1. Bsc Honours Degree in Quality Management and Technology
An Introduction to Six Sigma
Eoin Duff
Institute of Technology Sligo April 2009

2. Abstract
Six sigma first gained public attention when Motorola won the Malcolm Baldrige
national quality award in 1988. The savings ($15 billion over 11 years) (Kwak et al
2006) that Motorola attributed to it’s six sigma programme attracted the attention of
numerous companies such as General Electric, IBM, Allied Signal, Johnson and
Johnson and many more. Six sigma’s rise in popularity led to various different forms
of implementation, which has led to disagreement in the literature over how to define
six sigma. There is also disagreement over whether six sigma is a new concept or
simply a new form of Total Quality Management. Six sigma can be the basis of a
quality management system or a driver of organisational culture change and
continuous business improvement. In the author’s opinion, six sigma has evolved
from TQM and uses many of the same tools and theories but has a fixed structure (the
belt system), measurable goals and an expansive tool set to achieve those goals. The
plan used to implement a six sigma programme is critically important, there has been
progress made towards identifying the critical success factors involved and this is
discussed in Chapter 4. Six sigma should not be seen as a quick fix solution or a one
size fits all option for every business, if it is suitable for a business it should be
tailored to fit their needs and implemented with care. If suitable to the problem at
hand and applied correctly the benefits of success outweigh the risks of failure.

3. Table of Contents:
Chapter 1: Introduction Page 1
1.1: What is six sigma? Page 1
Chapter 2: Evolution of Six Sigma Page 7
2.1: Brief history of quality leading to Six Sigma Page 7
2.1.1: W.A Shewhart Page 8
2.1.2: W.E Deming Page 8
2.1.3: Joseph M. Juran Page 9
2.1.4: Armind V. Feigenbaum Page 10
2.1.5: Dr Karou Ishikawa Page 10
2.1.6: Genichi Taguchi Page 11
2.1.7: Philip B. Crosby Page 12
2.1.8: Landmarks on the road to Six Sigma Page 12
2.2: The Origins of Six Sigma Page 15
Chapter 3: Six Sigma Tools Page 18
3.1: Define Measure Analyse Improve Control (DMAIC) Page 18
3.2: Measurement system analysis Page 21
3.3: Process Control Page 21
3.4: Design for Six Sigma (DFSS) Page 23
3.5: Design of Experiments (DOE) Page 24
3.6: Failure Mode and Effects Analysis (FMEA) Page 25
3.7: Capability analysis Page 26
3.8: Histogram Page 26

4. 3.9: Pareto chart Page 27
3.10: Cause and Effect diagram Page 28
3.11: Scatter Plots Page 29
3.12: Statistical Analysis Page 30
Chapter 4: Implementing Sigma Page 33
4.1: Six Sigma Critical Success Factors Page 34
4.1.1: Management involvement and commitment Page 35
4.1.2: Cultural change Page 36
4.1.3: Communication Page 36
4.1.4: Organisational infrastructure Page 37
4.1.5: Training Page 38
4.1.6: Linking Six Sigma to business strategy Page 39
4.1.7: Linking Six Sigma to customer Page 40
4.1.8: Linking Six Sigma to Human Resources Page 40
4.1.9: Linking Six Sigma to suppliers Page 40
4.1.10: Understanding the tools and techniques of Six Sigma Page 41
4.1.11: Project management skills Page 41
4.1.12: Project Prioritisation skills Page 42
Conclusion Page 44
Bibliography Page 46
Appendix 1: Six Sigma Tools Page 47

5. List of Illustrations
Tables:
Table 1: Six sigma process capability
Table 2: Sigma/Quality level
Table 3: Some reported benefits and savings arising from six sigma programs
Table 4: Major events in the development of modern day quality theory
Table 5: Process Cpk index values versus sigma values
Table 6: Examples of commonly used statistical tools and their use
Table 7: Different emphasis of six sigma in various companies
Table 8: Critical success factors for six sigma implementation
Table 9: Work role versus training profile for six sigma implementation
Figures:
Figure 1: Process model of work
Figure 2: Short term (left) and long term variation (right) of a single characteristic,
the 1.5 σshift
Figure3: Overview of DMAIC method
Figure 4: Differentiation between specification limits and actual process variation
Figure 5: Flow diagram of a DFSS process
Figure 6: Example of a Pareto chart
Figure 7: Cause and effect diagram
Figure 8: Scatter Plot of Defects versus Temperature
Figure 9: Six sigma belt system structure

6. Acknowledgements
I would like to say thank you to all my friends and family who helped me and put up
with me while I was completing this thesis, you all know who you are. I would also
like to thank my supervisor Paul Curran for being both flexible and encouraging
throughout. I would finally like to thank all the staff in IT Sligo who helped me with
references from the library and Noel Rafferty for his initial guidance

7. Chapter 1: Introduction
The phenomenon known as six sigma is actually quite hard to pin down and there is
much disagreement in the literature. The reason for this may be due in part, to its
evolving nature. In this thesis a fundamental definition of what six sigma is will be
discussed. Its history in terms of its evolution and application will also be discussed,
along with an outline of some of the more popular six sigma tools. In order to
critically evaluate the application of six sigma, a selection critical success factors will
be examined.
1.1 What is six sigma?
So what is six sigma and why does it exist? In order to understand what it is, it is first
useful to consider why six sigma would be needed in the first place. Put simply,
variation is the reason for the existence of six sigma. Variation is everywhere and in
every process, it is present in the manufacture of all products as well as the provision
of services and in this way it affects the “quality” of products and services provided
by every business on the planet. The “quality” of the products or services provided by
a business has a major effect on customer satisfaction, which affects the bottom line
of any business, profits. Quality itself has many definitions and will be briefly
discussed later, for now it can be thought of as consistently giving the customers what
they want or expect. But why is variation important? Work can be thought of as a
process where inputs are transformed into outputs, these outputs can be thought of as
the product which is delivered to the customer. In order to be consistent in providing
the customer with satisfactory or good products, the variation within the process must
be minimised. In order to control variation it must first be measured and understood,
statistics can be used for this purpose and six sigma uses proven statistical methods to
measure and reduce process variation and defective results, see Figure 1 below.
1

8. Figure 1: Process model of work (Blakeslee, 1999)
Six sigma can be thought of as a business improvement mantra based on the core
foundation of identifying and eliminating the root causes of defects in processes. This
is done by focusing on the outputs which are critical to customer satisfaction. Process
improvement is the goal, which leads to improved customer satisfaction as well as
augmented profits through increased savings and revenue (Snee 1999). In simple
terms, six sigma refers to the ability of a process to produce outputs with only 3.4
defects per million opportunities (DPMO) or a success rate of 99.9997%, where a
defect can be thought of as anything that leads to customer dissatisfaction (Snee
1999). In this way six sigma proposes a direct correlation between: the level of
defects and wasted operating costs to the level of customer satisfaction (Harry 2000).
The “sigma value” can be seen as a metric to measure the quality of any process.
Higher levels of DPMO are associated with lower “sigma values” where sigma in this
case is used as a unit of measurement for the amount of defects produced by a
process, as can be seen in Table 1. In statistical terms σ(sigma) represents standard
deviation, a measure of variation but in terms of six sigma the “sigma value” is used
to indicate how many defects are likely to occur in a process.
2

