1. Understanding Six Sigma-an Overview
ROHAN B. SAWANT
HPGD/JL14/0146
SPECIALIZATION - OPERATION
MANAGEMENT
WELINGKAR INSTITUTE OF MANAGEMENT
DEVELOPEMENT & RESEARCH
JUNE 2016

2. I have taken efforts in this project. However, it would not have been possible without
the kind support and help of many individuals and organizations. I would like to
extend my sincere thanks to all of them.
I am highly indebted to Mr. Zoheb Haq for their guidance and constant supervision
as well as for providing necessary information regarding the project & also for their
support in completing the project.
I would like to express my gratitude towards my Family & Team mates of BT India
Pvt Ltd, Mumbai for their kind co-operation and encouragement which help me in
completion of this project.
I would like to express my special gratitude and thanks to industry persons for giving
me such attention and time.
My thanks and appreciations also go to my colleague in developing the project and
people who have willingly helped me out with their abilities.
(Rohan Bramhaji Sawant)
Date: 04 June, 2016
Place: Mumbai

3. CERTIFICATE FROM THE GUIDE
This is to certify that the Project work titled Under Six
Sigma-An Overview is a bonafide work carried out by
Rohan Bramhaji Sawant (Roll No-HPGD/JL14/0146)a
candidate for the Post Graduate Diploma examination
of the Welingkar Institute of Management under my
guidance and direction.
SIGNATURE OF GUIDE
NAME:
DESIGNATION:
ADDRESS:
DATE:
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4. Preface
I take an opportunity to present this project report on "Understanding Six
Sigma-An Overview" and put the readers some useful information
regarding my project.
I have made sincere attempts and taken every care to present this
matter in precise and compact form, the language being simple as
possible.
I am sure that the information contained in this volume would certainly
prove useful for better insight in the scope and dimension of this project
in its true perspective.
The task of completion of project though being difficult was made quite
simple, interesting, and successful due to deep involvement and
complete dedication of my colleagues.

5. Table Contents
CHAPTER NO DESCRIPTION PAGE NO
1 INTRODUCTION 5
1.1 What is Six Sigma? 6
1.2 Evolution of Six Sigma 9
1.3 Overview of Sigma 10
2 Six Sigma Success Factors 11
2.1 General Respondent Information 12
2.2 Research Survey Results 14
1.3 Six Sigma Asserts 14
3.0 Difference between Lean Management & Six Sigma 15
3.1 Six Sigma Methodologies 16
3.2 DMAIC 16
3.3 DMADV 17
4.0 Six Sigma Significance 18
4.1 Short term Process Capability 19
4.2 Long term Process Capability 20
4.3 Cost of quality & Six Sigma 21
5.0 Total Quality through Six Sigma 22
5.1 Six Sigma V/s TQM 24
5.2 Six Sigma Infrastructure for Large Companies 25
5.3 Six Sigma for Small & Medium sized 31
5.4 Criticism about Six Sigma 35
6 Six Sigma Tools & Techniques 37
7 Conclusion 61
8 Bibliography 62

6. 1. Introduction:
1.1 What is Six Sigma?
Sigma is a Greek symbol represented by "σ".
The term “sigma” is used to designate the distribution or spread about the mean
(average) of any process or procedure.
For a process, the sigma capability (z-value) is a metric that indicates how well that
process is performing. The higher the sigma capability, the better. Sigma capability
measures the capability of the process to produce defect-free outputs. A defect is
anything that
results in customer dissatisfaction.
Six Sigma was developed by CEOs to create an infrastructure of change agents
throughout the organization. These change agents focus on strategic goals to
provide tangible benefits to major stake holder groups (customers, shareholders, and
employees). Six Sigma emphasizes breakthrough rates of improvement, and
focuses on world class performance to achieve organizational success.
Six Sigma is a set of techniques and tools for process improvement. It was
introduced by engineer Bill Smith while working at Motorola in 1986.Jack Welch
made it central to his business strategy at General Electric in 1995. Today, it is used
in many industrial sectors.
Six Sigma seeks to improve the quality of the output of a process by identifying and
removing the causes of defects and minimizing variability in manufacturing and
business processes. It uses a set of quality management methods, mainly empirical,
statistical methods, and creates a special infrastructure of people within the
organization, who are experts in these methods. Each Six Sigma project carried out
within an organization follows a defined sequence of steps and has specific value
targets, for example: reduce process cycle time, reduce pollution, reduce costs,
increase customer satisfaction, and increase profits.
The term Six Sigma originated from terminology 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 all
opportunities to produce some feature of a part are statistically expected to be free of
defects (3.4 defective features per million opportunities). Motorola set a goal of "six
sigma" for all of its manufacturing operations, and this goal became a by-word for the
management and engineering practices used to achieve it.

7. 1.2 The Evolution of Six Sigma
Before, January 15, 1987, Six Sigma was solely a statistical term. Since then, the Six
Sigma crusade, which began at Motorola, has spread to other companies who are
continually striving for excellence. While it is progressing, it has extended and
evolved from a problem-solving technique to a quality strategy and ultimately into a
sophisticated quality philosophy. However, this unique philosophy only became well
known after GE’s Jack Welch made it a central focus of his business strategy in
1995. Today, Six Sigma is the fastest growing business management system in
industry.
To elaborate the evolution of Six Sigma, one Six Sigma authority has to be
introduced: Mikel Harry, who is called the “godfather” of Six Sigma and is
acknowledged as the leading authority on theory and practice. Even though he did
not invent the concept, the way that it is currently practiced bears the unmistakable
marks of Harry’s personality and personal history. Harry's history path is followed
here to reveal the evolution of Six Sigma.
The evolution began in the late 1970s, when a Japanese firm took over a Motorola
factory that manufactured television sets in the United States and the Japanese
promptly set about making drastic changes to the way the factory operated. Under
Japanese management, the factory was soon producing TV sets with 1/20th the
number of defects they had produced under Motorola management. Finally, Motorola
recognized its quality was awful. Since then. Motorola management decided to take
quality seriously. When Bob Galvin became Motorola's CEO in 1981, he challenged
his company to achieve a tenfold improvement in performance over a five-year
period.
In 1984, after Harry was awarded a doctorate from Arizona State University, he
joined Motorola where he worked with Bill Smith, a veteran engineer who was in
Mikel Harry's words, “the father of Six Sigma”. During 1985, Smith wrote an internal
quality research report which caught the attention of Bob Galvin. Smith discovered
the correlation between how well a product did in its field life and how much rework
had been required during the manufacturing process. He also found that products
that were built with fewer nonconformities were the ones that performed the best
after delivery to the customer. Although Motorola executives agreed with Smith's
supposition, the challenge then became how to create practical ways to eliminate the
defects. With the concept of “logic filter”, one of Harry's papers at Arizona State
University, together with Smith, Harry developed a four-stage problem-solving
approach: Measure, Analyse, Improve, and Control (MAIC). Later, the MAIC
discipline became the road map for achieving Six Sigma quality.
On January 15, 1987, Galvin launched a long term quality program, called “The Six
Sigma Quality Program”. The program was a corporate program which established
Six Sigma as the required capability level to approach the standard of 3.4 DPMO.
This new standard was to be used in everything, that is, in products, processes,
services and administration. The Corporate Policy Committee of Motorola then
updated their quality goal as follows:

8. “Improve product and service quality ten times by 1989, and at least one hundred
fold by 1991. Achieve Six Sigma capability by 1992. With a deep sense of urgency,
Galvin spread dedication to quality to every facet of the corporation, and achieve a
culture of continual improvement to assure Total Customer Satisfaction. There is
only one ultimate goal: zero defects in everything we do.”
The revised corporate quality goal stated that everyone was responsible for and to
each other regarding this objective. In addition, it affirmed that no one could assume
she or he had done enough until the entire goal of Six Sigma was achieved
company-wide. After implementing Six Sigma, in 1988, Motorola was among the first
recipients of the Malcolm Baldrige National Quality Award. Since then, Six Sigma
has constantly caught the attention of industry. However, at Motorola, Six Sigma was
only a disciplined problem-solving methodology.
In 1988, at Unisys Corp. Harry discussed with Cliff Ames, one of Unisys’ plant
managers, about how to leverage the Six Sigma technique throughout an
organization and how to recognize the people who were equipped with Six Sigma
tools. Since Ames was a lover of karate and Harry himself was a martial arts
enthusiast, in some respects, they shared the same eastern martial arts philosophy.
People in martial arts are incredibly skilled, have a precise command of tools, are
very dedicated, and are very humble to learn. Based on this insight, Harry decided to
designate those with Six Sigma skills as “Black Belt”.
In 1989, Galvin invited Harry to head up Motorola's Six Sigma Research Institute and
challenged him to do “short cycle quality knowledge transfer and rapid dissemination
of quality knowledge into a world-wide company”. Harry answered the challenge with
Six Sigma implementation strategy that attempted to put quality tools into the hands
of large numbers of workers and managers throughout the organization. From that
moment, Six Sigma skills were not solely owned by quality engineers, but began to
transfer from the quality department to the entire organization.
In 1993, at Asea Brown Boveri (ABB), Harry teamed with Richard Schroeder who
later joined him to found Six Sigma Academy. Inspired by Kjell Magnuson, one of
ABB’s business unit presidents, Harry realized that high level executives only
focused on clear and quantifiable gains. Further, Harry recognized that it should not
be quality first, but business first which will lead to the realization of quality. In
addition, from his Marine Corps experience, he understood the importance of tactics.
To exploit the full power of Six Sigma by focusing on bottom-line results, Harry
refined Six Sigma deployment tactics which included: Champion, Master Black Belt,
Black Belt, and Green Belt.
At that time, enamoured by Motorola's success, several other companies, such as
Texas Instruments, began a similar pursuit. But, it wasn't until late 1993 that Six
Sigma really began to transform business. That's the year that Harry and Schroeder
moved to Allied Signal and its CEO, Larry Bossidy, decided to adopt Six Sigma.
By adequately selecting the right Six Sigma projects and promptly providing the right
support for them, Bossidy suggested that high level executives should also
understand Six Sigma tools. To respond to that, Harry developed a methodology for
a leadership team to select high financial leverage projects. At Allied Signal, an

