Sig Sigma - Manufacturing Processes

Six Sigma is a business management strategy,
originally developed by Motorola in 1986.[1]
[2] Six Sigma became well known after Jack
Welch made it a central focus of his business
strategy at General Electric in 1995,[3] and
today it is widely used in many sectors of
industry.[citation needed]
Six Sigma seeks to improve the quality of
process outputs by identifying and removing
the causes of defects (errors) and minimizing
variability in manufacturing and business
processes.[4] It uses a set of quality
management methods, including statistical
methods, and creates a special infrastructure
of people within the organization ("Black Belts",
"Green Belts", etc.) who are experts in these
methods.[4] Each Six Sigma project carried out
within an organization follows a defined
sequence of steps and has quantified financial
targets (cost reduction and/or profit increase).
[4]
The term Six Sigma originated from
terminology associated with manufacturing,
specifically terms associated with statistical
modeling of manufacturing processes. The
maturity of a manufacturing process can be
described by a sigma rating indicating its yield
or the percentage of defect-free products it
creates. A six sigma process is one in which
99.99966% of the products manufactured are
statistically expected to be free of defects (3.4
defects per million). Motorola set a goal of "six
sigma" for all of its manufacturing operations,
and this goal became a byword for the
management and engineering practices used to
achieve it.
Historical overview
Six Sigma originated as a set of practices
designed to improve manufacturing processes
and eliminate defects, but its application was
subsequently extended to other types of
business processes as well.[5] In Six Sigma, a
defect is defined as any process output that
does not meet customer specifications, or that
could lead to creating an output that does not
meet customer specifications.[4]
The core of Six Sigma was “born” at Motorola
in the 1970s out of senior executive Art
Sundry's criticism of Motorola’s bad quality.[6]
As a result of this criticism, the company
discovered a connection between increases in
quality and decreases in costs of production.
At that time, the prevailing view was that
quality costs extra money. In fact, it reduced
total costs by driving down the costs for repair
or control.[7]Bill Smith subsequently
formulated the particulars of the methodology
at Motorola in 1986.[1] Six Sigma was heavily
inspired by the quality improvement
methodologies of the six preceding decades,
such as quality control, Total Quality
Management (TQM), and Zero Defects,[8][9]
based on the work of pioneers such as
Shewhart, Deming, Juran, Crosby, Ishikawa,
Taguchi, and others.
Like its predecessors, Six Sigma doctrine
asserts that:
Continuous efforts to achieve stable and
predictable process results (i.e., reduce
process variation) are of vital importance
to business success.
Manufacturing and business processes
have characteristics that can be measured,
analyzed, improved and controlled.
Achieving sustained quality improvement
requires commitment from the entire
organization, particularly from top-level
management.
Features that set Six Sigma apart from previous
quality improvement initiatives include:
A clear focus on achieving measurable and
quantifiable financial returns from any Six
Sigma project.[4]
An increased emphasis on strong and
passionate management leadership and
support.[4]
A special infrastructure of "Champions",
"Master Black Belts", "Black Belts", "Green
Belts", etc. to lead and implement the Six
Sigma approach.[4]
A clear commitment to making decisions
on the basis of verifiable data and
statistical methods, rather than
assumptions and guesswork.[4]
The term "Six Sigma" comes from a field of
statistics known as process capability studies.
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).[10][11] Six
Sigma's implicit goal is to improve all
processes to that level of quality or better.
Six Sigma is a registered service mark and
trademark of Motorola Inc.[12] As of 2006
Motorola reported over US$17 billion in
savings[13] from Six Sigma. Other early
adopters of Six Sigma who achieved well-
publicized success include Honeywell
(previously known as AlliedSignal) and General
Electric, where Jack Welch introduced the
method.[14] 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.[15]
In recent years, some practitioners have
combined Six Sigma ideas with lean
manufacturing to create a methodology named
Lean Six Sigma.[16] 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".[16] Companies such as IBM and
Sandia National Laboratories use Lean Six
Sigma to focus transformation efforts not just
on efficiency but also on growth. It serves as a
foundation for innovation throughout the
organization, from manufacturing and software
development to sales and service delivery
functions.
Methods
Six Sigma projects follow two project
methodologies inspired by Deming's Plan-Do-
Check-Act Cycle. These methodologies,
composed of five phases each, bear the
acronyms DMAIC and DMADV.[15]
DMAIC is used for projects aimed at
improving an existing business process.