9. Table 1: Six sigma process capability (Lucas, 2002)
In theory, the higher the sigma value of a process the less likely it is for defects to
occur. Consequently an increase in the sigma value will increase product reliability,
improve costs and reduce cycle times as well as increase customer satisfaction (Harry
2000). There is a fundamental assumption in six sigma methodology which has
gained a lot of attention within the literature, the assumption that long term process
capability can be estimated from short term performance data or the “1.5 sigma shift”
debate. Based on past research at Motorola, six sigma assumes that any process is
quite likely to shift from its natural center point or mean on a normal distribution, by
approximately 1.5 standard deviations at any given moment (Harry 1998). This 1.5
sigma shift represents the long term variability present in a process which can be put
down to things such as wear and tear, material changes, machine set up etc. (Harry
1998). With this information in mind, it can be seen from Figure 2 below that even
processes with very good performance in the short term can have largely increased
variation in the long term, 0.002 rises to 3.4 DPMO when there is a 1.5 sigma shift in
the location of the process mean and Table 2 explores this relationship further.
3

10. Figure 2: Short term (left) and long term variation (right) of a single characteristic,
the 1.5 σshift (Harry 1998)
Research by Bothe (2002) provides a statistical rationale for the 1.5 sigma shift,
stating that shifts in the mean below this magnitude have less than a 50% chance of
being detected by statistical process control. If these shifts remain undetected they
increase the number of defects and widen the spread of the output. Therefore by
assuming the worst and allowing for a 1.5 sigma shift the producers can have more
confidence that their customers will receive the desired quality levels. Bothe also
states however, that assuming a 1.5 sigma shift for every process is not optimal and he
presents a guideline based on subgroup size for selecting the level of shift to be used
for a certain process. The important point is that all quality problems are measured by
DPMO as a metric and this metric is transformed into an equivalent Z value for the
normal distribution which is known as the sigma value of process capability as shown
in Table 2 below along with the effect of various shifts from the mean.
4

11. Table 2: Sigma/Quality level (Henderson et al 2000)
As a methodology six sigma can be thought of as combining traditional quality tools
and statistical methods to improve profits and customer satisfaction by eliminating
defects from processes. The “breakthrough strategy” as it is commonly referred to,
which six sigma is based on was originally seen as a four step process for problem
solving and process improvement: measure, analyse, improve and control (MAIC). A
define step is now commonly included to the MAIC process to form DMAIC. The
define step is self explanatory in that it defines the problem accurately and in detail.
Measuring quantifies the situation, this involves analysing the measurement data and
the processes involved to identify the root cause of the problem. Improvement is
carried out by considering a range of solutions to the problem and implementing the
most suitable ones. Controlling the process involves on going measurement and
maintenance of the process to ensure the problems do not resurface (Hammer and
Goding 2001). This will be discussed in more detail later, it is interesting for now to
consider how similar this core MAIC process is to the traditional plan-do-check-act
(PDCA) cycle originally proposed by Shewhart and popularised by Deming (Lucas
2002). Six sigma enthusiasts claim that the structured roles and responsibilities of its
implementation set six sigma apart from other quality initiatives. Six sigma uses a
“belt system” (Zu et al 2008) as part of a structured approach to assign the relevant
5

12. training needs and different responsibilities to improvement specialists. This typically
involves a champion, who is usually an executive who will sponsor improvement
programs and provide the resources required to complete projects. Master black belts
are experts in statistical analysis, project management, problem solving techniques
leadership skills, quality improvement techniques etc and they are responsible for
training black belts and green belts. They are also responsible for overseeing a
number of projects which are run by black belts who in turn work with and mentor
green belts to complete six sigma projects. Whether or not six sigma is a novel idea is
irrelevant in the author’s opinion. Results to the bottom line are what matter in the
world of business and by that measure six sigma can be seen as a formidable resource
which has had some impressive results to date as shown in Table 3 below.
Table 3: Some reported benefits and savings arising from six sigma programs (Kwak
et al 2006)
Works cited in this chapter:
• Blakeslee, J, (1999), “Implementing the six sigma solution” Quality Progress,
July, pp 77-86
• Bothe, D, (2002), “Statistical reason for the 1.5σ shift” Quality Engineering
Vol 14, No 3, pp 479-487
6

13. • Harry, M, (2000), “Framework for business leadership: Breakthrough strategy
makes factorial dimensions of quality visible so managers can close capability,
capacity gaps”, Quality Progress, April
• Harry, M, (1998), “Six sigma: a breakthrough strategy for profitability”,
Quality Progress, May, pp 60-65
• Hammer, M and Goding, J, (2001) “Putting six sigma in perspective” Quality,
Vol 40, No.10, pp 58-62
• Henderson, K, and Evans, J, (2000), “Successful implementation of six sigma:
benchmarking General Electric Company”, Benchmarking: an International
Journal, Vol 7, No 4, pp 260-281
• Kwak, Y, and Anbari F, (2006) “Benefits, obstacles and future of six sigma
approach” Technovation 26, pp 707-715
• Lucas, J, (2002 ), “The essential six sigma: How successful six sigma
implementation can improve the bottom line”, Quality Progress, January, pp
27-31
• Snee, R, (1999 ), “Why should statisticians pay attention to six sigma?” ,
Quality Progress, September, pp 100-103
• Zu, X, Fredendall, L, and Douglas, T, (2008) “The evolving theory of quality
management: The role of Six Sigma” Journal of operations management 26,
pp 630-650
7

14. Chapter 2: Evolution of Six Sigma
2.1 Brief history of quality leading to Six sigma
The history of the quality movement is a complex area of debate, despite the intense
interest and the volume of research and publications on the subject there is still no
universally agreed definition of quality. It seems as though every commentator
contributing to the quality literature has their own definition of what quality is.
Because of this it is useful to consider quality by a generalisation of definitions,
Reeves and Bednar (1994) evaluate four definitions of quality:
1. Excellence
2. Value
3. Conformance to specifications
4. Meeting and/or exceeding expectations
Considering the wide scope of the subject area and the difficulties encountered while
trying to define quality, the history and development of quality is difficult to represent
concisely and completely. This section will attempt to cover the main areas which
had an impact on quality theory and practice leading up to six sigma. When
considering quality theory the so called “Quality Guru’s” theories are generally
agreed to form the foundation of the modern day understanding of quality. Although
it is understood that there is much disagreement over who is a guru and who is not,
there is general a consensus over the gurus discussed in this section (Bendel et al
1995), (Bendel 1991), (Martinez-Lorente et al 1998), (Nwabueze 2001), (Goldman
2005), (Sanderson 1995), (Flood 1993), (Kruger 2001), (Hoyer and Hoyer 2001),
(Dale et al 2001).
8

