The six sigma handbook a complete guide

The
Six Sigma
Handbook
Revised and Expanded

A Complete Guide
for Green Belts, Black Belts,
and Managers at All Levels

THOMAS PYZDEK

McGraw-Hill
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DOI: 10.1036/0071415963


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Contents
Preface
Introduction
Part I

Six Sigma Implementation and Management

Chapter 1 Building the Six Sigma Infrastructure
What is Six Sigma?
Why Six Sigma?
The Six Sigma philosophy
The change imperative
Change agents and their effects on organizations
Implementing Six Sigma
Timetable
Infrastructure
Six Sigma deployment and management
Six Sigma communication plan
Six Sigma organizational roles and responsibilities
Selecting the ‘‘Belts’’
Integrating Six Sigma and related initiatives
Deployment to the supply chain
Change agent compensation and retention
Chapter 2 Six Sigma Goals and Metrics
Attributes of good metrics
Six Sigma versus traditional three sigma performance
The balanced scorecard
Measuring causes and effects
Information systems

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Contents

Customer perspective
Internal process perspective
Innovation and learning perspective
Financial perspective
Strategy deployment plan
Information systems requirements
Integrating Six Sigma with other information systems
technologies
OLAP, data mining, and Six Sigma
Dashboard design
Dashboards for scale data
Dashboards for ordinal data
Dashboards for nominal data
Setting organizational key requirements
Benchmarking
Chapter 3 Creating Customer-Driven Organizations
Elements of customer-driven organizations
Becoming a customer- and market-driven enterprise
Elements of the transformed organization
Surveys and focus groups
Strategies for communicating with customers and employees
Surveys
Focus groups
Other customer information systems
Calculating the value of retention of customers
Complaint handling
Kano model of customer expectations

Customer expectations, priorities, needs, and ‘‘voice’’
Garden variety Six Sigma only addresses half of the Kano
customer satisfaction model
Quality function deployment (QFD)
Data collection and review of customer expectations, needs,
requirements, and specifications
The Six Sigma process enterprise
Examples of processes
The source of conflict
A resolution to the conflict
Process excellence
Using QFD to link Six Sigma projects to strategies
The strategy deployment matrix
Deploying differentiators to operations

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Deploying operations plans to projects
Linking customer demands to budgets
Structured decision-making

Category importance weights
Subcategory importance weights
Global importance weights
Chapter 4 Training for Six Sigma
Training needs analysis
The strategic training plan
Training needs of various groups
Post-training evaluation and reinforcement
Chapter 5 Six Sigma Teams
Six Sigma teams
Process improvement teams
Work groups
Quality circles
Other self-managed teams
Team dynamics management, including con£ict resolution
Stages in group development
Common problems
Member roles and responsibilities
Facilitation techniques
When to use an outside facilitator
Selecting a facilitator
Principles of team leadership and facilitation
Facilitating the group task process
Facilitating the group maintenance process
Team performance evaluation
Team recognition and reward
Chapter 6 Selecting and Tracking Six Sigma Projects
Choosing the right projects
Customer value projects
Shareholder value projects

Other Six Sigma projects
Analyzing project candidates
Benefit-cost analysis
A system for assessing Six Sigma projects
Other methods of identifying promising projects
Throughput-based project selection
Multi-tasking and project scheduling
Summary and preliminary project selection

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Contents

Tracking Six Sigma project results
Financial results validation

Financial analysis
Lessons learned capture and replication
Part II

Six Sigma Tools and Techniques

Introduction to DMAIC and Other Improvement
Models
DMAIC, DMADV and learning models
Design for Six Sigma project framework
Learning models
PDCA
Dynamic models of learning and adaptation

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Chapter 7

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The Define Phase

Chapter 8 Problem Solving Tools
Process mapping
Cycle time reduction through cross-functional process
mapping
Flow charts
Check sheets
Process check sheets
Defect check sheets
Stratified defect check sheets
Defect location check sheets
Cause and effect diagram check sheets
Pareto analysis
How to perform a Pareto analysis
Example of Pareto analysis
Cause and e¡ect diagrams
7M tools
Affinity diagrams
Tree diagrams
Process decision program charts
Matrix diagrams
Interrelationship digraphs
Prioritization matrices
Activity network diagram
Other continuous improvement tools