9. entire system of leadership and support systems began to form around the statistical
problem solving tools of Six Sigma.
Not long after Allied Signal began its pursuit of Six Sigma quality, Jack Welch, then
Chairman and CEO of General Electric, influenced by Bossidy, then began to get
interested in Six Sigma. In fact, before Six Sigma, according to Welch, neither he nor
Bossidy quality enthusiasts. They felt the earlier quality programs were too heavy on
slogans and light on results. In June 1995, Welch invited Bossidy to attend GE’s
Corporate Executive Council meeting and share his experience with Six Sigma. After
that meeting, GE conducted a cost-benefit analysis on Six Sigma implementation.
The analysis showed that if GE, then running at three to four sigma quality level,
were to raise its quality to six Sigma, the cost saving opportunity was somewhere
between $7 billion and $10 billion. This amounted to a huge number - 10 to 15
percent of sales.
Then, in January 1996, teaming with Six Sigma Academy, Welch announced the
launch of Six Sigma at GE. At that time, he called Six Sigma the most ambitious
undertaking the company had ever taken on. He stated: “Quality can truly change
GE from one of the great companies to absolutely the greatest company in world
business.” Needless to say that when GE does something, it does it all the way.
Welch said to GE’s Corporate Executives: “Everyone in this room must lead the
quality charge. There can be no spectators on this. What took Motorola ten years,
we must do in five - not through shortcuts, but in learning from others”. From that
moment, Jack Welch became the global promoter of Six Sigma.
There are two important contributions from GE’s way of implementation to the
evolution of Six Sigma. First, Welch demonstrated the great paradigm of leadership.
Second, Welch backed the Six Sigma program up with a strong rewards system to
show his commitment to it. GE changed its incentive compensation plan for the
entire company so that 60 percent of the bonus was based on financials and 40
percent on Six Sigma results. The new system successfully attracted GE employees’
attentions to Six Sigma. Moreover, Six Sigma training had become a prerequisite for
advancement up GE’s corporate ladder. Welch insisted that no one would be
considered for a management job without at least a Green Belt training by the end of
1998.

10. 1.3. Overview of Six Sigma
Six Sigma is one of the most popular quality methods lately. It is the rating that
signifies “best in class”, with only 3.4 defects per million units or operations (DPMO).
Its concept works and results in remarkable and tangible quality improvements when
implemented wisely. Today, Six Sigma processes are being executed in a vast array
of organizations and in a wide variety of functions.
Fueled by its success at large companies such as Motorola, General Electric, Sony,
and Allied Signal, the methodology is proving to be much more than just a quality
initiative. Why are these large companies embracing Six Sigma? What makes this
methodology different from the others?
The goal of Six Sigma is not to achieve six sigma levels of quality, but to improve
profitability. Prior to Six Sigma, improvements brought about by quality programs,
such as Total Quality Management (TQM) and ISO 9000, usually had no visible
impact on a company's net income. In general, the consequences of immeasurable
improvement and invisible impact caused these quality programs gradually to
become the fad of the moment.
In 1891, British physicist Lord Kelvin wrote, “When you can measure what you are
speaking about, and express it in numbers, you know something about it.” Mikel
Harry, a noted Six Sigma authority, extends the thought as, “we don't know what we
don’t know; we can’t act on what we don’t know; we won’t know until we search; we
won’t search for what we don’t question; we don’t question what we don’t measure.”
Both imply that if you failed to quantify the results of what you were doing, in a way, it
means that you might not understand what you were really doing.
Hence, organizations that are unable to track the impact of quality improvements on
profitability can not know what changes need to be made to improve their profit
margins. And most importantly, profitability is the natural concern of management in
organizations. If a quality initiative failed to present its quantitative bottom-line value
to the management, it will lose the management's commitment to it and, eventually,
fade away.
In contrast with other quality initiatives, Six Sigma recognizes that there is a direct
correlation between the number of product defects, wasted operating costs, and the
level of customer satisfaction. In the short term, Six Sigma is a method to eliminate
defects and the opportunity for defects. It utilizes a statistical unit of measurement to
measure the capability of the process, then achieve defect free performance, and
ultimately increase the bottom-line and customer satisfaction.

11. 2. Six Sigma Success Factors
 What makes Six Sigma work?
 What are the factors contributing to its successes at big corporations?
 How can it be implemented to secure the bottom-line results that Six
Sigma promises?
These are the most frequent questions being asked by the potential Six Sigma
followers. But, there is no easy answer to each one of them. Simply because driving
a business toward Six Sigma is not a one-time effort; it is about producing products
and services that continue to meet customer and market requirements. This requires
organizational agility and constant vigilance to changes in the marketplace. Thus, the
real challenge with Six Sigma is getting to the point where one can meaningfully
measure a business' current performance against dynamic customer requirements
while developing the internal organizational abilities to response to changing
marketplace conditions. Doing this well means aligning organizational components
inside the company (leadership, strategy, people, and technology) to give Six Sigma
efforts the momentum and staying power they need to succeed.
Most recently, one of PQA's Associates, Dr. John McLellan has done his doctorate
thesis on the Six Sigma success factors and their integration into the Management
Control structure for the organization. For more information on this Six Sigma
Doctoral thesis, or to obtain a copy,
PQA has also written a PQA Whitepaper on management commitment for Six
Sigma, and the typical and PQA recommended method of Six Sigma
implementation.
There is limited literature to reveal the success factors for Six Sigma implementation.
However, one doctoral candidate, Tsung-Ling Chang, performed a research survey
which identifies ten Critical Success Factors for implementing a Six Sigma quality
system. Based on Chang’s research findings, Process Quality Associates has
developed a Six Sigma implementation framework.
Following is a summary of Chang’s research survey:

12. 2.1 General Respondents Information:
The samples were mainly selected from two groups of Six Sigma professionals: the
members of the International Society of Six Sigma Professionals and the trainees of
a Six Sigma consulting firm. A total of 345 questionnaires were originally e-mailed
with 21 being returned for e-mail address discrepancies. The resulting sample size
was 324. There were 106 returned.
In addition, 25 of the respondent companies (24%) had implemented all 10
initiatives listed and 74% of them had implemented over 6 of the 10 initiatives listed.
It indicates that their Six Sigma Quality Management Systems (QMS) are quite
advanced and mature. It also implies that companies, which adopted Six Sigma
programs required a much higher maturity of their quality management systems.
However, it is unusual to find that only 69.2% of the companies were IS09000
certified and about 20.6% of them did not have any industry certification. In general,
about 95.2% of them indicated that the implementation of quality initiatives had a
positive impact on company business operations.
2.2 Research Survey Results:
The respondents were asked to indicate their level of agreement with 53 statements
about their Six Sigma QMS on a scale of “1: strongly disagree” to “7: strongly agree”.
After the verification through the hypotheses examinations, the proposed 10 factors:
strategic planning, competitive benchmarking, leadership, process management,
human resource development, education and training, quality tools, information and
analysis, supplier management, and customer management, have been confirmed to
be critical to the success of Six Sigma QMS.
Chang also asked the respondents to name the factors they considered to be the
five (5) most critical ones for implementing a Six Sigma QMS.
A summary of the mean value of responses for each critical success factor, and the
Top 5 rankings, are presented in below table.