15. 2.1.1 W. A Shewhart:
Seen by many as the father of modern quality, Shewhart based his work on statistical
methods and is considered the founder of Statistical Process Control (SPC) as he
pioneered the use of control charts to statistically analyse processes. He believed that
quality standards should be defined in terms of quantitatively measurable product
characteristics; he also had consideration for customer satisfaction and value received
for the price paid. His definition of quality considers quality to be both subjective and
objective where the subjective side refers to what the customer wants and the
objective side refers to aspects of the product which are separate to what the customer
wants. As six sigma has a customer focus and is heavily based in statistical tools it
could be argued that Shewhart’s work is hugely important to six sigma and may
represent the foundations of the methodology. Shewhart also considered value for the
price paid to be critically important and as six sigma focuses on bottom line savings
as well as improving product quality, in this way it could be argued that six sigma is a
methodology which attempts to create quality as described by Shewhart.
2.1.2 W. E Deming:
Deming worked closely with Shewhart and is considered by many to have popularised
many of Shewhart’s teachings. Deming’s definition of quality is not concisely stated
but can be considered as including the following: Quality must be defined in terms of
customer satisfaction. Quality is multidimensional and complex and cannot be
defined by a single characteristic. Deming emphasised variability and the difference
between special causes and common causes. Special causes of variation can be seen
as those which prevent constant performance in a statistical sense, which can be
attributed to operation of the process. Common causes can be thought of as those
which are inherent in the process and can only be changed by management, for
9

16. example the equipment used. The underlying theory of six sigma is not significantly
different to this concept, improvements can be made by identifying and eliminating
special cause variation and subsequent improvements must involve changes in design
of the process to minimise the amount of common cause variation. Six sigma uses a
tool known as Design For Six Sigma (DFSS) when attempting to optimise the design
of a process, this will be discussed later. Deming encouraged the Japanese to
implement a systematic approach to problem solving by using the PDCA (Plan Do
Check Act) cycle. As discussed previously, the DMAIC method of structured
problem solving used in six sigma could be considered a close relative of the PDCA
cycle. Deming encouraged a top down approach to quality which required senior
management to become actively involved in their companies quality improvement
programmes. This is considered a critical success factor when implementing a six
sigma program and will be discussed in more detail later.
2.1.3 Joseph M. Juran:
Juran proposed that a practical definition of quality was probably not possible and
therefore he defined quality as “fitness for use”. In this way it seems like he tries to
encompass customer requirements (use) as well as conformance to measurable
product characteristics or specifications (fitness). Juran focused on planning,
organisational issues, management’s responsibility for quality and the need to set
targets for improvement. Juran believed that quality does not happen by accident, it
must be planned for and he proposed the “quality trilogy” of quality planning, quality
control and quality improvement as critical aspects. Juran also introduced a four point
formula to attain results:
1. Establish specific goals to be reached
2. Establish plans for reaching them
10

17. 3. Assign clear responsibility for meeting the goals
4. Base rewards on results achieved
Again the basis of a six sigma approach can be seen as similar to Juran’s beliefs in
that goal focused planning and responsibilities are a major part of its employment.
Bonuses in many six sigma companies are tied to the financial results achieved by
their projects which can be seen as the fourth point in Juran’s plan for reaching goals.
2.1.4 Armind V. Feigenbaum:
Feigenbaum is seen as the creator of total quality control. His theory outlined a
systematic approach to quality which involved all staff equally trying to build quality
in to the product rather than trying to inspect out bad quality. He believed that quality
must be defined by customer satisfaction and therefore it is a dynamic entity which
must change along with changing customer expectations or desires. Feigenbaums
theories can be seen as forming part of the organisational side of six sigma. His belief
that every worker must strive to build quality into the product and process by taking a
proactive approach rather than trying to inspect out bad quality is also a key
component of six sigma theory.
2.1.5 Karou Ishikawa:
Known as a pioneer of the Quality Circle movement in Japan in the early 1960’s,
Ishikawa attempted to make statistical techniques such as control charts, scatter
diagrams, binomial probability and sampling inspection more accessible to those
working in industry. He emphasised good data collection and presentation, the use of
Pareto charts to prioritise quality improvement projects and the use of cause and
effect diagrams (fishbone or Ishikawa diagrams) for finding, solving and documenting
the causes of variations in quality. He described quality control as including company
wide participation from top management all the way down to lower ranking
11

18. employees, all departments should be involved and all should study statistical
methods. He proposed that quality control concepts and methods should be used for
problem solving and analysis in all areas of the business; and that internal and
external audits should be carried out to ensure that this is actually taking place. When
considering the impact of Ishikawa’s work to modern day six sigma, it is easily seen
that all of his teachings above are still the basic building blocks of a six sigma
program. The idea that statistical thinking should be used to solve problems in all
areas of the business, and that statistical and problem solving techniques should be
made accessible to workers involved in improvement initiatives is a core principle of
six sigma and the same tools Ishikawa recommended are still used in six sigma
programs today.
2.1.6 Genichi Taguchi:
In the early 1970’s Taguchi developed the concept of the Quality Loss Function
which is defined as the loss imparted by the product to society from the time the
product is shipped. The loss function shows that a reduction in variability from a
target value leads to a decrease in loss and therefore an increase in quality. This idea
of reducing variation to increase quality has been described earlier as the reason why
six sigma exists and is key to any six sigma program. Taguchi’s methodology
included routine optimisation of product and process prior to manufacture as opposed
to the achievement of quality through inspection. Design For Six Sigma (DFSS) is
used for the same purpose and could be seen as stemming from Taguchi’s work.
Taguchi methodology is basically a method for identifying optimal conditions for
consistently producing a robust product which satisfies the customer requirements.
Taguchi methods can be used to identify variables which are critical to quality and
therefore identify areas to improve quality. Statistical process control can then be used
12

19. to keep quality characteristics on target. This is also know as the signal to noise ratio
and can be used to choose the control setting that minimises the sensitivity to noise.
In this way the Taguchi method is often seen as the forerunner to the Design Of
Experiments (DOE) methodology applied in six sigma which will be discussed later.
2.1.7 Philip B. Crosby:
Crosby is probably best known for the concepts of “Do it right first time” and “Zero
Defects”. He defines quality as conformance to the requirements that the company
has established for its products based directly on its customers needs. Crosby
believed that all staff should be given training for the tools of quality improvement so
prevention of bad quality can take place in every area. He believed that all work
should be viewed as a process or series of actions to produce the desired output. In
this way process models could be used to ensure clear requirements have been defined
and are understood by the supplier and the customer both internally and externally.
By examining Crosby’s theories on quality it can again be seen that the fundamentals
of six sigma have a lot in common with the views of the quality Gurus. In his case,
3.14 DPMO is seen by many as practically zero defects. His process view of quality
is again in line with the view of quality taught by six sigma practitioners, with the
belief that prevention of bad quality by improving the processes that create the
outputs is the key to good quality. Six sigma projects are measured by financial
metrics which is again in line with Crosby’s belief that the measurement of quality
must be price.
2.1.8 Landmarks on the road to Six Sigma
The contributions of the guru’s discussed above have definitely had an influence on
shaping how we view quality today and therefore how Six sigma came into existence.
But they are only a minute portion of the major theorists and key events which led to
13

20. our modern day quality understanding and methodologies. There are various differing
theories regarding the evolution of quality present in the literature but there is some
basic agreement amongst certain commentators (Garvin 1988), (Dahlgaard et al
1998), (Dahlgaard 1999), (Bregman et al 1994) that the main landmarks can be seen
as:
1. Inspection
2. Statistical Quality Control
3. Quality Assurance
4. Total Quality Management or Strategic Quality
It can be argued that Six sigma is not vastly different to TQM in theory but it lays
down a plan to be followed or a “road map” which shows companies what structure
must be set up, the training it requires and which tools should be used in certain
situations which will all ultimately lead to the desired result of improving quality,
reducing waste and increasing profits. These four landmarks represent a major
summarisation of the events and theories which have led to our current understanding
of quality, this is directly linked to six sigma’s core beliefs and many of the tools it
uses. Martinez-Lorente et al 1998, outline some of the major events which have
shaped modern day quality thinking and this is included as Table 4 below.
14