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The Measure Phase
Chapter 9 Basic Principles of Measurement
Scales of measurement
Reliability and validity of data
Definitions
Overview of statistical methods

Enumerative versus analytic statistical methods
Enumerative statistical methods
Assumptions and robustness of tests
Distributions
Probability distributions for Six Sigma
Statistical inference
Hypothesis testing/Type I and Type II errors
Principles of statistical process control
Terms and concepts
Objectives and benefits
Common and special causes of variation
Chapter 10 Measurement Systems Analysis
R&R studies for continuous data
Discrimination, stability, bias, repeatability,
reproducibility, and linearity
Gage R&R analysis using Minitab
Output
Linearity
Attribute measurement error analysis
Operational definitions
Example of attribute inspection error analysis
Respectability and pairwise reproducibility
Minitab attribute gage R&R example

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The Analyze Phase
Chapter 11 Knowledge Discovery
Knowledge discovery tools
Run charts
Descriptive statistics
Histograms
Exploratory data analysis
Establishing the process baseline

Describing the process baseline

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Contents

SIPOC
Process for creating a SIPOC diagram
SIPOC example
Chapter 12 Statistical Process Control Techniques
Statistical process control (SPC)
Types of control charts
average and range, average and sigma, control charts for
individual measurements, control charts for proportion
defective, control chart for count of defectives, control
charts for average occurrences-per-unit, control charts
for counts of occurrences-per unit
Short-run SPC
control chart selection, rational subgroup sampling,
control charts interpretation

EWMA
EWMA charts
SPC and automatic process control
Minitab example of EWMA
Chapter 13 Process Capability Analysis
Process capability analysis (PCA)
How to perform a process capability study
Statistical analysis of process capability data
Process capability indexes
Interpreting capability indexes
Example of capability analysis using normally distributed
variables data
Estimating process yield
Rolled throughput yield and sigma level
Normalized yield and sigma level
Chapter 14 Statistical Analysis of Cause and E¡ect
Testing common assumptions
Continuous versus discrete data
Independence assumption
Normality assumption
Equal variance assumption
Regression and correlation analysis
Scatter plots
Correlation and regression
Analysis of categorical data
Chi-square, tables

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Contents

Logistic regression
binary logistic regression, ordinal logistic regression,
and nominal logistic regression
Non-parametric methods
Guidelines on when to use non-parametric tests
Minitab’s nonparametric tests

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The Improve Phase
Chapter 15 Managing Six Sigma Projects
Useful project management tools and techniques
Project planning
Project charter
Work breakdown structures
Feedback loops
Performance measures
Gantt charts
Typical DMAIC project tasks and responsibilities
PERT-CPM-type project management systems
Resources
Resource conflicts
Cost considerations in project scheduling

Relevant stakeholders
Budgeting
Project management implementation
Management support and organizational roadblocks
Short-term (tactical) plans
Cross-functional collaboration
Continuous review and enhancement of quality process
Documentation and procedures
Chapter 16 Risk Assessment
Reliability and safety analysis
Reliability analysis
Risk assessment tools
Fault free analysis
Safety analysis
Failure mode and e¡ect analysis (FMEA)
FMEA process
Statistical tolerancing
Assumptions of formula
Tolerance intervals

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Contents

Chapter 17 Design of Experiments (DOE)
Terminology

Definitions
Power and sample size
Example
Design characteristics
Types of design
One-factor
Examples of applying common DOE methods using software
Two-way ANOVA with no replicates
Two-way ANOVA with replicates
Full and fractional factorial
Empirical model building and sequential learning
Phase 0: Getting your bearings
Phase I: The screening experiment
Phase II: Steepest ascent (descent)
Phase III: The factorial experiment
Phase IV: The composite design
Phase V: Robust product and process design
Data mining, arti¢cial neural networks and virtual process
mapping
Example