13. Critical Success factors for a Six Sigma QMS
Critical Success Factor (CSF) Mean % of SMEs
Placing CSF
in TOP 5
(Rank in
Brackets)
1. Top managementleadership & commitment
are essential to Six Sigma QMS success.Top
management should act as key driver in continuous
improvements,communicate to employeesabout
organizational goals, and establish an environment
for supporting organizational & employee learning.
5.99 95.9%
(1)
2. A well-implementedcustomer management
system is critical to Six Sigma QMS success.
Processesneed to be established in order to
monitor customersatisfaction levels, to receive
customerfeedback,and to resolve customer
concerns.
5.92 74.0%
(2)
3. The education and training system should
provide continuous courses to employees for
equipping them with quality-related knowledge and
problem-solving skills.
5.64 71.2%
(3)
4. A well-organizedinformationand analysis
system should be designed to collectthe
performance measures in order to monitor the
quality of key business processes.
5.58 30.1%
(8)
5. A well-implementedprocessmanagement
system that identifies,improves,and monitors the
key business processeshas a positive impact on Six
Sigma QMS success.The quality assurance system,
work unit performance measures,and statistical
techniques are essential to process management.
5.57 34.2%
(7)

14. 6. A well-developed strategic planning system
must translate into executable action plans with
related performance measurements.The necessary
human & financial resources mustbe allocated to
supportthe implementationof business action plans.
5.53 61.6%
(4)
7. A well-developed suppliermanagement
system,where the main criteria for selecting
suppliers is based on quality aspects,has a positive
impact on Six Sigma QMS success.Processesneed
to be built in order to monitor the quality
performance levels of suppliers.
5.50 39.7%
(6)
8. Throughout the organization, from management
to employees, equipping all with quality tools has
a positive impact on Six Sigma QMS success.
Quality tools should be used in productionand non-
productionprocesses.
5.46 61.6%
(4)
9. A well-developed human resource
managementsystem has a positive impact on Six
Sigma QMS success.A job advancement system is
important to human resource development.Various
methods are developedto facilitate the
communication between the organization and its
employees.To promptlyimprove performance,
employees needto receive their performance
feedbackfrom their supervisors.
5.22 17.8%
(9)
10. A well-developed competitive benchmarking
system should be capable of collecting market and
competitors’information. The process of
benchmarking information collectionneeds to be
evaluated to ensure its effectiveness.
4.95 13.7%
(10)
2.3 Six Sigma asserts:

15. • Continuous efforts to achieve stable and predictable process results (e.g. by
reducing process variation) are of vital importance to business success.
• Manufacturing and business processes have characteristics that can be
defined, 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.
• An increased emphasis on strong and passionate management leadership
and support.
• A clear commitment to making decisions on the basis of verifiable data and
statistical methods, rather than assumptions and guesswork.
The term "six sigma" comes from statistics and is used in statistical quality control,
which evaluates process capability. Originally, it referred to the ability of
manufacturing processes to produce a very high proportion of output within
specification. Processes that operate with "six sigma quality" over the short term are
assumed to produce long-term defect levels below 3.4 defects per million
opportunities (DPMO). Six Sigma's implicit goal is to improve all processes, but not
to the 3.4 DPMO level necessarily. Organizations need to determine an appropriate
sigma level for each of their most important processes and strive to achieve these.
As a result of this goal, it is incumbent on management of the organization to
prioritize areas of improvement.
"Six Sigma" was registered June 11, 1991 as U.S. Service Mark 1,647,704. In 2005
Motorola attributed over US$17 billion in savings to Six Sigma.
Other early adopters of Six Sigma include Honeywell (today's Honeywell is the result
of a "merger of equals" of Honeywell and Allied Signal in 1999) and General Electric,
where Jack Welch introduced the method. 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.
The International Organization for Standardization (ISO) has published in 2011 the
first standard "ISO 13053:2011" defining a Six Sigma process.[12] Other "standards"
are created mostly by universities or companies that have so-called first-party
certification programs for Six Sigma.

16. 3. Difference between Lean Management& Six
Sigma:
Lean management and Six Sigma are two concepts which share similar
methodologies and tools. Both programs are of Japanese origin, but they are two
different programs. Lean management is focused on eliminating waste and ensuring
swift while Six Sigma's focus is on eliminating defects and reducing variability.
3.1 Methodologies:
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.
• DMAIC (an acronym for Define, Measure, Analyze, Improve and Control)
(pronounced də-MAY-ick) refers to a data-driven improvement cycle used for
improving, optimizing and stabilizing business processes and designs.
The DMAIC improvement cycle is the core tool used to drive Six Sigma projects.
However, DMAIC is not exclusive to Six Sigma and can be used as the framework
for other improvement applications.
• DMADV an acronym for (Define, Measure, Analyze, Design and Verify)
pronounced as ("duh-mad-vee") is used for projects aimed at creating new product
or process designs.
It is used in many industries, like finance, marketing, basic engineering, process
industries, waste management, and electronics. It is based on the use of statistical
tools like linear regression and enables empirical research similar to that performed
in other fields, such as social science.

17. 3.2 DMAIC (Define, Measure, Analyze, Improve
and Control)
The DMAIC projectmethodologyhas five phases:
Define the system, the voice of the customerand their requirements,
and the project goals, specifically.
Measure key aspects of the current processand collectrelevant data;
calculate the 'as-is' ProcessCapability.
Analyze the data to investigate and verify cause-and-effectrelationships.
Determine what the relationships are, and attempt to ensure that all
factors have been considered.Seekout root cause of the defectunder
investigation.
Improve or optimize the current processbased upon data analysis using
techniques such as designof experiments,poka yoke or mistake
proofing,and standard work to create a new, future state process.Set
up pilot runs to establish processcapability.
Control the future state process to ensure that any deviations from the
target are corrected beforethey result in defects.Implementcontrol
systems such as statistical process control,production boards,visual
workplaces, and continuously monitor the process.

18. 3.3 DMADV(Design, Measure, Analyze, Design,
Verify)
DMADV aka DFSS Design for Six Sigma (DFSS) is a business-process
management method related to traditional Six Sigma. It is used in many industries,
like finance, marketing, basic engineering, process industries, waste management,
and electronics. It is based on the use of statistical tools like linear regression and
enables empirical research similar to that performed in other fields, such as social
science. While the tools and order used in Six Sigma require a process to be in place
and functioning, DFSS has the objective of determining the needs of customers and
the business, and driving those needs into the product solution so created. DFSS is
relevant for relatively simple items / systems. It is used for product or process design
in contrast with process improvement. Measurement is the most important part of
most Six Sigma or DFSS tools, but whereas in Six Sigma measurements are made
from an existing process, DFSS focuses on gaining a deep insight into customer
needs and using these to inform every design decision and trade-off.
There are different options for the implementation of DFSS. Unlike Six Sigma, which
is commonly driven via DMAIC (Define - Measure - Analyze - Improve - Control)
projects, DFSS has spawned a number of stepwise processes, all in the style of the
DMAIC procedure.
DMADV, define – measure – analyze – design – verify, is sometimes synonymously
referred to as DFSS, although alternatives such as IDOV (Identify, Design, Optimize,
and Verify) are also used. The traditional DMAIC Six Sigma process, as it is usually
practiced, which is focused on evolutionary and continuous improvement
manufacturing or service process development, usually occurs after initial system or
product design and development have been largely completed. DMAIC Six Sigma as
practiced is usually consumed with solving existing manufacturing or service process
problems and removal of the defects and variation associated with defects. It is clear
that manufacturing variations may impact product reliability. So, a clear link should
exist between reliability engineering and Six Sigma (quality). In contrast, DFSS (or
DMADV and IDOV) strives to generate a new process where none existed, or where
an existing process is deemed to be inadequate and in need of replacement. DFSS
aims to create a process with the end in mind of optimally building the efficiencies of
Six Sigma methodology into the process before implementation; traditional Six
Sigma seeks for continuous improvement after a process already exists.

19. 4. Significance about Six Sigma
The term sigma is a Greek alphabet letter (σ) used to describe variability. In Six
Sigma, the common measurement index is DPMO (Defects per Million Operations)
and can include anything from a component, piece of material, or line of code, to an
administrative form, time frame or distance. A sigma quality level offers an indicator
of how often defects are likely to occur, where a higher sigma quality level indicates
a process that is less likely to create defects. Consequently, as sigma level of quality
increases, product reliability +improves the need for testing and inspection
diminishes, work in progress declines, cycle time goes down, costs go down, and
customer satisfaction goes up.
To have a more comprehensive understanding about sigma quality level, it will be
explained from two perspectives of process capability: short-term and long-term
process capabilities.
4.1 Short-term process capability
A part or item is classified as defective if the desired measurement, denoted by X, is
outside the customer-supplier specification limit (USL) or lower specification limit
(LSL). In addition to specifying the USL and LSL, a customer would also specify a
target value, which typically is the midpoint between the USL and LSL. From a short-
term process capability view, after sampling data from the process, a six sigma
process that produces the parts is normally distributed (see below Figure). Below
Table displays short-term process capability in various sigma levels.
Short-Term Process Capability at Various Sigma Quality Levels
Sigma Level % Good PPM/DPMO
2 95.45 45500
3 99.73 2700
4 99.9937 63
5 99.999943 0.57
6 99.9999998 0.002

20. Short-Term Six Sigma Performance for a Single Process Graph

21. 4.2 Long-term process capability
Due to the nature of the process, when dealing with the situation of a long-term
process, shifts and drifts in the mean of the distribution of a component value occur
for a number of reasons as do changes in other parameters of the distribution: for
example, tool wear is one source of a gradual drift, differences in raw material or
change of suppliers can cause shifts in the distribution. A solution proposed by D.H.
Evans (Statistical Tolerance: The State of the Art Part III, Shifts and Drifts 1975)
focuses on high production rates, and low cost components. Evans suggests that
one should use 1.5s as the standard deviation to calculate the percentage of out of
tolerance responses. Furthermore, research by M.J. Harry (The Nature of Six Sigma
Quality 1988) has shown that a typical process is likely to deviate from its natural
centring condition by approximately 1.5 standard deviations at any given moment in
time. With this principle in hand, one can make a rational estimate of the long-term
process capability with knowledge of only the short-term process capability.
Long-Term Six Sigma Performance for a Single Process (Shifted 1.5σ)

22. Table below displays long-term process capability in various sigma levels.
Long-Term Process Capability in Various sigma Levels
Sigma Level % Good PPM/DPMO
2 69.15 308,537
3 93.32 66,807
4 99.379 6,210
5 99.9676 233
6 99.99966 3.4
4.3. Cost of Quality and Six Sigma
How can one correlate the quality level with a company's bottom-line?
Most of the quality initiatives cannot answer this question, but Six Sigma can. Six
Sigma is a process of asking questions that lead to tangible and quantifiable
answers that ultimately produce profitable results.
There are four groups of quality costs:
 External failure cost: warranty claims, service cost
 Internal failure cost: the costs of labour, material associated with scrapped
parts and rework
 Cost of appraisal and inspection: these are materials for samples, test
equipment, inspection labour cost, quality audits, etc.
 Cost related to improving poor quality: quality planning, process planning,
process control, and training.