21. Table 4: Major events in the development of modern day quality theory (Martinez-
Lorente et al 1998)
15

22. 2.2 The Origins of Six Sigma
Although it has been shown in the previous section that six sigma is firmly rooted in
the theories and practices used in other quality initiatives such as TQM, it can still be
thought of as a separate entity and its own roots can be firmly traced back to
Motorola, “six sigma” is a registered trademark of Motorola. As outlined by Harry
and Schroeder (2000), six sigma gained recognition and popularity when Motorola
won the Malcolm Baldrige national quality award in 1988. The credit for coining the
phrase “six sigma” is given to Bill Smith who was an engineer at Motorola’s
communications sector. Smith wrote a paper in 1985 which concluded that products
which were found defective but were repaired during the production process were
frequently the subject of early customer complaints. Conversely, products that were
produced right first time were rarely the subject of such early customer complaints.
This sparked a debate within Motorola and eventually led to the adoption of a
proactive approach to quality by focusing on process optimisation. Six sigma was
applied to various processes and within the first four years it saved Motorola 2.2
billion dollars (Harry and Schroeder, 2000). Motorola’s CEO at the time, Bob Galvin
was determined to improve quality. When Galvin read a paper written by Mikel
Harry, a senior staff engineer at Motorola’s government electronics group entitled
“The strategic vision for accelerating six sigma within Motorola”, he realised its
potential and decided to make achieving six sigma a blue chip for the company. In
1990 Galvin asked Harry to start up the six sigma research institute in Illinois in
conjunction with various other companies including IBM and Kodak. These events
represent the birth of six sigma as a realistic business strategy. The work done by
Harry and his colleagues in Illinois laid the ground rules which are still followed by
six sigma practitioners today. Kumar and Gupta (1993) describe the implementation
16

23. of a TQM system at Motorola’s Austin assembly plant and this outlines the early
implementation of six sigma. The importance of SPC and DOE are outlined in this
paper as well as a focus on working in teams with assigned roles and responsibilities
(early version of the belt system, although not stated), providing appropriate statistical
and problem solving training, setting targets, assigning responsibilities, justifying
costs and documenting results. The cultural resistance to the change is also discussed
and this paper represents an ideal vantage point for considering the early
implementation of six sigma.
Works cited in this chapter:
• Bendell, T, (1991), “The Quality gurus: What can they do for your business?”,
London: The Department of Trade and Industry, HMSO
• Bendell, T, Penson, R and Carr, S, (1995), “The quality gurus-their
approaches described and considered”, Managing service quality, Vol 5, No 6,
pp 44-48
• Bregman, B and Klefsjo, B, (1994), “Quality, from customer needs to
customer satisfaction” London, McGraw-Hill
• Dahlgaard, J, Kristensen, K and Kanji, G (1998), “Fundamentals of Total
Quality Management”, London: Chapman & Hall
• Dahlgaardd, S, (1999), “The evolution patterns of quality management: some
reflections on the quality movement”, Total Quality Management, Vol 10, No
4, pp. 473-480
• Dale, B, Wu, P, Zairi, M, Williams, A, Van Der Wiele, T, (2001), “Total
quality management and theory: An exploratory study of contribution”, Total
Quality Management, Vol 12, No 4, pp 439-449
• Garvin, D, (1998), “Managing Quality: The Strategic and Competitive Edge”,
The Free Press, New York
• Goldman, H, (2005), “The origins and development of quality initiatives in
American business”, The TQM magazine Vol 17, No 3, pp 217-225
• Harry, M and Schroeder, R, (2000), “Six sigma: The breakthrough
management strategy revolutionizing the world’s top corporations”, New
York: Doubleday
17

24. • Hoyer, R, and Hoyer, B (2001), “What is Quality?”, Quality Progress, July,
pp 53-62
• Kruger, V, (2001), “Main schools of TQM “the big 5””, The TQM magazine,
Vol 13, No 3, pp 146-155
• Kumar S and Gupta Y, (1993) “Statistical process control at Motorola’s
Austin assembly plant”, The Institute of Management Sciences, Interfaces Vol
23, No 2, pp 84-92
• Martinez-Lorente, A, Dewhurst, F and Dale, B, (1998) “Total Quality
management: Origins and evolution of the term”, The TQM magazine, Vol 10,
No 5, pp 378-386
• Nwabueze, U, (2001), “How the mighty have fallen: the naked truth about
TQM”, Managerial Auditing Journal, Vol 16, No 9, pp 504-513
• Reeves, C and Bednar, D, (1994) “Defining Quality: Alternatives and
implications”, Academy of management review, Vol 19, No 3, pp 419-445
• Sanderson, M, (1995), “Future developments in total quality management-
what can we learn from the past?”, The TQM magazine, Vol 7, No 3, pp 28-31
18

25. Chapter 3: Six Sigma Tools
Six sigma’s main focus is achieving results that affect the bottom line by reducing
variation, increasing efficiency, optimising processes and augmenting profits. But
how is it possible to achieve such results on a practical level? As was discussed in the
brief history of quality, there have been many theories and practical solutions
discovered over the years for achieving these goals. Six sigma brings tools together
from a variety of fields such as quality engineering, problem solving, process analysis
and industrial statistics under its banner to achieve its goals. Due to the non prejudice
nature of six sigma’s tool selection almost any useful scientific tool can be used to
achieve its goals and only a selection of the more popular tools will be discussed in
this section. It should also be noted that applying six sigma is not a quick fix solution,
nor is it suitable to every situation. It is necessary to have an understanding of each
tool, how it works, its strengths and its limitations before deciding if it is a suitable
approach to take for the problem at hand. Inappropriate application of tools can do
more harm than good and is a danger that cannot be understated. De Koning and De
Mast (2006) compiled a list of commonly used tools including what phase of DMAIC
they are generally used in and this is included as Appendix 1.
3.1 Define Measure Analyse Improve Control (DMAIC):
Known as the breakthrough methodology, DMAIC is arguably the most commonly
used six sigma method and is at the heart of the six sigma mentality, an overview can
be seen in Figure. There are many variations of the basic idea present in the literature
De Koning et al (2005) conducted a study of some of the more popular renditions and
their research claims that the main points of each stage are as follows:
19