[15] DMAIC is pronounced as "duh-may-
ick".
DMADV is used for projects aimed at
creating new product or process designs.
[15] DMADV is pronounced as "duh-mad-
vee".
DMAIC
The DMAIC project methodology has five
phases:
Define the problem, the voice of the
customer, and the project goals,
specifically.
Measure key aspects of the current
process and collect relevant data.
Analyze the data to investigate and verify
cause-and-effect relationships. Determine
what the relationships are, and attempt to
ensure that all factors have been
considered. Seek out root cause of the
defect under investigation.
Improve or optimize the current process
based upon data analysis using
techniques such as design of experiments,
poka yoke or mistake proofing, and
standard work to create a new, future state
process. Set up pilot runs to establish
process capability.
Control the future state process to ensure
that any deviations from target are
corrected before they result in defects.
Implement control systems such as
statistical process control, production
boards, visual workplaces, and
continuously monitor the process.
Some organizations add a Recognize step at
the beginning, which is to recognize the right
problem to work on, thus yielding an RDMAIC
methodology.[17]
DMADV or DFSS
The DMADV project methodology, also known
as DFSS ("Design For Six Sigma"),[15] features
five phases:
Define design goals that are consistent
with customer demands and the enterprise
strategy.
Measure and identify CTQs
(characteristics that are Critical To
Quality), product capabilities, production
process capability, and risks.
Analyze to develop and design
alternatives, create a high-level design and
evaluate design capability to select the
best design.
Design details, optimize the design, and
plan for design verification. This phase
may require simulations.
Verify the design, set up pilot runs,
implement the production process and
hand it over to the process owner(s).
Quality management tools and methods
used in Six Sigma
Within the individual phases of a DMAIC or
DMADV project, Six Sigma utilizes many
established quality-management tools that are
also used outside Six Sigma. The following
table shows an overview of the main methods
used.
5 Whys
Analysis of
variance
ANOVA Gauge
R&R
Axiomatic
design
Business
Process
Mapping
Cause & effects
diagram (also
Pareto analysis
Pareto chart
Pick chart
Process
capability
Quality Function
Deployment
(QFD)
Quantitative
marketing
research
through use of
Implementation roles
One key innovation of Six Sigma involves the
"professionalizing" of quality management
functions. Prior to Six Sigma, quality
management in practice was largely relegated
to the production floor and to statisticians in a
separate quality department. Formal Six Sigma
programs adopt a ranking terminology (similar
to some martial arts systems) to define a
hierarchy (and career path) that cuts across all
business functions.
Six Sigma identifies several key roles for its
successful implementation.[18]
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 the other role holders with
the freedom and resources to explore new
ideas for breakthrough improvements.
Champions take responsibility for Six
Sigma implementation across the
organization in an integrated manner. 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 100% of their time
to Six Sigma. They assist champions and
guide Black Belts and Green Belts. Apart
from statistical tasks, they spend their
time on ensuring consistent application of
Six Sigma across various functions and
departments.
Black Belts operate under Master Black
Belts to apply Six Sigma methodology to
specific projects. They devote 100% of
their time to Six Sigma. They primarily
focus on Six Sigma project execution,
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.
Some organizations use additional belt
colours, 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.[19]
Certification
Corporations such as early Six Sigma pioneers
General Electric and Motorola developed
certification programs as part of their Six
Sigma implementation, verifying individuals'
command of the Six Sigma methods at the
relevant skill level (Green Belt, Black Belt etc.).
Following this approach, many organizations
in the 1990s started offering Six Sigma
certifications to their employees.[15][20]
Criteria for Green Belt and Black Belt
certification vary; some companies simply
require participation in a course and a Six
Sigma project.[20] There is no standard
certification body, and different certification
services are offered by various quality
associations and other providers against a fee.
[21][22] 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.
[20][23] The International Quality Federation
offers an online certification exam that
organizations can use for their internal
certification programs; it is statistically more
demanding than the ASQ certification.[20][22]
Other providers offering certification services
include the the Juran Institute, Six Sigma
Qualtec, Air Academy Associates and many
others.[21]
Origin and meaning of the term "six
sigma process"
The term "six sigma process" comes from the
notion that if one has six standard deviations
between the process mean and the nearest
specification limit, as shown in the graph,
practically no items will fail to meet
specifications.[11] This is based on the
calculation method employed in process
capability studies.