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The Control Phase
Chapter 18 Maintaining Control After the Project
Business process control planning
How will we maintain the gains made?
Tools and techniques useful for control planning
Using SPC for ongoing control
Process control planning for short and small runs
Strategies for short and small runs
Preparing the short run process control plain (PCP)
Process audit
Selecting process control elements
The single part process
Other elements of the process control plan
PRE-Control
Setting up PRE-Control
Using PRE-Control

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Beyond DMAIC
Chapter 19 Design for Six Sigma (DFSS)
Preliminary steps
De¢ne
Identify CTQs
Beyond customer requirementsöidentifying ‘‘delighters’’
Using AHP to determine the relative importance of the CTQs
Measure
Measurement plan
Analyze
Using customer demands to make design decisions

Using weighted CTQs in decision-making
Pugh concept selection method
Design
Predicting CTQ performance
Process simulation
Virtual DOE using simulation software
Design phase cross-references
Verify
Pilot run
Transition to full-scale operations
Verify phase cross-references
Chapter 20 Lean Manufacturing and Six Sigma
Introduction to Lean and muda
What is value to the customer?
Example: Weld dents
The value definition
Kinds of waste
What is the value stream?
Value stream mapping
How do we make value £ow?
Example of Takt time calculation
Spaghetti charts
How do we make value £ow at the pull of the customer?
Tools to help improve flow
5S; constraint management; level loading; pull
systems; flexible process; lot size reduction
How can we continue towards perfection?
KAIZEN
Becoming Lean: A tactical perspective
Six Sigma and Lean

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Contents

Appendix
Table 1öGlossary of basic statistical terms
Table 2öArea under the standard normal curve
Table 3öCritical values of the t-distribution
Table 4öChi-square distribution
Table 5öF distribution (a ẳ 1%ị
Table 6ửF distribution (a ẳ 5%)
Table 7ửPoisson probability sums
Table 8öTolerance interval factors
Table 9öDurbin-Watson test bounds
Table 10öy factors for computing AOQL
Table 11öControl chart constants
Table 12öControl chart equations

Table 13öTable of dÃ2 values
Table 14öPower functions for ANOVA
Table 15öFactors for short run control charts for
individuals, X-bar, and R charts
Table 16ưSigni¢cant number of consecutive highest or
lowest values from one stream of a multiple-stream
process
Table 17öSample customer survey
Table 18öProcess s levels and equivalent PPM quality levels
Table 19ưBlack Belt e¡ectiveness certi¢cation
Table 20ưGreen Belt e¡ectiveness certi¢cation
Table 21öAHP using Microsoft ExcelTM
References
Index

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^^^

Preface
First, a basic question: just what are organizations anyway? Why do they
exist? Some experts believe that the reason organizations exist is because of the
high cost of executing transactions in the marketplace. Within an organization
we can reallocate resources without the need to negotiate contracts, formally
transfer ownership of assets, and so on. No need for lawyers, the managers do
things on their own authority. The question is: how should they do this? In the
free market prices tell us how to allocate resources, but prices don’t exist inside
of an organization. We must come up with some alternative.
Transaction costs aside, organizations exist to serve constituencies.
Businesses have shareholders or private owners. The equivalent for non-profits
are contributors. Organizations also serve ‘‘customer’’ constituencies. In other
words, they produce things that other people want. Businesses must produce
things that people are willing and able to buy for their own benefit. Non-profits
must produce things that contributors are willing and able to buy for the benefit
of others. Both types of organizations must do one thing: create value. The output must be of greater value than the inputs needed to produce it. If the output
serves the constituencies well, the organization is effective. If it creates added

value with a minimum of resources, it is efficient. (It is a common misconception that non-profits don’t need to be efficient. But the only difference between
a for-profit and a not-for-profit is that the ‘‘surplus’’ created by adding value is
used for different purposes. A not-for-profit that produces negative value (i.e.,
spends more for its output than contributors are willing to pay) will not survive
any more than a business posting continuous losses.) Boards of directors evaluate the effectiveness and efficiency of management and have the authority and
duty to direct and replace inefficient or ineffective managers.