26. Define:
This step should include an examination of the rationale behind a six sigma project
including the impact it will have on processes and customer satisfaction as well as any
other benefits. In order to achieve this, the customer requirements must be defined.
The problem that must be solved should also be rigorously defined in this stage as
failure to accurately define the problem will jeopardise the project before it begins.
Measure:
The basic purpose of this stage is to convert the problem definition into some
measurable form. In this stage the critical to quality (CTQ) characteristics should be
identified for the output of the process being examined. These are characteristics
which represent the voice of the customer. The capability of the measurement system
to consistently measure the CTQ’s with the desired accuracy must be verified (see
measurement system analysis below). The current output of the process should be
examined to determine the baseline defect rate and determine realistic targets for
improvement. The process should be accurately mapped and the short term and long
term process capability should be determined. An unreliable or unproven
measurement method can again put the project in jeopardy and carrying on a project
that is generating false data can lead to implementation of erroneous and dangerous
changes to live processes.
Analyse:
This phase involves analysing the data collected in the measure phase in order to
discover the root causes of defects; and what factors impact the CTQ’s as well as to
determine what relationship these factors have with the output of the process. In this
way the root causes of defects should be highlighted and key process variables which
cause defects should be revealed. The key product performance data should be
20

27. benchmarked against the best in class. A gap analysis can then be performed to
determine what areas require improvement in order to be considered best in class. It
is important that the right analysis is made by fully trained and experienced
professionals as a faulty interpretation of the data could again lead to implementing
the wrong changes which could cause more harm than good.
Improve:
This stage involves the design and implementation of changes to the process which
will have a positive effect on the CTQ’s and will therefore reduce variability and
defect rates. The consequences of a change to live systems should be thoroughly
analysed and validated before making the change to avoid unwanted complications.
Control:
The main concern of this phase is the control of the process once the desired process
capability and output quality have been achieved. It is imperative that a reliable
system is put in place to maintain any improvements which have been made.
Figure3: Overview of DMAIC method (Cheng 2008)
21

28. 3.2 Measurement system analysis (MSA):
The ability to measure the quality of your product is of major importance in order to
provide customer satisfaction. Even if you can determine what your customer wants
you will not be able to consistently provide it unless you have confidence in your
measurement system. The first step in a six sigma implementation is quite often an
analysis of the ability to accurately measure the product characteristics that require
optimisation. The methodology used to determine the fitness of measurement systems
is known as measurement system analysis (MSA). MSA is carried out as a gage study
by separating the variation due to measuring equipment (repeatability) from the
variation due to operator bias (reproducibility). Multiple measurement systems can be
used to measure the same output to discover the optimum system relative to the
desired range of control. Once a suitable measurement system has been discovered
experimentation on the process can be carried out which will provide results that can
be analysed with confidence to discover where and how improvements can be made.
In order to obtain valid results the study must be carefully planned and randomised
and representative samples must be obtained to guarantee that accurate conclusions
are drawn about the measurement systems capabilities (Raisinghani et al 2005).
3.3 Process control:
Process control is crucial to consistently producing outputs that meet the
specifications laid down to represent customer satisfaction. A control system
highlights when a process is producing outputs which are deviating from the process
optimum. In this way it acts as a warning system, highlighting shifts in the process
before product quality is compromised. Statistical process control (SPC) can be used
as a method to achieve this goal. As mentioned earlier Dr Walter Shewhart pioneered
the use of control charts, where the output of a process is measured and charted with
22

29. an upper limit of +3 standard deviations from the process mean and a lower limit of -3
standard deviations from the process mean based on a normal distribution. Product
control requires product specifications for critical to quality characteristics of the
product which are based on customer requirements. Products with characteristics
outside the specification are deemed unacceptable to customers and therefore are
scrapped or reworked. Process control is unrelated to the product specifications; it is
based on the capability of the production process itself. This involves measuring the
output of a process under normal conditions over many runs. After sufficient data has
been collected (at least 30 runs) the mean and standard deviation are calculated.
Limits of + 3 and – 3 standard deviations from the mean are put on the process, all
runs are measured against these limits as opposed to the specification limits. If output
measurements are outside the control chart limits then something in the process has
changed and must be corrected before product quality is affected, in other words
before the process output drifts beyond the product specification as seen in Figure 4.
Periodic checks on the process must be carried out to ensure the limits remain suitable
to the desired output, especially if customer expectations change as the process could
still be under control but the output may be unacceptable to the customer (Raisinghani
et al 2005).
Figure 4: Differentiation between specification limits and actual process variation
(McAdam et al 2004)
23

30. Statistical control charts as used in SPC are used to: quantify variation in the process
being “controlled”, center the process around the desired mean value for a given
product characteristic being measured, monitor processes in real time and to help
decide when it is necessary to adjust the process to prevent defects. There are many
different types of control charts each suited to different types of processes but all
charts can be categorised as either variable (continuous data) or attribute (discrete
data). Some examples include: the and R chart, the and S chart, XmR chart, the
p chart, the np chart, the c chart and the u chart, see Rooney et al 2009 for more
detail.
3.4 Design for Six Sigma (DFSS):
DFSS is used to design processes which can produce products that will meet customer
expectations by being capable of working at six sigma quality levels. It is applied in
the early stages of product development and has a customer and process focus, its
goal is to maximise quality and reduce the chance of defects occurring during routine
manufacture (Kwak and Anbari 2006). The process itself involves applying
qualitative and quantitative tools to identify and measure key performance indicators
which when controlled will allow the process to be optimised in terms of quality, cost
and time. Although powerful, DFSS can be difficult to implement and the creation of
accurate mathematical models to predict future performance can often be very
challenging. Like all tools it should first be considered if DFSS is suitable and an
appropriate use of resources for any given process before its implementation. Figure
5 below shows a flow diagram of the process including some useful tools that can be
used in the DFSS process.
24

31. Figure 5: Flow diagram of a DFSS process (Kwak and Anbari 2006)
3.5 Design of experiments (DOE):
This method is used for the optimisation of complex processes which have numerous
independent inputs which may interact with each other. DOE can be used to analyse
the output and determine how it is affected by changes to the various inputs. When
analysing a complex system the traditional method of one factor at a time (OFAT)
will rarely succeed as it ignores the interactions between the various factors. DOE
attempts to discover all possibilities and the end product of a successful DOE is a
mathematical model that can accurately predict the output characteristics given any
combination of input variables. Typical of six sigma, this involves rigorous analysis
of the process characteristics and all input characteristics. Process mapping and
25

32. analysis of variance (ANOVA) are used to determine the significance of each factor
and produce the mathematical model which will be used to optimise the process and
also to trouble shoot any deviations which may occur during normal operation. It
must be stated that this approach may not be successful in all situations. The correct
statistical approach for the conditions of the experiment can be difficult to find, if it
exists at all. It is suggested that in order to have confidence in the DOE it should be
tested with known truths when designing the model to confirm its accuracy (Deaconu
and Coleman 2000) (Raisinghani et al 2005).
3.6 Failure Mode and Effects Analysis (FMEA)
The purpose of FMEA is to predict problems before they occur and proactively
improve processes in order to prevent detrimental effects to the product/process
output from such problems occurring. To carry out FMEA for any process; a group
of all the stakeholders must be determined and a representative from each group
should be brought together to discuss potential problems at every stage of the process.
The group will start with a process map and/or a design schematic for any relevant
tools/devices. The process is carefully examined to identify any possibilities which
may harm the product at every stage of the process. A relative priority number (RPN)
is assigned to each activity depending on the severity of the failure, the possibility of
the failure occurring and the ability to detect it. If the RPN is high, usually 120, 60
for a six sigma organisation then corrective actions must be taken to reduce the
magnitude of the RPN and therefore reduce the risk of detrimental effects on the
product at that stage of the process. The corrective action may be a designed
experiment to optimise an area of the process or it may require purchasing of new
equipment. A detailed FMEA may require a weekly meeting of stakeholder
representatives for 6 months but the benefits of such a thorough approach are usually
26