23. Typical North American companies' average Sigma level is around 3
Sigma. In other words, 25-40% of most company's annual revenue gets
chewed up by their costof quality. Thus, if a companycan improve its
quality by 1 sigma level, its net income will increase hugely,
approximately 10 percent net income improvement.
Table below showsSigma Quality Leveland Related Cost of Quality
Sigma
Level
% Good PPM/DPMO Cost of Quality
as % of Sales
2 95.45 45500 Over 40%
3 99.73 2700 25 - 40%
4 99.9937 63 15 - 25%
5 99.999943 0.57 5 - 15%
6 99.9999998 0.002 Less than 1%
Furthermore, when the level of process complexityincreases (eg. output
of one sub-process feeds the input of another sub-process),the rolled
throughput yield of the process will decrease,then the final outgoing
quality level will decline, and the costof quality will increase.
For example, if a company satisfies its single process yield with 93.32%
of good,3 sigma level, it may end up with an unacceptable final yield
which represents a very high costof quality.

24. 5. Total Quality through Six Sigma
Some argue that many of the tools Six Sigma uses are not new. However, while Six
Sigma uses conventional methods, its application is anything but conventional.
Instead it stresses the importance of searching for a new way of thinking and doing.
In fact, Six Sigma defines a clear road map to achieve Total Quality:
1. Leadership Commitment: Top management not only initiates Six
Sigma deployment, it also plays an active role in the whole deployment cycle.
Six Sigma starts by providing senior leadership with training in the principles
and tools it needs to direct the development of a management infrastructure
to support Six Sigma. This involves reducing the levels of organizational
hierarchy and removing procedural barriers to experimentation and change.
2. Customer Focus: Systems are developed for establishing close
communications with “external customers” (direct customers, end-users,
suppliers, regulatory bodies, etc), and with internal customers (employees).
From upstream suppliers to ultimate end-users, Six Sigma eliminates the
opportunities for defects.
3. Strategic Deployment: Six Sigma targets a small number of high-
financial leveraged items. It focuses the company’s resources: right support,
right people, right project, and right tools, on identifying and improving
performance metrics that relate to bottom-line success.
4. Integrated Infrastructure: The Leadership Team defines and
reviews project progress. The Champion acts as a political leader and
removes the barriers for the project team. The Master Black Belt acts as a
technical coach and provides in-depth knowledge of quality tools. The Black
Belt controls the project while the Green Belt supports the Black Belt -
together they form the Six Sigma Project Teams. In addition, the incentive and
recognition systems motivate the project teams to achieve the business goals.

25. 5. Disciplined Framework: Six Sigma projects are implemented using
the Define, Measure, Analyze, Improve and Control disciplined road map.
This DMAIC discipline sets up a clear protocol to facilitate internal
communication. In addition, from a business perspective, Six Sigma is also a
framework for continuous business improvement.
6. Education and Training: Six Sigma believes that true commitment
is driven by true understanding. As a fact-based methodology, it intensively
utilizes quality and statistical tools to transform a practical problem to a
practical solution. Thus, a top-to-bottom training is conducted in Six Sigma
philosophy and system improvement techniques for all levels.
In conclusion, Six Sigma’s approach and deployment makes it distinguishable from
other quality initiatives. The Six Sigma approach involves the use of statistical tools
within a structured methodology for gaining the knowledge needed to achieve better,
faster, and less expensive products and services than the competition.
The repeated, disciplined application of the master strategy on project after project,
where the projects are selected based on key business objectives, is what drives
dollars to the bottom line, resulting in impressive profits.
Moreover, fuelled by the bottom line improvement, top management will continuously
be committed to this approach, the work culture will be constantly nurtured,
customers will definitely be satisfied, and Total Quality will ultimately be achieved.

26. 5.1 Six Sigma VS. Total QualityManagement
(TQM)
In some aspects of quality improvement, TQM and Six Sigma share the same
philosophy of how to assist organizations to accomplish Total Quality. They both
emphasize the importance of top-management support and leadership. Both
approaches make it clear that continuous quality improvement is critical to long-term
business success. However, why has the popularity of TQM waned while Six
Sigma's popularity continues to grow in the past decade?
T. Pyzdek (Why Six Sigma is Not TQM, 2001) stated that the primary difference is
management. Unlike TQM, Six Sigma was not developed by technicians who only
dabbled in management and therefore produced only broad guidelines for
management to follow. The Six Sigma way of implementation was created by some
of America's most gifted CEOs - people like Motorola's Bob Galvin, Allied Signal's
Larry Bossidy, and GE's Jack Welch. These people had a single goal in mind:
making their businesses as successful as possible. Once they were convinced that
tools and techniques of Six Sigma could help them do this, they developed a
framework to make it happen.
The differences between TQM and Six Sigma are summarized in Table below.
TQM vs. Six Sigma Table
TQM Six Sigma
A functional specialty within the
organization.
An infrastructure of dedicated change
agents. Focuses on cross-functional
value delivery streams rather than
functional division of labour.
Focuses on quality. Focuses on strategic goals and applies
them to cost, schedule and other key
business metrics.
Motivated by quality idealism. Driven by tangible benefit for a major
stockholder group (customers,
shareholders, and employees).
Loosely monitors progress toward
goals.
Ensures that the investment produces
the expected return.
People are engaged in routine
duties (Planning, improvement,
and control).
“Slack” resources are created to change
key business processes and the
organization itself.

27. Emphasizes problem solving. Emphasizes breakthrough rates of
improvement.
Focuses on standard
performance, e.g. ISO 9000.
Focuses on world class performance,
e.g., 3.4 PPM error rate.
Quality is a permanent, full-time
job. Career path is in the quality
profession.
Six Sigma job is temporary. Six Sigma is
a stepping-stone; career path leads
elsewhere.
Provides a vast set of tools and
techniques with no clear
framework for using them
effectively.
Provides a selected subset of tools and
techniques and a clearly defined
framework for using them to achieve
results (DMAIC).
Goals are developed by quality
department based on quality
criteria and the assumption that
what is good for quality is good for
the organization.
Goals flow down from customers and
senior leadership's strategic objectives.
Goals and metrics are reviewed at the
enterprise level to assure that local sub-
optimization does not occur.
Developed by technical
personnel.
Developed by CEOs.
Focuses on long-term results.
Expected payoff is not well-
defined.
Six Sigma looks for a mix of short-term
and long-term results, as dictated by
business demands.

28. 5.2. Six Sigma Infrastructure for Large Companies
For successfully implementing Six Sigma, a complete and well-connected
infrastructure is necessary. The infrastructure includes: Core Team, Master Black
Belt, Black Belt, Green Belt, Yellow Belt, MAIC discipline, and an incentive system.
The Core Team defines and reviews Six Sigma projects progress, and acts as the
political leader, removing the barriers for the project teams.
 The Master Black Belt acts as a technical coach and provides the knowledge
of quality tools for the project team. There is typically one Master Black Belt
for every 1,000 employees.
 The Black Belt controls the project. There are typically 10 to 20 Black Belts
per 1,000 employees.
 The Green Belt supports Black Belt. There are typically 3 to 5 Green Belts on
the Project Team with the Black Belt. There are typically 300 Green Belts per
1,000 employees.
 Yellow Belts are the balance of your population. They provide information
and support to the Six Sigma project teams, and are a source for future Green
Belts.
The DMAIC discipline sets up a clear protocol to expedite internal communication.
The incentive system facilitates Six Sigma projects to generate results.
The infrastructure and the necessary training programs for different levels are
described as follows:
 Core Team
Six Sigma involves changing major business value streams that cut across
organizational barriers. It is the means by which the organization's strategic goals
are to be achieved. Top management's commitment and involvement are critical to
Six Sigma implementation. Hence, the core team is formed by top management. Its
main responsibility is selecting high financial leverage projects, derived from the
organization's strategic plan. While the projects are progressing, the team regularly
reviews the projects. To understand the Six Sigma approach, a two-day (leadership)
training program could use as a foundation selected topics from the list below:
 Six Sigma overview and implementation
 Knowledge-centered activity focus and process improvement
 Overview of descriptive statistics and experimentation
 Understanding the 10 Six Sigma Success Factors and how to deploy them
throughout your organization
Master Black Belts: This is the highest level of technical and organizational
proficiency. They provide technical leadership of the Six Sigma program and ensure
business is self-sustaining in training. They are in-house experts in Six Sigma tools
and methodology. Their roles are:

29.  Coach and support projects for results.
 Develop and deliver Six Sigma training.
 Assist in project identification.
 Partner with Six Sigma Champions.
 Identify and deploy best practices.
Two one-week Master Black Belt training sessions can involve the expansion of
topics or addition of other related topics not included in the normal Black Belt
training. The training of Master Black Belts can also involve the critique of their
training of Black Belts. The training prescription in advanced quality and statistical
thinking.
Black Belts: Black Belts are change agents for institutionalizing the Six Sigma
improvements and methodology. Their roles are:
 Lead strategic and high impact process improvement projects.
 Master basic and advanced quality tools and statistics.
 Deploy techniques of measurement, analysis, improvement and control.
An effective approach to the training of the Six Sigma concepts is the use of four
weekly modules spread over four months. Between workshop sessions, attendees
apply the concepts previously learned to their projects. During this time they also get
one-on-one coaching of the application of the techniques to their project.
Green Belts: They are technical process experts and change agents who work in
their own functional area. Their roles are:
 Lead important process improvement projects.
 Support strategic Black Belt projects.
 Drive continuous process improvement.
Green Belt training sessions that are two weeks long can include topics and
exercises as desired from the Black Belt's four-week training sessions.
Yellow Belts: They are the balance of your population. They are provided 3-days of
training so that they can understand and apply basic statistical concepts that are
used in problem solving. They provide support to the Six Sigma project team and
offer insights on root causes for the project team to investigate.