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xiv

Preface

Six Sigma’s role in all of this is to help management produce the maximum
value while using minimum resources. It does this by rationalizing management. By this I mean that it applies scientific principles to processes and products. By using the Six Sigma DMAICÃ approach processes or products are
improved in the sense that they are more effective, more efficient, or both. If
no process or product exists, or if existing processes or products are deemed
beyond repair, then design for Six Sigma (DFSS) methods are used to create
effective and efficient processes or products. Properly applied, Six Sigma minimizes the negative impact of politics on the organization. Of course, in any
undertaking involving human beings, politics can never be completely eliminated. Even in the best of Six Sigma organizations there will still be the occasional Six Sigma project where data-based findings are ignored because they
conflict with the preconceived notions of a powerful figure in the organization.
But this will be the exception rather than the rule.
It should be obvious by now that I don’t view Six Sigma either as a panacea or
as a mere tool. The companies that have successfully implemented Six Sigma
are well-known, including GE, Allied Signal, Intuit, Boeing Satellite Systems,
American Express and many others. But the picture isn’t entirely rosy, failures
also exist, most notably Motorola, the company that invented Six Sigma.ÃÃ
Running a successful business is an extremely complicated undertaking and it
involves much more than Six Sigma. Any organization that obsesses on Six

Sigma to the exclusion of such things as radical innovation, solid financial management, a keen eye for changing external factors, integrity in accounting, etc.
can expect to find itself in trouble some day. Markets are akin to jungles, and
much danger lurks. Six Sigma can help an organization do some things better,
but there are places where Six Sigma doesn’t apply. I seriously doubt that Six
Sigma would’ve helped Albert Einstein discover relativity or Mozart compose
a better opera. Recognizing the limits of Six Sigma while exploiting its strengths
is the job of senior leadership.
If you are working in a traditional organization, deploying Six Sigma will
rock your world. If you are a traditional manager, you will be knocked so far
out of your comfort zone that you will literally lose sleep trying to figure out
what’s happening. Your most cherished assumptions will be challenged by
your boss, the accepted way of doing things will no longer do. A new full-time,
temporary position will be created which has a single mission: change the orgaÃ

Define, Measure, Analyze, Improve, Control.

ÃÃ

Whether Six Sigma has anything to do with Motorola’s recent problems is hotly debated. But it is undeniable that Motorola

relied heavily on Six sigma and that it has had difficulties in recent years. Still, Motorola is a fine company with a long and
splendid history, and I expect to see it back on top in the not too distant future.


Preface

xv

nization. People with the word ‘‘belt’’ in their job title will suddenly appear,
speaking an odd new language of statistics and project management. What

used to be your exclusive turf will be identified as parts of turf-spanning processes; your budget authority may be usurped by new ‘‘Process Owners.’’ The
new change agents will prowl the hallowed halls of your department, continuously stirring things up as they poke here and peek there, uncovering inefficiency and waste in places where you never dreamed improvement was
possible. Your data will be scrutinized and once indispensable reports will be
discontinued, leaving you feeling as if you’ve lost the star you use to naviage.
New reports, mostly graphical, will appear with peculiar lines on them labeled
‘‘control limits’’ and ‘‘process mean.’’ You will need to learn the meaning of
such terms to survive in the new organization; in some organizations you
won’t be eligible for advancement until you are a trained ‘‘belt.’’ In others, you
won’t even be allowed to stay.
When done properly, the result of deploying Six Sigma is an organization
that does a better job of serving owners and customers. Employees who adapt
to the new culture are better paid and happier. The work environment is exciting and dynamic and change becomes a way of life. Decisions are based on reason and rationality, rather than on mysterious back-room politics.
However, when done half-heartedly, Six Sigma (or any other improvement
initiative) is a colossal waste of money and time. The message is clear: do it
right, or don’t do it at all.
It has been nearly two decades since Six Sigma began and the popularity of
the approach continues to grow. As more and more firms adopt Six Sigma as
their organizational philosophy, they also adapt it to their own unique circumstances. Thus, Six Sigma has evolved. This is especially true in the way Six
Sigma is used to operationalize the organization’s strategy. Inspired leaders,
such as Jack Welch and Larry Bossidy, have incorporated Six Sigma into the fabric of their businesses and achieved results beyond the predictions of the most
enthusiastic Six Sigma advocate. Six Sigma has also been expanded from merely
improving existing processes to the design of new products and processes that
start life at quality and performance levels near or above Six Sigma. Six Sigma
has also been integrated with that other big productivity movement, Lean
Manufacturing. In this second edition I attempt to capture these new developments and show how the new Six Sigma works.