Capability studies measure the number of
standard deviations between the process mean
and the nearest specification limit in sigma
units. 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.[11]
Graph of the normal distribution, which
underlies the statistical assumptions of
the Six Sigma model. The Greek letter σ
(sigma) marks the distance on the
horizontal axis between the mean, µ, and
the curve's inflection point. The greater
this distance, the greater is the spread of
values encountered. For the green curve
shown above, µ = 0 and σ = 1. The upper
and lower specification limits (USL and
LSL, respectively) are at a distance of 6σ
from the mean. Because of the properties
of the normal distribution, values lying
that far away from the mean are extremely
unlikely. Even if the mean were to move
right or left by 1.5σ at some point in the
future (1.5 sigma shift, coloured red and
blue), there is still a good safety cushion.
This is why Six Sigma aims to have
processes where the mean is at least 6σ
away from the nearest specification limit.
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.[11] 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.[11] To account for this
real-life increase in process variation over
time, an empirically-based 1.5 sigma shift is
introduced into the calculation.[11][24]
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.[11]
Hence the widely accepted definition of a six
sigma process is a process that produces 3.4
defective parts per million opportunities
(DPMO). This is based on the fact that a
process that is normally distributed will have
3.4 parts per million beyond a point that is 4.5
standard deviations above or below the mean
(one-sided capability study).[11] So the 3.4
DPMO of a six sigma process in fact
corresponds to 4.5 sigma, namely 6 sigma
minus the 1.5-sigma shift introduced to
account for long-term variation.[11] 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.
[11]
Sigma levels
A control chart depicting a process that
experienced a 1.5 sigma drift in the
process mean toward the upper
specification limit starting at midnight.
Control charts are used to maintain 6
sigma quality by signaling when quality
professionals should investigate a
process to find and eliminate special-
cause variation.
See also: Three sigma rule
The table[25][26] below gives long-term DPMO
values corresponding to various short-term
sigma levels.
It must be understood that 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 Cpk
value will turn out to be 0.5 less than the
short-term Cpk value. So, 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 (Cpk = –0.17),
rather than 1 sigma within it, as it was in the
short-term study (Cpk = 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.
Sigma
level DPMO Percent
defective
Percentag
yield
1 691,462 69% 31%
2 308,538 31% 69%
3 66,807 6.7% 93.3%
4 6,210 0.62% 99.38%
5 233 0.023% 99.977%
6 3.4 0.00034% 99.99966%
7 0.019 0.0000019% 99.9999981
Software used for Six Sigma
There are generally four classes of software
used to support Six Sigma:
Analysis tools, which are used to perform
statistical or process analysis
Program management tools, used to
manage and track a corporation's entire
Six Sigma program
DMAIC and Lean online project
collaboration tools for local and global
teams
Data Collection tools that feed information
directly into the analysis tools and
significantly reduce the time spent
gathering data
Analysis tools
Arena
ARIS Six Sigma
Bonita Open Solution BPMN2 standard
and KPIs for statistic monitoring
JMP
Microsoft Visio
Minitab
R language (The R Project for Statistical
Computing[27]). Open source software:
statistical and graphic functions from the
base installation can be used for Six Sigma
projects. Furthermore, some contributed
packages at CRAN contain specific tools
for Six Sigma: SixSigma,[28] qualityTools,
[29] qcc[30] and IQCC.[31]
SDI Tools
SigmaXL
Software AG webMethods BPM Suite
SPC XL
Statgraphics
STATISTICA
STATA
MATLAB
Mathematica
Application
Main article: List of Six Sigma companies
Six Sigma mostly finds application in large
organizations.[32] An important factor in the
spread of Six Sigma was GE's 1998
announcement of $350 million in savings
thanks to Six Sigma, a figure that later grew to
more than $1 billion.[32] According to
industry consultants like Thomas Pyzdek and
John Kullmann, companies with fewer than
500 employees are less suited to Six Sigma
implementation, or need to adapt the standard
approach to make it work for them.[32] This is
due both to the infrastructure of Black Belts
that Six Sigma requires, and to the fact that
large organizations present more opportunities
for the kinds of improvements Six Sigma is
suited to bringing about.[32]
In healthcare
Six Sigma strategies were initially applied to the
healthcare industry in March 1998. The
Commonwealth Health Corporation (CHC) was
the first health care organization to
successfully implement the efficient strategies
of Six Sigma.[33] Substantial financial benefits
were claimed, for example in their radiology
department throughput improved by 33% and
costs per radiology procedure decreased by
21.5%;[34] Six Sigma has subsequently been
adopted in other hospitals around the world.