30. 5.3 Six Sigma for Small and Medium Sized Enterprises
(SME's)
 Six Sigma for SME's In designing a Six Sigma framework for Small and
Medium Sized Enterprises (SMEs), Process Quality Associates took into
account certain positive characteristics inherent in this business sector which
can speed up the structuring of an effective Six Sigma Quality Management
System (QMS) more than in large businesses, such as flexible process flows,
short decision-making chain, and high visibility of senior management, etc. On
the other hand, there are factors that can be disadvantageous, such as lack of
resources and expertise in change initiatives. To make the framework more
applicable and suitable for SMEs, the identified critical factors for a Six Sigma
QMS are encompassed and designed into the proposed framework (shown in
Figure below).
Six Sigma projects should be derived from your business' strategic plan which sets
an organization's goals and key performance measurements. Based on key
business goals, senior management defines the scope of each Six Sigma project
and organizes the right project team. While projects are progressing, they regularly
review the projects and promptly provide political, financial, and technical support.
After the projects are completed, senior management audits the projects' results,
establishes necessary systems to sustain the improvements, and continuously
adjusts the business strategic plans. More importantly, for managing a Six Sigma
quality system, each critical factor should be implemented through the mapping of
the Six Sigma MAIC (measure, analyze, improve, and control) discipline into its
routine processes.

31. 1. Measure the existing systems. Identify and describe the potential critical
processes/products. Establish valid and reliable metrics to help monitor
progress towards the project goals.
2. Analyze the system to identify ways to eliminate the gap between the current
performance of the system or process and the desired goal.
3. Improve the process performance. Each process is modified and the outcome
is measured to determine whether the revised method produces results within
customer expectations.
4. Control the new system. Institutionalize the improved system by modifying
policies, procedures, operating instructions, and other management systems.
Further, each critical factor should also be continuously improved by transforming its
management into different Six Sigma projects. The following explains how to apply
the Six Sigma MAIC discipline into each individual critical factor of your Six Sigma
QMS.
 MEASURE
1 Leadership Management provides personal leadership and commitment for quality
improvement
2 Customer Focus Marketing/Sales and customer satisfaction information is used to target
potential market segments and customers
3 Education & Training Education system balances short-term and long-term organizational and
employee needs
4 Information and
Analysis
The needs for quality tools in facilitating job performance are identified
5 Process Management Key business processes are identified, improved and monitored
6 Strategic Planning Establish a quality steering committee to develop short-term and long-
term strategic plans to ensure quality improvements
7 Supplier Management Key performance requirements are incorporated into suppliers' process
management
8 Quality Tools The needs for quality tools in facilitating job performance are identified
9 Human Resource
Development
A variety of methods are designed and used to measure employee
satisfaction
10 Competitive
Benchmarking
Marketing, new technology, and competitor benchmarking information is
obtained

32.  ANALYZE
1 Leadership Management communicates organizational policies and performance
expectations to employees
2 Customer Focus Complaints received from customers are aggregated and analyzed for
use in overall organizational improvement
3 Education & Training Management and employees are trained to obtain problem-solving skills,
and equipped with quality-related knowledge
4 Information and
Analysis
Analysis results of measurables are linked to work units and functional-
level operations
5 Process Management Project related training system is in place
Statistical techniques are used to reduce variance in processes
6 Strategic Planning Strategic plans are translated into executable action plans for all
business units
Measures are developed to evaluate the performance of each action
plan
7 Supplier Management Working with suppliers towards long-term partnerships
Suppliers selected on the basis of quality aspects
8 Quality Tools Training on quality tools is provided to management and employees
9 Human Resource
Development
Recruitment plan is aligned with strategic plan
10 Competitive
Benchmarking
Benchmarking information is analyzed and used to identify strategic
opportunities
 IMPROVE
1 Leadership Management acts as key driver in continuous improvement
Management regularly reviews quality performance measures
2 Customer Focus Processes are established to ensure customers’ complaints are
effectively resolved
Follow up with customers on recent transactions is undertaken in
order to receive prompt feedback
3 Education & Training Knowledge/skill sharing system is established across work units
Continuous learning is provided through education & training
4 Information and
Analysis
Integrated performance information is provided to management to
review overall organizational performance
The accessibility and utilization of information systems are improved

33. 5 Process Management Systems and procedures for quality assurance are implemented
6 Strategic Planning Allocate human and financial resources to accomplish action plans
A recognition/reward system based on quality performance is
established so as to facilitate attainment of the business objectives
7 Supplier Management Suppliers are actively involved in quality improvement activities
Supplier performance audit and evaluation are important activities to
be conducted
8 Quality Tools Quality tools are used in production and non-production related
functions for improvement activities
9 Human Resource
Development
Job advancement system is provided
Communication methods (such as newsletter, meetings) are
implemented
Work environment is conducive to the well-being of all employees
10 Competitive
Benchmarking
Benchmarking information is used to drive improvement
 CONTROL
1 Leadership Management audits the execution of results of each action plan
2 Customer Focus Customer satisfaction levels are measured and controlled
3 Education & Training The performance and process of the training systems are evaluated by
management
4 Information and Analysis Information analysis results are used to monitor improvement activities
5 Process Management Work unit performance measures are identified and used to control and
evaluate the improvement process
6 Strategic Planning Define performance measurements for tracking progress relative to
action plans
7 Supplier Management Suppliers’ quality performance levels are measured and monitored
8 Quality Tools Quality tools are used in management processes
9 Human Resource
Development
The measures for employee performance are clearly defined and have
been communicated with employees
10 Competitive
Benchmarking
The process for selecting benchmarking information is evaluated

34. 5.4 Criticism on Six Sigma:
 Lack of originality: Quality control analyst Joseph M. Juran described Six
Sigma as "a basic version of quality improvement", stating that "there is
nothing new there. It includes what we used to call facilitators. They've
adopted more flamboyant terms, like belts with different colors. I think that
concept has merit to set apart, to create specialists who can be very helpful.
Again, that's not a new idea. The American Society for Quality long ago
established certificates, such as for reliability engineers."
 Role of consultants: The use of "Black Belts" as itinerant change agents has
fostered an industry of training and certification. Critics have argued there is
overselling of Six Sigma by too great a number of consulting firms, many of
which claim expertise in Six Sigma when they have only a rudimentary
understanding of the tools and techniques involved or the markets or
industries in which they are acting.
 Potential negative effects: A Fortune article stated that "of 58 large
companies that have announced Six Sigma programs, 91 percent have trailed
the S&P 500 since". The statement was attributed to "an analysis by Charles
Holland of consulting firm Quarto (which espouses a competing quality-
improvement process)".The summary of the article is that Six Sigma is
effective at what it is intended to do, but that it is "narrowly designed to fix an
existing process" and does not help in "coming up with new products or
disruptive technologies."
 Over-reliance on statistical tools: 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).The extensive
reliance on significance testing and use of multiple regression techniques
increases the risk of making commonly unknown types of statistical errors or
mistakes. A possible consequence of Six Sigma's array of P-value
misconceptions is the false belief that the probability of a conclusion being in
error can be calculated from the data in a single experiment without reference
to external evidence or the plausibility of the underlying mechanism.
One of the most serious but all-too-common misuses of inferential statistics is to take
a model that was developed through exploratory model building and subject it to the
same sorts of statistical tests that are used to validate a model that was specified in
advance.