33. seen in reduced defect rates and improved trouble shooting due to a deep
understanding of the process (Raisinghani et al 2005). It is essential that all team
members have a deep understanding of FMEA development and that the correct
inputs are identified or an inadequate and inaccurate FMEA could be the result. It is
also imperative that RPN’s represent the reality of the situation or the FMEA could
again be ineffective.
3.7 Capability analysis:
The process capability (Cpk/Cp) indices are often used to measure a process’ ability
to produce outputs which conform to specifications in order to determine if a process
is capable of producing products with six sigma quality. Process capability is a
measure of how much variation there is in the process in relation to its specifications.
It can be used when discussing quality levels internally and also with key suppliers
and customers. As can be seen from Table 5 below, if the Cpk is below a certain level
then the process will not be capable to produce quality at a sigma level higher than the
corresponding level in the table. In order for a process to be able to operate at a six
sigma level of quality it must have a Cpk of 2 (Raisinghani et al 2005).
Table 5: Process Cpk index values versus sigma values (Raisinghani et al 2005)
3.8 Histogram:
A histogram is a graphical display of frequencies present in a tabulated data set. It
can be used to clearly show the number of occurrences for each different category in a
given data set. It is similar to a bar chart in appearance but it differs when the
27

34. categories are represented by bars of differing width as it is the area of the bar which
provides the value in a histogram rather than just the height as in a bar chart. As a
fairly simple method to use it can be applied to provide a relatively quick insight into
any major messages present in the data. The histogram can be used to gain an early
insight into the data set before moving on to further analysis and is commonly used in
six sigma programs to analyse data sets, but only superficial information can be
determined, such as the distribution of the data etc. (Rooney et al 2009).
3.9 Pareto chart:
In the 1950’s Juran was involved in popularising the theories of an Italian economist
named Vilfredo Pareto and Juran coined the phrase “The vital few”. The main focus
was on the Pareto principle, also known as the 80-20 rule, which states that in any
situation or set of variables; a small number of factors will have the greatest effect.
For example, 80% of a company’s revenue will most likely come from only 20% of
its products. A Pareto chart is used to graphically separate the vital few areas which
should be focused on to achieve the greatest rewards; from the trivial many which will
not provide as impressive gains should they be improved upon. A Pareto chart is a
good place to start when trying to decide what areas should be the focus of an
improvement project and Pareto charts are commonly used by six sigma practitioners.
The chart itself is similar to a bar chart, but differs by sorting the bars so that the chart
displays the values from the highest to the lowest from left to right, the chart usually
includes a cumulative percentage line as shown in Figure 6 below. If the categories
represented in the first Pareto chart are complex categories then further Pareto
analysis can be performed on the major categories using stratification of individual
categories to highlight exactly where the focus should be placed to achieve the
greatest results for the effort required. The most difficult part of successfully using
28

35. this tool is creating meaningful and accurate categories. If the correct categories
cannot be identified then the resultant chart will be inaccurate and may cause teams to
focus efforts in the wrong places (Rooney et al 2009).
Figure 6: Example of a Pareto chart (Rooney et al 2009)
3.10 Cause and Effect diagram
Also known as the fishbone diagram or Ishikawa diagram after the man credited with
its development, Karou Ishikawa who allegedly first used the tool in 1943 (Rooney et
al 2009). Cause and effect diagrams can be used to analyse process deviations to find
the root cause by investigating the main causes and their sub causes which in turn
leads to the effect of interest, a certain deviation for example. The effect of interest,
usually a quality characteristic of the product is the focus of improvement and is
defined as Y in Figure 7 below, while the factors which could potentially impact the
quality characteristic, the process variables are defined as X. The key process
parameters: People, Material, Method, Equipment and Environment represent the
major areas where causes for variation may be present. These can be tailored to suit
the user’s specific process but the categories represented in Figure 7 are generally a
good place to start. Using the diagram facilitates a better understanding of
interrelationships that may exist within the process that might otherwise be difficult to
identify and the diagram can also be used to provide a good structure and method of
29

36. documentation for brainstorming sessions such as the cross functional sessions
required in a FMEA project (Rooney et al 2009). The major limitation of this tool
can be the people who are using it as it greatly depends on inherent skills of team
members to identify and understand causes, sub-causes and interrelationships present.
Figure 7: Cause and effect diagram (Rooney et al 2009)
3.11 Scatter Plots:
Scatter plots can be used to determine if there is a potential relationship between two
sets of data using a graph to visually represent the data sets. It widely used by six
sigma practitioners due to its simplicity of use yet powerful nature. When
constructing the graph the independent variable (Temperature in Figure 8 below) is
plotted on the x-axis and the dependent variable (Defects in Figure 8 below) is plotted
on the y-axis. If there appears to be a relationship such as a sloped or curved line on
the graph then there more than likely is a relationship between the data. If the points
are randomly distributed or “scattered” then more than likely there is no relationship
between the data. From examining Figure 8 below it could be proposed that the
number of defects increases as the temperature increases. Proving that there is a
relationship between two sets of data is a good place to start, but usually further
investigation is necessary to determine the causes of the relationship and the effects of
interrelationships between factors and with other factors if present (Rooney et al
2009). Caution should be taken not to make inaccurate assumptions about the true
30

37. relationship between the data as damage to processes and loss of business could be
caused when implementing changes based on inaccurate assumptions.
Figure 8: Scatter Plot of Defects versus Temperature (Rooney et al 2009)
3.12 Statistical analysis:
One of the most widely known aspects of six sigma is its use of statistics to drive
process improvements in a data based way. There are numerous statistical methods
which can be applied to various situations and in the modern industrial environment it
is easier to make use of them due to powerful and relatively easy to use statistical
software packages such as minitab for example. The most important thing is to
understand what method is applicable to a given situation or set of data and the
accurate interpretation of the results obtained. It should be noted that like all six
sigma tools, there will be many situations which may not suitable for this approach
and if used inappropriately inaccurate theories and ineffective or damaging changes to
processes may be the result. Some of the more common methods and their uses are
included below as Table 6.
31

38. Table 6: Examples of commonly used statistical tools and their use (Henderson et al
2000)
Works cited in this chapter:
• De Koning, H, and De Mast, J, (2006), “A rational reconstruction of six-
sigma’s breakthrough cookbook”, International Journal of Quality and
Reliability Management, Vol 23, No 7, pp 766-787
• Deaconu, S, and Coleman, H, (2000), “Limitations of statistical Design of
experiments approaches in engineering testing”, J. Fluids Eng. Vol. 122, No.
2, pp 254-260
• Henderson, K, and Evans, J, (2000), “Successful implementation of six sigma:
benchmarking General Electric Company”, Benchmarking: an International
Journal, Vol 7, No 4, pp 260-281
• Kwak, Y, and Anbari F, (2006) “Benefits, obstacles and future of six sigma
approach” Technovation 26, pp 707-715
• McAdam, R, and Lafferty, B, (2004), “A multilevel case study critique of six
sigma: statistical control or strategic change?”, International Journal of
operations and production management, Vol 24, No 5, pp 530-549
32