^^^

Introduction

The goal of this book remains the same as for the first edition, namely, to provide you with the comprehensive guidance and direction necessary to realize
Six Sigma’s promise, while avoiding traps and pitfalls commonly encountered.
In this book you will find a complete overview of the management and organization of Six Sigma, the philosophy which underlies Six Sigma, and those problem
solving techniques and statistical tools most often used in Six Sigma. It is not
intended to be an ASQ certification study guide, although it includes coverage
of most of the topics included in the ASQ body of knowledge. Rather it is
intended as a guide for champions, leaders, ‘‘belts,’’ team members and others
interested in using the Six Sigma approach to make their organizations more
efficient, more effective, or both. In short, it is a user’s manual, not a classroom
textbook.
Compared to the first edition, you will find less discussion of theory. I love
theory, but Six Sigma is quite hard-nosed in its bottom-line emphasis and I
know that serious practitioners are more interested in how to use the tools and
techniques to obtain results than in the theory underlying a particular tool.
(Of course, theory is provided to the extent necessary to understand the proper
use and limitations of a given tool.) Minitab and other software are used extensively to illustrate how to apply statistical techniques in a variety of situations
encountered during Six Sigma projects. I believe that one of the major differences between Six Sigma and previous initiatives, such as TQM, is the integration of powerful computer-based tools into the training. Many actual examples
are used, making this book something of a practical guide based on the school
of hard knocks.
Several different constituencies can benefit from this book. To serve these
constituents I separate the book into different parts. Part I is aimed at senior
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Introduction

xvii

leaders and those managers who are charged with developing strategies and
deploying the Six Sigma systems within the organization. In Part I you will

find a high level presentation of the philosophy behind Six Sigma, but I get
down to the nuts and bolts very quickly. By this I mean identifying how Six
Sigma will change the organization, and answer such questions as what are the
new positions that will be created? What knowledge, skills, abilities and personal attributes should those filling these positions possess? What personnel
assessment criteria should be used, and how can these criteria be used to evaluate candidates? Do we need to formally test applicants? What are the specific
responsibilities of people in the organization with respect to Six Sigma?
Unless such issues are carefully considered and addressed, Six Sigma will fail.
There’s no real point to training Black Belts, Green Belts, and other parts of
the Six Sigma infrastructure if the supporting superstructure isn’t in place.
Part I also addresses the issue of linking Six Sigma to the enterprise’s strategic
goals and objectives. Six Sigma is not Management By Objectives, but MBO
didn’t fail because it was an entirely bad idea. What was missing from MBO
was an understanding that results are process-driven and the development of a
resource pool and the building of an infrastructure that was dedicated to driving
the change necessary to accomplish the objectives. With Six Sigma one doesn’t
achieve objectives by directly manipulating results, but by changing the way
things are done. The driving force behind this change are the ‘‘belts,’’ who are
highly trained full- and part-time change agents. These people lead and support
projects, and it is the projects that drive change. But not just any projects will
do. Projects must be derived from the needs of the enterprise and its customers.
This is accomplished via a rigorous flow-down process that starts at the top of
the organization. In addition to describing the mechanisms that accomplish
this linkage, Part I describes the importance of rewards and incentives to success. In short, Six Sigma becomes the way senior leaders reach their goals.
Part II presents the tools and techniques of Six Sigma. Six Sigma provides
an improvement framework known as Define-Measure-Analyze-ImproveControl (DMAIC), and I have elected to organize the technical material within
the DMAIC framework. It is important to note that this isn’t always the best
way to first learn these techniques. Indeed, as a consultant I find that the Black
Belt trainee often needs to use tools from the improve or control phase while
she is still working in the define or measure phase of her project. Also,
DMAIC is often used to establish ‘‘tollgates’’ at the end of each phase to help

with project tracking, but there is usually considerable back-and-forth movement between the phases as the project progresses and one often finds that a
‘‘closed gate’’ must be kept at least partially ajar. Still, DMAIC serves the important purpose of providing a context for a given tool and a structure for the
change process.