35. Another comment refers to the often mentioned Transfer Function, which seems to
be a flawed theory if looked at in detail.[34] Since significance tests were first
popularized many objections have been voiced by prominent and respected
statisticians. The volume of criticism and rebuttal has filled books with language
seldom used in the scholarly debate of a dry subject. Much of the first criticism was
already published more than 40 years ago
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."
Nassim Nicholas Taleb consider risk managers little more than "blind users" of
statistical tools and methods. He states that statistics is fundamentally incomplete as
a field as it cannot predict the risk of rare events — something Six Sigma is
especially concerned with. Furthermore, errors in prediction are likely to occur as a
result of ignorance for or distinction between epistemic and other uncertainties.
These errors are the biggest in time variant (reliability) related failures.
 Stifling creativity in research environments: A Business Week 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. 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.
According to an article by John Dodge, editor in chief of Design News, use of Six
Sigma is inappropriate in a research environment. Dodge states “excessive metrics,
steps, measurements and Six Sigma's intense focus on reducing variability water
down the discovery process. Under Six Sigma, the free-wheeling nature of
brainstorming and the serendipitous side of discovery is stifled." He concludes
"there's general agreement that freedom in basic or pure research is preferable while
Six Sigma works best in incremental innovation when there's an expressed
commercial goal."
 Lack of systematic documentation: One criticism voiced by Yasser Jarrar
and Andy Neely from the Cranfield School of Management's Centre for
Business Performance is that while Six Sigma is a powerful approach, it can
also unduly dominate an organization's culture; and they add that much of the
Six Sigma literature – in a remarkable way (six-sigma claims to be evidence,
scientifically based) – lacks academic rigor:

36. 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.
 1.5 sigma shift: The statistician Donald J. Wheeler has dismissed the 1.5
sigma shift as "goofy" because of its arbitrary nature. Its universal applicability
is seen as doubtful.
The 1.5 sigma shift has also become contentious because it results in stated
"sigma levels" that reflect short-term rather than long-term performance: a
process that has long-term defect levels corresponding to 4.5 sigma
performance is, by Six Sigma convention, described as a "six sigma process.
“The accepted Six Sigma scoring system thus cannot be equated to actual
normal distribution probabilities for the stated number of standard deviations,
and this has been a key bone of contention over how Six Sigma measures are
defined. 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.

37. 6. Six Sigma Tools Used:
Every stage of a Six Sigma project recipe requires a mix of these methods, tools &
techniques. Let us briefly review what we mean by these keywords. Method is a way
of doing something in a systematic way. Here word "systematic" implies an orderly
logical sequence of steps or tasks. A tool provides a mechanical or mental
advantage in accomplishing a task. A technique is a specific approach to efficiently
accomplish a task in a manner that may not be immediately obvious.
 Benchmarking
Benchmarking is a standard by which something can be measured or
judged. This term was first used by surveyors. They set a benchmark by
marking a point of known vertical elevation. Therefore benchmark
becomes a point of reference for a measurement.
We benchmark every day. We compare our performance, lifestyle, or a game
of golf with friends and peers.
What is benchmarking in a business environment? It involves making
comparisons with other businesses, so that we can develop an objective
assessment of our business. It helps us to a) identify areas for breakthrough
improvements, b) establish higher targets, and c) new priorities.
Note benchmarking is not simple comparison and subsequent blind copy of
what seems to be the best. We must carefully analyze the outcome of
benchmarking and focus on what adds maximum value in our business
context. There are three types of benchmarking.
• Internal Benchmarking
It compares (critical-to-business) processes or products across the
organization on key critical-to-quality parameters such as turn-around-time or
cost.
• Functional Benchmarking
It compares similar functions or processes with industry leaders in that area.
• Competitive Benchmarking
It focuses on direct competitors in terms of their products, services,
processes, and customers.

38. The following flowchart summarizes the benchmarking processes:
Some of the simple and common candidates for benchmarking are customer
satisfaction, critical-to-business processes, products, profitability, and value
addition per employee. The list is endless. We need to shortlist what is critical
to our business success.
Benchmarking is one of the first key steps in any Six Sigma DMAIC or DFSS project

39.  SIPOC:
SIPOC is a high-level picture of the process that depicts how the given process is
servicing the customer. It is an acronym for Suppliers - Inputs - Process - Outputs -
Customers. The definition of each of these SIPOC entities is given below.
• Suppliers provide inputs to the process.
• Inputs define the material, service and/or information that are used by the
process to produce the outputs.
• Process is a defined sequence of activities, usually adds value to inputs to
produce outputs for the customers.
• Outputs are the products, services, and/or information that are valuable to
the customers.
• Customers are the users of the outputs produced by the process.
In more formal terms, SIPOC can be seen as a high-level process map. It is typically
used during the define phase of a process improvement project, as it helps us clearly
understand the purpose and the scope of a process. It is a starting point in
identifying the voice of the customer (VOC). It gives us initial insight in to the vital
inputs (or X variables) of a process [Y = f(X)] that have significant impact on critical
outputs (or Y variables). It also becomes a primary input to detailed process map
construction.
Creation of SIPOC is a team activity that requires brainstorming to discover (hidden)
details. The team consists of all the stakeholders of the process under consideration.
Brainstorming is carried out iteratively for each element (i.e. suppliers, inputs,
process steps, outputs and customers) of SIPOC. While creating SIPOC for a new
process under design, it is a good idea to start from customer and move backwards
to supplier. On the other hand, during discovery or documentation of an existing
process, SIPOC is usually created starting from process definition followed by
identification of outputs, customers, inputs and suppliers.

40.  SCATTER PLOT:
Scatter plot is a technique to discover relationship between a dependent variable (y)
and an independent variable (x) by plotting “y” against “x”. Once plotted, it is very
easy to spot the correlation between “x” and “y” variables. For example, the following
scatter plot between pizza delivery time (y) and the delivery distance (x) reveals a
possible linear correlation.
Such analysis is the first step towards determining the maximum delivery distance or
the nearby areas, where this pizza outlet will be confident of 30 minutes delivery
(after taking in to the account pizza preparation time and of course variation).

41.  FISHBONE DIAGRAM:
The fishbone diagram is a graphical method for finding the root causes of an effect.
The effect can be either a negative one, such as a process defect or an undue
process variation; or a positive one, such as a desired process outcome. Kaoru
Ishikawa, a famous Japanese consultant developed this method in the 1960s. It is
also known as "Cause-and-Effect Diagram" or "Ishikawa Diagram".
The below example effect to illustrate the concept is "high petrol consumption in a
car".
• Step I-Identify the process effect to be analyzed. Develop an
Operational Definition to ensure that it is clearly understood. Write the effect in
a box on the right side and draw a horizontal arrow from left to right that
touches the box as illustrated in the figure below.

42. • Step II-Identify the main categories of causes resulting in the effect
under consideration. These categories can easily be selected from the
applicable six key process elements. These process elements are people,
environment, material, method, machinery, and measurement. Add selected
categories in the diagram as illustrated in the following figure.
Step III-Identify as many causes under each category and add them to the
corresponding category. Detail each cause further (recursively) to the lowest level
possible.

43. Analyze this diagram to identify the causes that require deeper investigation. As
fishbone diagram identify only potential causes, it may be a good idea to use a
Pareto Chart to determine the cause(s) to focus on first.
 Pareto Chart –
A Pareto Chart depicts the frequency with which certain events occur. It is a
bar graph where each frequency (or frequency range) is shown in a
descending order of importance of data, from left to right.
This is based on the Pareto Principle, also called 80-20 rule or rule of vital few.
Formulated by the father of quality - Dr. Juran and named after the famous Italian
economist Vilfredo Pareto, this principle helps separate the "vital few" from the
"useful many" in any business scenario. It helps us identify and focus on "vital few" to
maximize our returns on investments on resources.
To develop an exact understanding of the concept, let us go back to our famous
pizza shop example. Here is some old customer complaint data before they
mastered the thirty minutes pizza delivery.
The pizza shop wanted to develop a strategy to reduce the customer complaint
dramatically. This is where Pareto Chart comes in to action. Let us see how.

44. To create a Pareto Chart, this data is sorted and cumulative count & percentages are
computed as illustrated in the following table.
Using this data. A bar-cum-line graph is drawn using a standard spreadsheet like MS
Excel. The bar represents the count of each complaint and the line illustrates the
cumulative complaint count percentages.
With this graph, finding the vital few is simple. Locate the 80% point on the right y-
axis and find the corresponding point on the x-axis. It clearly highlights that we need
to address "delayed delivery" and "pizza not hot" complaint categories to take care of

45. 80% of the customer complaints. In fact, the "pizza not hot" complaint was partially
the result of "delayed delivery" problem. These two complaint categories only
constitute 22% of all the complaint categories. Can we see the 80-20 rule? It is
evident that by fixing only 20% (22% to be precise) of the complaint categories solve
80% (78% to be precise) of the customer complaints. This helped our pizza shop to
focus on solving the right set of the problems
 RISK MANAGEMENT-
Risk has two key elements - a) an uncertainty and b) an impact in terms of
potential loss (if it happens).
Risk management is a continuous process. Risk management process involves
following key steps:
1. Identify risks
2. Assess each risk
3. Rank all risks according to their severity
4. Plan for risk mitigation and contingency on the basis of outcome of step 3
5. Monitor each risk
6. Control deviations (if any) from risk mitigation plan
• Identify risks:
Risk identification is carried out at the beginning of every project. Subsequently, it is
revisited during each project review on an ongoing basis for all residual risks and
new risks. The identification of risk is highly project specific. In general, any project
has three key dimensions viz. cost, specifications, and time; and risks can be
discovered in these contexts. Each risk must be clearly documented in a "condition
(i.e. uncertainty)" - "consequence (i.e. impact)" format. In our previous example,
"condition" is the occurrence of heavy traffic and "consequence" is losing the initial
negotiation advantage.
It is always a good idea to create a risk classification or taxonomy. Each risk must be
classified according to the taxonomy. Once this data acquires critical mass, it helps
in developing better risk management strategies.