39. • Raisinghani, M, Ette, H, Pierce, R, Cannon, G and Daripaly, P, (2005), “Six
sigma: concepts tools and applications”, Industrial management and data
systems, Vol 105, No 4, pp 491-505
• Rooney, J, Kubiak, T, Westcott, R, Reid, R, Wagoner, K, Pylipow, P and
Plesk, P, (2009), “Building from the basics: Master these quality tools and do
your job better”, Quality Progress, January 2009, online content only
33

40. Chapter 4: Implementing Six Sigma
This section will attempt to discuss some of the more important aspects involved in
attempting to apply six sigma in an industrial setting and some critical success factors
will be discussed. The decision to embrace a six sigma program should not be taken
lightly and may not suit every business. Before beginning a six sigma program a
thorough examination of expectations and a realistic assessment of the current
situation of the business as well as its goals and mission statement should be
undertaken. If an existing quality framework exists within the business then decisions
must be made about how it will interact with the new program, or if the new six sigma
program will replace the old program then there must be a consideration of exactly
how the transition will be made. Each business must make its quality program fit its
unique needs so a “one size fits all” solution is probably not possible and although
many businesses use six sigma, a closer inspection reveals some differences between
each company’s “version” of six sigma, see Table 7 below. However six sigma
provides a good foundation for a quality system to be built upon and can be made to
fit individual business goals by placing emphasis on the desired areas of importance
for that business and then selecting the appropriate tools and methodologies from the
six sigma tool kit to deliver the goals decided upon.
34

41. Table 7: Different emphasis of six sigma in various companies (Motwani et al 2004)
4.1 Six Sigma critical success factors
Due to the highly customised nature of quality systems as discussed above, it is
difficult to determine an exhaustive list of success factors. Given the widespread use
of six sigma across various business sectors from pure manufacturing to pure service,
finance, healthcare etc. the difficulties of defining an all encompassing list of critical
success factors becomes even more difficult and there is widespread disagreement in
the literature regarding the success of six sigma when applied to services. In the
authors opinion the difficulties encountered while applying six sigma to services are
most likely a result of the intangible nature of services and the relatively new attempts
to control quality levels when compared to manufacturing based industries, where
many solutions to inherent quality problems had already been defined and addressed
before six sigma was conceived. When applying six sigma in a non-manufacturing
setting it is critically important that the process is well understood and can be
35

42. measured accurately. Metrics must be carefully selected and tools should be
appropriate to the context of the processes involved. The progress of six sigma
application in this area will require further advances in understanding service quality
as well as its measurement and control. However, there has been some progress made
in the literature towards defining some critical success factors of implementing six
sigma. Nonthaleerak and Hendry 2008, investigated the progress made in this area
and suggested that the factors in Table 8 can be considered as a complete list.
Table 8: Critical success factors for six sigma implementation (Nonthaleerak and
Hendry 2008)
4.1.1 Management involvement and commitment
This is arguably the most important factor in successful six sigma implementations
and in the major success stories such as Allied Signal, General Electric and Motorola
the involvement of the CEO in each case is seen as one of the main reasons for its
success. Six sigma should be part of ever employee’s daily work including top and
middle level managers and all managers should be taught the underlying principles.
Management should be involved in the creation of the process management system
and they should be involved in six sigma projects themselves to encourage buy in
36

43. from the rest of the workforce and to show the importance of the six sigma program
(Coronado and Antony 2002), (Antony and Banuelas 2002).
4.1.2 Cultural change
The implementation of a new management and working structure in any organisation
is a major undertaking and can be met with resistance from the workforce. The
implementation of a six sigma program can involve a substantial change in
organisational infrastructure and this change can be met with fear of the unknown and
resistance from the work force. Eckes (2000) suggests four main causes of such
resistance:
1. Technical
2. Political
3. Organisational
4. Individual
It is crucial that the workforce understand the need for change and accept the new
method as the way forward. In six sigma programs workers must take on
responsibility for the quality of their own work, defects must be highlighted as
opportunities for improvement and workers must be made feel comfortable to
highlight defects without fear. It has been seen by examining some companies who
successfully managed large scale organisational changes that it is vital to increase
communication, training and motivation of the workforce throughout the transition in
order to overcome cultural resistance to change (Coronado and Antony 2002),
(Antony and Banuelas 2002).
4.1.3 Communication
A communication plan must be made with the goal of educating the workforce so they
understand why the change is necessary as well as how the new philosophy will help
37

44. improve the business. It is good practice to publish results of all six sigma projects to
highlight success stories and also problems which have been met in order to avoid the
same problems being met by other projects and also to earn the trust of the workforce
through open and honest communication (Coronado and Antony 2002), (Antony and
Banuelas 2002).
4.1.4 Organisational infrastructure
It is essential to have the correct infrastructure in place to support the six sigma
program. There must be sufficient training for the individuals who have been selected
to lead the six sigma program, as well as top management support, efficient
communication methods, teamwork and financial backing. The individuals selected
to lead the six sigma implementation are the members of the six sigma belt system
(Coronado and Antony 2002), (Antony and Banuelas 2002), (Ho et al 2008):
• The champion is a high level director of the six sigma program
• Master black belts mentor six sigma teams and report to the champion
• Black belts run six sigma projects, mentor green belts and report to the master
black belt
• Green belts carry out small scale six sigma projects and work with black belts
on larger projects
Once the organisation has set up the belt system as shown in Figure 9 and its members
are fully trained, the teams can be set up to start six sigma projects. It is advisable to
start with the projects that can be easily completed but will provide relatively large
return on investment in order to gain buy in from the workforce by showing the
benefits of the six sigma approach.
38

45. Figure 9: Six sigma belt system structure (Ho et al 2008)
4.1.5 Training
Training is obviously of critical importance to the success of any six sigma
implementation. An effective training system allows workers to feel more
comfortable with their new roles and also helps them buy in to the program through
learning and using new skills to tackle improvement opportunities and produce results
to the bottom line. The belt system as described above and shown in Figure 9 must be
rolled out throughout the organisation starting at the very top with CEO’s and top
level management before being cascaded down throughout the rest of the
organisational levels. The details of the training system differ from organisation to
organisation as well as from the various consultancy firms, but the members of the
belt system should be seen as the change agents within the organisation and their
training is critically important as they will spread this training to the rest of the
organisation over time until the entire workforce is educated in six the sigma
philosophy, especially the operators of processes which are the subject of
39

46. improvement projects as they have the greatest knowledge of the process they work
on. Table 9 shows a comparison of the various roles in the belt system with regard to
their job profile, role, training necessary and the recommended numbers within the
organisation (Coronado and Antony 2002), (Antony and Banuelas 2002).
Table 9: Work role versus training profile for six sigma implementation (Coronado
and Antony 2002)
4.1.6 Linking six sigma to business strategy
For every six sigma project the link to business strategy must be stated and proven,
each project should have a target of process or product improvement that will directly
impact the operational or financial goals of the organisation. Goals should be stated
in financial terms whenever possible and return on investment should be analysed for
each project to determine if the potential benefits outweigh the cost of the project
before the project is approved. Six sigma should not be treated as a stand alone set of
tools for quality improvement, it must be at the heart of the business culture to reduce
variability and improve quality and therefore customer satisfaction (Coronado and
Antony 2002), (Antony and Banuelas 2002).
40

