical Association, Quality
& Productivity Section. Enabling Broad Application of
Statistical Thinking [online]. Available WWW
<URL: http://web.utk.edu/~asaqp/thinking.html>
(2001).
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[Bylinsky 98]
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Bylinsky, Gene. How to Bring Out
Better Products Faster [online]. Available WWW
<URL: http://www.amsup.com/media/fortune.htm>
(1998).
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[Harrold 99]
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Harrold, Dave. Designing for Six Sigma
Capability [Online]. Available WWW <URL:
http://www.controleng.com/archives/1999/ctl0101.99/01a103.htm>
(1999).
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[Harry 00]
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Harry, Mikel. "Six Sigma: The
Breakthrough Management Strategy Revolutionizing the World's
Top Corporations." New York, N.Y. Random House Publishers,
2000.
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[Lahiri 99]
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Lahiri, Jaideep. The Enigma of Six
Sigma [online]. Available WWW <URL:
http://www.india-today.com/btoday/19990922/cover.html>
(1999).
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[Marash 99]
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Marash, Stanley A. Six Sigma: Passing
Fad or a Sign of Things to Come? [online].
Available WWW <URL: http://www.thesamgroup.com/sixsigmaarticle.htm>
(1999).
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[Pyzdek 01]
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Pyzdek, Thomas. The Six Sigma
Handbook. New York, N.Y.: McGraw-Hill Professional
Publishing, 2001.
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[Pyzdek 2-01]
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Pyzdek, Thomas. Six Sigma and Beyond:
Why Six Sigma Is Not TQM [online]. Available WWW
<URL: http://www.qualitydigest.com/feb01/html/sixsigma.html>
(2001).
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Jeannine Siviy, SEI
Anita Carleton, SEI
Wolfhart Goethert, SEI
David Zubrow, SEI
1 May 2001 (original)
The Software
Engineering Institute (SEI) is a federally funded research and
development center sponsored by the U.S. Department of Defense
and operated by Carnegie Mellon University.
Copyright
2007
by Carnegie Mellon University
Terms of Use
URL: http://www.sei.cmu.edu/str/descriptions/sigma6_body.html
Last Modified: 11 January 2007
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