46. • Assess Risks
Risk assessment involves determining the uncertainty, the impact, and the first risk
indicator. The uncertainty is the probability of occurrence of the risk. This probability
can be determined either qualitatively or quantitatively. For qualitative measure, it is
recommended to use 4 categories (to avoid middle point bias) such as 1-low, 2-
medium, 3-high, and 4-very high. The quantitative measure is a normal probability
scale measure from 0 to 1. The impact can be determined in terms of its severity,
preferably a value from 1 (lowest) to 4 (highest). The first risk indicator is earliest
condition or event that signals risk turning in to a problem.
• Rank
After successful risk assessment, ranking is a relatively simple task. Sorting the
product of the probability of every risk and its corresponding impact generates the
risk ranking. This now becomes an important input for risk mitigation planning. The
risk ranking determines the extent of risk planning focus.
• Plan
At this step, a mitigation approach is developed for each risk, to either avoid or
reduce the impact of risk. The responsibility to implement the mitigation strategy is
assigned to a team member along with a target date. The actual execution of the
mitigation plan is called risk resolution. In addition, a contingency plan is also
developed to handle the situation when a risk turns in to a problem.
• Monitor
It involves regular tracking of risk resolution process and first risk indicator. The
deviations in the risk resolution process are recorded. Occurrence of first risk
indicator may trigger activation of contingency plan.
• Control
At this step, strategy to reduce deviation in the risk resolution process is developed
and implemented.
All the above six steps are carried out on an ongoing basis for a project so that all
risks stay managed during its life cycle

47.  SAMPLING
Sampling is a method to draw inference about one or more characteristics of a large
group of items by examining a smaller but representative selection of group items.
This selection is referred as the sample. This selection can be probability driven, or
judgment/non-probability driven.
In non-probability sampling scheme, the probability of a population element being
chosen is unknown and is based on the judgment of the researcher.
In probability driven sampling, each element of population has a "known non-zero"
probability selection. It is easy to build mathematical or statistical models for drawing
inference about the population. Our focus will be on this type of sampling.
One common example of probability sampling is "random sampling", where each
element (and each combination of element) has an equal chance of being selected.
Why Sampling?
Why can't we look at the entire population? Simply because sometimes the
population is so large that it makes it prohibitive in terms of time and resources to
carry out the measurements or survey. In any large volume manufacturing process,
we face such challenges regularly. Other examples are exit polls, or any national
level survey. In addition, there are occasions where measurement leads to
destruction of the item under consideration. Typical examples are measurement of
tensile strength of a wire, or measuring battery life, or crash testing of a car.
Steps to Successful Sampling
The goal is to acquire a sample that is true representative of the population; it is
something like a mini replica of population that is good enough to draw inference
with required "accuracy & precision" about the population. Any successful sampling
requires striking a balance between the required "accuracy & precision" and the
"available resources". The key steps are outlined below.
1. Defining the population
2. Determining the sample size
3. Selecting the sampling technique

48. • Defining the population
This is an important step if the entire population is not accessible for sampling. Ideal
situation will be to draw sample from across the entire population. However, this may
not be practical in every situation. The items within a population who can be sampled
are usually limited. Such a target population, which can be sampled, is called a
sampling frame. Defining this sampling frame is the first step of any successful
sampling. This becomes an important input in determination of sample size and
selection of sampling technique.
• Determine the sample size
How large or how small sample should be drawn? The answer lies in the goal of
successful sampling i.e. "to acquire a sample that is true representative of the
population".
Variation comes in action once again. Imagine that there is no variation in the
population or in other words, every item is identical. In such a situation a sample size
of "1" is not only good enough but it will also deliver 100% accuracy & precision.
However, in real life variation is everywhere. Therefore, higher the variation in the
population, larger the sample size required.
Let us turn to some mathematics to develop clear understanding. This part of the
discussion will be interspersed with some introductory content on drawing inference.
It is both unavoidable and important at this stage.
Consider a population with a variable of interest called, "x". If we draw a random
sample of size "n", the sample mean of "x" will be given by the following equation.
Now if we have to draw an inference about the population mean of "x", we need to
know about the distribution of sample mean of "x". The distribution of any such
sample statistic (like sample mean) is known as a sampling distribution (of mean in

49. this case). The Central Limit Theorem (CLT) gives us three very important pieces of
information that help us in drawing the required inference.
First, the mean of sampling distribution is equal to population mean. This can be
understood intuitively. When we repeatedly sample from the given population, and
record the each sample mean, we will observe that (a) it is highly unlikely that the
sample means are exactly equal to the population mean, (b) some sample mean will
be smaller than the population mean, (c) some sample mean will be larger than the
population mean. Now since our sampling is truly random without any bias, the
deviations of sampling mean from the population mean in either direction will be
equally likely. Therefore, we can conclude that the sampling distribution mean is
equal to population mean.
Second, the standard deviation of the sampling distribution is given by the following
equation.
This is called "standard error". This can also be understood intuitively. The larger the
standard deviation of population, the larger would be sampling error i.e. the standard
deviation of sampling distribution. Therefore, standard deviation of population
appears in the numerator. In any case, larger the sample size, smaller would be
sampling error. This happens because you get a more representative same leading
to lesser dispersion in sampling distribution. This is why "n" appears in the
denominator. The square root indicates the returns diminish faster with increase in
sample size. For example, if you increase a sample size to 40 from 10 (a 4x
increase), the standard deviation will only reduce by 1/2 and not by 1/4.
Third, if the sample size is large enough (typically more than 30), the sampling
distribution of mean will tend to be distributed normally. This is true irrespective of
the distribution pattern of population. This phenomenon seems true intuitively as
most of the random natural distributions are normal in nature.

50. Now armed with this knowledge, we can easily compute the certainty (or uncertainty)
in a measurement for a required precision. This certainty (or probability) is called
"confidence interval" (CI). Since, the sampling distribution is normal, the
computations are very simple. For example for 95% CI for μ is given below:
Just to recap, 95% CI means 95% area under the normal curve or an area covered
within 1.96 times the standard deviation (or Z-score, refer to section on Measure of
Dispersion) away from the mean in both directions.
And the figure for 99% CI will be
Coming back to correct sample size, note that this depends on (a) variation in
population, (b) standard error, and (c) required CI. Typically as rule of thumb, a
minimum sample size of 30 is required and in most cases, a sample size between 50
and 100 is good enough.
A more formal method can be derived in form of a formula. For a given CI,
corresponding Zci can be found in any z-score table. Therefore, as discussed earlier,
μ for the given CI will be given by following equation.
We can say that the precision, "P" will be,

51. • Selecting the sampling technique
There are many sampling techniques. This section discusses three key methods.
1. Simple Random Sampling ensures that each element of the population has
an equal chance of being selected. Typically, random number generators are
used to select a random sample from the population.
2. Systematic Random Sampling is a modified form of random sampling. It
adds a bit of order to random sampling. The first element of the population is
selected randomly. After that, starting from this randomly selected element,
every nth element is selected, where n is equal to the population size divided
by the sample size. It is easier compared to the simple random sampling.
However, it is not suitable if there is a periodicity in the population. It works
very well if the list is haphazard.
3. Stratified Random Sampling is another form of modified random sampling.
In this case, the entire population is divided in to homogenous subgroups that
share a common characteristic. Thereafter, random sampling is carried out on
each group. This technique reduces the standard error and produces more
representative sample from the population whenever subgroups are present

52. or possible. For example, if our washers are produced on three different
machines then it may be a good idea to have three subgroups (one for each
machine) for stratified random sampling.
 BRAINSTORMING
Brainstorming is a technique to systematically generate ideas usually to
handle a challenging situation, from a group of people by nurturing free-
thinking. There are several such opportunities in any organisation, e.g.
improving productivity, increasing sales, and finding new business
development areas, launching new products or defining new processes.
While there may be well defined techniques or processes to handle these
situations, but brainstorming is a critical activity in all of these processes.
Techniques such as Affinity, Nominal group technique, Cause and Effect
Diagram, Failure mode effect analysis, 5 whys, Fault tree analysis, Decision
matrix, and Risk analysis require brainstorming as an integral part of their
execution. The list is endless!
Brainstorming required for generating inputs for the above techniques is
complex as compared to the free flow ideation that one usually associates
with the term brainstorming. An example of the kind of brainstorming required
here can be observed in a 5-why analysis, where brainstorming occurs for
every why in a hierarchical manner until a root cause is discovered.
Here are few examples of situations, where brainstorming can be applied as
an effective technique:
1. Process design or re-engineering using SIPOC, and Process Map.
2. Root cause determination by leveraging techniques like Fishbone
Diagram, 5 Whys, and Fault tree analysis.
3. Developing robust concepts, design, and process using FMEA.
4. Project Management using Project Planning & Scheduling and Risk
Analysis.
Brainstorming session must be orchestrated by a facilitator. The number of
participants in a session must be limited to a manageable number - typically

53. between 5 and 15. There are few rules for a successful brainstorming, which
should be enforced by the facilitator. These rules are listed below.
1. Focus on generating a large number of ideas
2. Active involvement of every participant in the process
3. Encourage out-of-the-box thinking and creativity
4. Promote criticism free environment - encourage all types of ideas
including wild or seemingly ridiculous ideas while keeping the purpose
of the brainstorming in mind
5. Combine ideas to create newer ideas
6. Setup a reasonable time limit based on the challenge in hand
How to conduct brainstorming?
1. Select and block a (lively) room free from interruptions and distractions
for brainstorming.
2. Identify and invite the participants. The invite must clearly state the
purpose of brainstorming.
3. Before the start, ensure that the room is equipped with basic essentials
like blackboard, flipcharts, pens, and large size post-its, etc.
4. Initiate the session by clearly explaining the purpose, possibly already
written and highlighted on the board. Also set the basic rules for the
session. Set some time towards the end of the session for organizing
the ideas generated.
5. Invite people to come up with ideas. One of the participants may be
designated to record each idea or alternatively each participant may be
requested to pen his/her idea on a post-it to speed up the process.
Maintain a lively environment; monotony must be avoided at every cost.
6. Ensure that the rules of a successful brainstorming are followed
properly.
7. Towards the end, focus on organizing ideas and eliminating the
duplicate ones. If the number of ideas generated is sufficiently large,
affinity diagram may be used to organize the ideas.
8. Close the session with a note collectively appreciating each ones
contribution.
Armed with this idea bank, we are now ready to shortlist ideas which
subsequently can be evaluated for implementation.