47. 4.1.7 Linking six sigma to customer
All six sigma projects should commence with a determination of customer
requirements to ensure that they are customer focused. It is vitally important that the
critical to quality aspects (CTQ) defined are actually what the customer wants. In
some cases this is difficult to determine, especially in service industries. Tools such
as quality function deployment can be used to aid the determination of CTQ’s which
will then be quantitatively defined and used as a baseline for improvement through
the six sigma project (Coronado and Antony 2002), (Antony and Banuelas 2002).
4.1.8 Linking six sigma to Human Resources
Human resource policies should be put in place which will encourage the workforce
to internalise and actively participate in the six sigma initiative. Coronado and
Antony (2002) state that “Some studies show that 61% of the top performing
companies link their rewards to their business strategies, while lower-performing
companies create minimal linkage”. There are many ways in which this can be
carried out for example by tying bonuses to successful completion of six sigma
projects or by requiring that full six sigma training and completion of at least one
project for any promotion as was the case under Jack Welch CEO of General Electric
(Antony and Banuelas 2002).
4.1.9 Linking six sigma to suppliers
It has been stated in the literature that many organisations have found it beneficial to
include their suppliers in their six sigma program. By selecting a fewer number of
suppliers and working with them to reach six sigma levels of quality, the variability of
the total process can be reduced by improving the quality of the inputs. The key is to
gain buy in from top management of the supplier (Coronado and Antony 2002),
(Antony and Banuelas 2002).
41

48. 4.1.10 Understanding the tools and techniques of six sigma
Six sigma training can be divided into three main areas:
1. Team tools
2. Process tools
3. Leadership tools
As stated earlier, because each business will have its own individual needs and each
process will have its own specific requirements, a “one size fits all” set of tools and
techniques is probably not possible. Such a tool set would be vastly over complicated
for most situations. The sheer volume of tools available can cause much confusion
and if not properly understood the wrong tool can be applied and can do more harm
than good so caution is necessary before any action is taken. The critical aspect is to
understand what tools and techniques are suitable to your own business and which
tools should be used in certain situations. This knowledge will only come with
training and a deep understanding of six sigma theory and application and each
business should have its own customised tool set suited to its internal processes
(Coronado and Antony 2002), (Antony and Banuelas 2002).
4.1.11 Project Management skills
Since six sigma is mainly a project based approach it is critical that team leaders have
a good level of project management skills. If projects are managed poorly, they are
unlikely to succeed. All team members should consider and define the major areas of
each project, cost, time and quality. In this way the team should determine and
document the scope of the project and they can then attempt to deliver the goals
decided upon in the shortest time possible for the lowest cost possible (Coronado and
Antony 2002), (Antony and Banuelas 2002).
42

49. 4.1.12 Project Prioritisation and selection
Again, six sigma is mainly a project driven program so the selection of the right
projects that will provide the most benefit in a suitable time for an acceptable cost to
resources, be it monetary or man hours is of paramount importance. If projects are
poorly selected then it is unlikely that the business will gain the most benefit from
them if any at all. To avoid poor selection of projects there must be some criteria for
selecting projects and a tracking system put in place to monitor each suggestion and
its progress. Selection criteria will be specific to each business but a general
guideline criteria could be:
1. Benefits to the business: Customer requirements, financial etc.
2. Project feasibility: resources required/available, project complexity etc.
3. Organisational impact, for example cross functional and learning benefits
Each project should be reviewed by black belts and master black belts on a regular
basis reinforcing six sigma methodology is being applied at all stages. Champions
should keep regular communications with master black belts to find out what
obstacles are being faced to complete the projects and what changes can be made to
facilitate their timely completion (Coronado and Antony 2002), (Antony and
Banuelas 2002).
References cited in this chapter:
• Antony, J, and Banuelas, R, (2002), “Key ingredients for the successful
implementation of Six sigma program”, Measuring Business excellence, Vol.
6, No. 4, pp 20-27
• Coronado, R, B, and Antony, J, (2002), “Critical success factors for the
successful implementation of six sigma projects in organisations”, The TQM
Magazine, Vol. 14, No 2. pp 92-99
• Eckes, G, (2000), “The six sigma revolution”, John Wiley and Sons, New
York, NY
43

50. • Ho, Y, Chang, O, and Wang, W, (2008), “An empirical study of key success
factors for six sigma green belt projects at an Asian MRO company”, Journal
of Air Transport Management, 14, pp 263-269
• Motwani, J, Kumar, A, Antony, J, (2004), “A business process change
framework for examining the implementation of six sigma: a case study of
Dow chemicals”, The TQM Magazine, Vol. 16, No 4. pp 273-283
• Nonthaleerak, P, and Hendry, L, (2008), “Exploring the six sigma
phenomenon using multiple case study evidence”, International Journal of
Operations and Production Management, Vol. 28, No. 3, pp 279-303
44

51. Conclusion
S ix sigma has come a long way since it first gained recognition and popularity when
Motorola won the Malcolm Baldrige national quality award in 1988. Today six sigma
can be many things depending on how it is applied. It can be the basis of a quality
management system or a driver of organisational culture change and continuous
business improvement. It has a large tool set, both quantitative and qualitative, drawn
from many different sources including quality engineering, problem solving,
marketing, industrial statistics etc. and although it makes use of statistical methods it
is more than “just a set of statistical tools”, which is a common misconception. As
discussed in chapter 2 the roots of six sigma can be firmly linked with the history of
quality and total quality management. Many of the tools used by six sigma have their
origins in TQM and the various quality movements of the past. It has been argued
that six sigma overcomes some of the problems inherent in TQM by defining
quantifiable goals and providing a framework for achieving them in the belt system
infrastructure and the associated roles and responsibilities of its members. In the
author’s opinion, it is irrelevant whether six sigma is vastly different to TQM, it is the
results that matter and six sigma has experienced both success and failures. A six
sigma program is generally introduced with the goal of reducing variation in all
processes to produce outputs with only 3.4 defects per million opportunities,
continually improving quality, reducing waste and increasing customer satisfaction
which ultimately leads to an increase in profits. However, it should not be seen as a
cure to every problem that a business may face and six sigma may not suit every
business. Six sigma implementation is a huge undertaking and if any of the critical
success factors discussed in chapter 4 are not carried out with the necessary
commitment and expertise then there is a danger that the program will fail and the
45

52. business will suffer as a result. It is critically important that the individuals who are
selected to use six sigma methodology are given sufficient training and are made
aware of the strengths and weaknesses of each tool, as well as what situations it is
suitable for and more importantly which situations it is not suitable for. In untrained
hands, application of six sigma tools can greatly damage a business by implementing
erroneous changes based on incorrect assumptions or unreliable data. In order to be
successful the critical success factors outlined in chapter 4 must be given due
attention and all employees must internalise the six sigma philosophy in order to reap
the potential rewards of reduced cycle times, reduced variation and defects, increased
quality levels, customer satisfaction and profits. In the authors opinion the decision to
embark on a six sigma implementation should not be taken lightly, but if it seems
suitable to the organisational goals and the necessary commitment, financial backing
and attention to the critical success factors is provided then the potential benefits of
success outweigh the risks of failure.
46

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56. Appendix 1: Six sigma Tools (De Koning and De Mast 2006)
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57. 51