[35][36]
Critics of Six Sigma believe that while Six Sigma
methods may have translated fluidly in a
manufacturing setting, they would not have the
same result in service-oriented businesses,
such as the health industry.[37]
Criticism
Lack of originality
Noted quality expert Joseph M. Juran has
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."[38]
Role of consultants
The use of "Black Belts" as itinerant change
agents has (controversially) fostered an
industry of training and certification. Critics
argue 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.[4]
Potential negative effects
A Fortune article stated that "of 58 large
companies that have announced Six Sigma
programs, 91 percent 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)".[39] 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." Advocates of Six
Sigma have argued that many of these claims
are in error or ill-informed.[40][41]
A more direct criticism is the "rigid" nature of
Six Sigma with its 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). [42] The extensive reliance
on significance testing and use of multiple
regression techniques include the risk of
making commonly unknown types of statistical
errors or mistakes. There are severe pitfalls
inherent to the use of these methods and the
misunderstandings among these issues are
widespread. Since decennia these dangers have
been pointed out, but are often not taken
serious or are simply ignored. The most
serious consequence of this 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. [43] Important warnings related to
the use and misuse (or abuse) of multiple
regression are provided by Gerard E. Dallal.
[44] 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. [45][46][47]
[48] Much of the first criticism was already
published more than 40 years ago. Refer to:
Statistical_hypothesis_testing#Criticism for
details.
Articles featuring critics have appeared in the
November-December 2006 issue of USA Army
Logistician regarding Six-Sigma: "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."[49]
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.[50] 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.[51]
Lack of systematic 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 lacks
academic rigor:
One final criticism, probably more to
the Six Sigma literature than concepts,
relates to the evidence for Six Sigma’s
success. So far, documented case
studies using the Six Sigma methods are
presented as the strongest evidence for
its success. However, looking at these
documented cases, and apart from a few
that are detailed from the experience of
leading organizations like GE and
Motorola, most cases are not
documented in a systemic or academic
manner. In fact, the majority are case
studies illustrated on websites, and are,
at best, sketchy. They provide no
mention of any specific Six Sigma
methods that were used to resolve the
problems. 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). Thus, when
looking at the evidence put forward for
Six Sigma success, mostly by
consultants and people with vested
interests, the question that begs to be
asked is: are we making a true
improvement with Six Sigma methods or
just getting skilled at telling stories?
Everyone seems to believe that we are
making true improvements, but there is
some way to go to document these
empirically and clarify the causal
relations.[42]
Based on arbitrary standards
While 3.4 defects per million opportunities
might work well for certain products/
processes, it might not operate optimally or
cost effectively for others. A pacemaker
process might need higher standards, for
example, whereas a direct mail advertising
campaign might need lower standards. The
basis and justification for choosing six (as
opposed to five or seven, for example) as the
number of standard deviations, together with
the 1.5 sigma shift is not clearly explained. In
addition, the Six Sigma model assumes that the
process data always conform to the normal
distribution. The calculation of defect rates for
situations where the normal distribution
model does not apply is not properly
addressed in the current Six Sigma literature.
This particularly counts for reliability-related
defects and other problems that are not time
invariant. The IEC, ARP, EN-ISO, DIN and other
(inter)national standardization organizations
have not created standards for the Six Sigma
process. This might be the reason that it
became a dominant domain of consultants
(see critics above).[4]
Criticism of the 1.5 sigma shift
The statistician Donald J. Wheeler has
dismissed the 1.5 sigma shift as "goofy"
because of its arbitrary nature.[52] Its
universal applicability is seen as doubtful.[4]
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."[11][53]
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.[53] The fact that it
is rarely explained that a "6 sigma" process
will have long-term defect rates corresponding
to 4.5 sigma performance rather than actual 6
sigma performance has led several
commentators to express the opinion that Six
Sigma is a confidence trick