54.  Descriptive Statistics
Descriptive Statistics is one of the simplest techniques used in quality
management to obtain a meaningful insight into the data being
analyzed.
Let us take a few examples. It makes sense to build a frequency table
of complaints by categories from the raw data on complaints from
different customers. It clearly tells us the top few complaints that need
immediate attention. On the other hand, it would be preferable to
compute the average or mean from loan processing time data of
thousands of applications from a bank to find out the average
turnaround time required to process any application. This can
subsequently be compared with industry average to benchmark bank's
performance.
Data types tell us how we can gain meaningful insight in the data – this
could be achieved by computing mean or by building frequency table or
by using other summary measures such as mode or median. Therefore,
it is important to understand the type of data being analyzed to
determine what summary measures are applicable to obtain a
meaningful insight. Recall that there are two types of data - quantitative
or numeric or scale, and qualitative or categorical or attribute. The
categorical data can be in form of either ordered or unordered
categories. Examples of unordered category data is marital status (e.g.
single, married, widowed, or separated) or customer complaints data
collected in our pizza shop example. Such data is also referred to as
nominal data.
Ordered categorical data or ordinal data defines the values representing
rank or order. For example, customer satisfaction in terms of
unsatisfied, expectations met, exceeded expectations, and significantly
exceeded expectations.
With this background of data it should be very easy to imagine that not
all measures may be computed for a given data (type). For example, it
does not make sense to compute mean of nominal data; imagine

55. computing mean of marital status! In this case, mode is what will give us
meaningful insight into the data. Following table summarizes the
applicable characteristics and representations for each data type.
Note that outliers are extreme data values in a dataset that have
significant numeric distance from the rest of the data. The presence of
an outlier is usually an indication of an error in measurement or
recording. Such data values impact mean and standard deviation
directly.
 Trimmed mean is computed after removing the fixed percentage of
extreme upper and lower data values; typical percentage is 5%. Such a
mean is resistant to outliers. Similarly in the case of dispersion, IQR is
more resistant to outliers than the standard deviation or variance. This
happens because IQR is the range of only the middle 50% of all data
values.
 Measures computed of a sample drawn from the population are referred
to as statistics; when the same measure is computed for a population, it
is called a parameter.

56.  Box Plot:
Box Plot provides an intuitive graphical representation of the five number
summary of a dataset. The five number summary consists of Minimum, Q1,
Q2 or Median, Q3, and Maximum of a dataset. John W. Tukey introduced the
concept of Box Plot in his book Exploratory Data Analysis, published in 1977.
It is also referred as Box & Whisker Plot.
Let us understand the power of box plot through a series of examples; the
following example shows the box plot along with the sample data.
The box represents the inter quartile range (IQR = Q3-Q1) where its left
border (also called hinge) corresponds to the first quartile (Q1) and the right
border corresponds to the third quartile (Q3). Therefore, the middle 50% of
data values fall within the box. The line in the middle represents the median of
the data. The left whisker represents the smallest 25% of data values with its
left most end corresponding to the minimum value of the data. Similarly, the
right whisker represents the largest 25% of data values with its right most end
corresponding to the maximum value of the data.
Let us look at the second data set and the corresponding box plot. The data
has been superimposed on the histogram with box plot aligned perfectly on
the top to give you a crisp and easy to understand picture.

57. The histogram in the figure clearly suggests that,
1. It has no skew implying that it has symmetrical distribution.
2. It has long tails i.e. it possibly has outliers.
Now, it is time to look at the box plot. Notice that both the whiskers are much
longer than the length of the box (IQR) - an indication of the possible presence
of outliers. In fact, Tukey suggests that an outlier is a point that is greater than
or less than 1.5 times the IQR. Here is the same box plot, but with outliers
(0.95 and 1.05) clearly highlighted as per Tukey's recommendations.
The above box plots (with or without outliers) also reveal that the two whiskers
are of equal length and the median lies right in the middle of the box - an
indication of symmetrical distribution. Any deviation from this leads to a non-
symmetrical distribution, as illustrated in the following box plot.

58. Box plot also serves as a great way to quickly compare two or more series by
juxtaposing the box plots of the series to be investigated. The following plot
shows the previous two box plots juxtaposed clearly to highlight the
differences (or similarities) in central tendencies and dispersions.
Important Observations
1. Box plot is based on robust statistics, i.e. it is more tolerant (or robust)
to the presence of outliers.
2. It gives an indication of shape of distribution in terms of symmetry or
skewness.
3. It is an excellent means to determine if there are similarities (or
differences) between two or more data sets by juxtaposing their box
plots.

59.  OperationalDefinition:
Operational definition is the first step towards effective management. It helps
us build a clear understanding of a concept or a phenomenon so that it can be
unambiguously measured.
Let us take a very simple example to understand the need and the concept of
operational definition. Let us imagine a situation that we wish to buy an all-
purpose shirt with 50% cotton and 50% polyester. Would you accept a shirt
whose front is made up of 100% cotton cloth and the back made of 100%
polyester cloth? Surely not! Clearly we need to (operationally) define what we
need.
A better expression would be that we need a shirt made up of a cloth having
even distribution of cotton and polyester fibers and their proportion by weight
(or may be by number) is equal. So far so good, but we also need to have a
mechanism to test it. In this case, we can send the shirt to a lab where
randomly selected two areas (say 1 cm x 1 cm) - one from the back and one
from the front are examined for the contents.
The lab reports that group of two fibers of each - polyester and cotton are
interwoven to make this clothe. Did we mean alternate fibers of polyester and
cotton or something else? We now discover that we even need to define "even
distribution".
In a business management scenario, common words such as good, reliable,
and accurate (etc.) can have multiple meanings unless they are (operationally)
defined in a specific context.

60. So how do we construct an operational definition? The process is explained
with the help of an example in the following figure:
Document the outcome of each process step and that becomes the
operational definition. The operation definition must be tested before it is rolled
out.
In the words of quality guru Deming, "An operational definition is one that
people can do business with.... It must be communicable, with the same
meaning to vendor as to purchaser, same meaning yesterday and today..."

61. 7.CONCLUSION:
This project is about “Understanding Six Sigma-an overview” gave me an opportunity
to understand the Six Sigma & its tools. Six Sigma emphasizes breakthrough rates
of improvement, and focuses on world class performance to achieve organizational
success.
This Research helps to improve the quality of the output of a process by identifying
and removing the causes of defects and minimizing variability in manufacturing and
business processes. It uses a set of quality management methods, mainly empirical,
statistical methods, and creates a special infrastructure of people within the
organization, who are experts in these methods.
Each Six Sigma project carried out within an organization follows a defined
sequence of steps and has specific value targets, for example: reduce process cycle
time, reduce pollution, reduce costs, increase customer satisfaction, and increase
profits.
Hence Six Sigma is methodology used for:
• Aligning key business processes to achieve those requirements.
• Utilizing rigorous data analysis to minimize data variation in those processes.
• Driving rapid and sustainable improvement to business processes.

62. BIBLIOGRAPHY
BOOKS:
o The Six Sigma Way: How to Maximize the Impact of Your Change and
Improvement Efforts by Peter Pande, Robert Neuman and Roland
Cavanaugh
o The Six Sigma Handbook: A Complete Guide For Green Belts, Black
Belts, And Managers At All Levels by Thomas Pyzdek
o Statistics For Six Sigma Made Easy! by Warren Brussee
o The Certified Six Sigma Green Belt Handbook by Roderick Munro,
Govindarajan Ramu and Daniel Zrymiak
o Lean Six Sigma for Hospitals: Simple Steps to Fast, Affordable, and
Flawless Healthcare by Jay Arthur
o Six Sigma For Dummies by Craig Gygi and Bruce Williams
o Six Sigma Demystified by Paul Keller
o Six Sigma for Everyone by George Eckes
References:
• http://www.isixsigma.com
• http://www.sixsigmaonline.org/index.html
• http://www.wikipedia.com/
• http://www.businessballs.com/sixsigma.htm