xviii

Introduction

The presentation of DMAIC is followed by design for Six Sigma (DFSS)
principles and practices. The DFSS methodology focuses on the DefineMeasure-Analyze-Design-Verify (DMADV) approach, which builds on the
reader’s understanding of DMAIC. DFSS is used primarily when there is no
process in existence, or when the existing process is to be completely redesigned.
Finally, a chapter on Lean Manufacturing provides the reader with an overview of this important topic and discusses its relationship to Six Sigma.

DMAIC overview
.

.

.

.

.

The De¢ne phase of the book covers process mapping and £owcharting,
project charter development, problem solving tools, and the so-called 7M
tools.
Measure covers the principles of measurement, continuous and discrete

data, scales of measurement, an overview of the principles of variation,
and repeatability-and-reproducibility (RR) studies for continuous and
discrete data.
Analyze covers establishing a process base line, how to determine process
improvement goals, knowledge discovery, including descriptive and
exploratory data analysis and data mining tools, the basic principles of statistical process control (SPC), specialized control charts, process capability analysis, correlation and regression analysis, analysis of categorical
data, and non-parametric statistical methods.
Improve covers project management, risk assessment, process simulation, design of experiments (DOE), robust design concepts (including
Taguchi principles), and process optimization.
Control covers process control planning, using SPC for operational
control, and PRE-control.

DFSS covers the DMADV framework for process design, statistical tolerancing, reliability and safety, using simulation software to analyze variation and
risk, and performing ‘‘virtual DOE’’ using simulation software and artificial
neural networks.
Lean covers the basic principles of Lean, Lean tools and techniques, and a
framework for deployment. It also discusses the considerable overlap between
Lean and Six Sigma and how to integrate the two related approaches to achieve
process excellence.


^^^
PART

I
Six Sigma Implementation
and Management

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CHAPTER

1
Building the Six Sigma
Infrastructure
WHAT IS SIX SIGMA?
This section provides a 10,000 foot overview of Six Sigma. Subsequent sections elaborate and provide additional information on tools and techniques.
Six Sigma is a rigorous, focused and highly effective implementation of proven quality principles and techniques. Incorporating elements from the work
of many quality pioneers, Six Sigma aims for virtually error free business performance. Sigma, s, is a letter in the Greek alphabet used by statisticians to measure the variability in any process. A company’s performance is measured by
the sigma level of their business processes. Traditionally companies accepted
three or four sigma performance levels as the norm, despite the fact that these
processes created between 6,200 and 67,000 problems per million opportunities!
The Six Sigma standard of 3.4 problems per million opportunities* is a response
to the increasing expectations of customers and the increased complexity of
modern products and processes.
If you’re looking for new techniques, don’t bother. Six Sigma’s magic isn’t in
statistical or high-tech razzle-dazzle. Six Sigma relies on tried and true methods
that have been around for decades. In fact, Six Sigma discards a great deal of
*Statisticians note: the area under the normal curve beyond Six Sigma is 2 parts-per-billion. In calculating failure rates for Six
Sigma purposes we assume that performance experienced by customers over the life of the product or process will be much
worse than internal short-term estimates predict. To compensate, a ‘‘shift’’ of 1.5 sigma from the mean is added before calculating estimated long-term failures. Thus, you will find 3.4 parts-per-million as the area beyond 4.5 sigma on the normal curve.

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4

BUILDING THE SIX SIGMA INFRASTRUCTURE

the complexity that characterized Total Quality Management (TQM). By one
expert’s count, there were over 400 TQM tools and techniques. Six Sigma
takes a handful of proven methods and trains a small cadre of in-house technical
leaders, known as Six Sigma Black Belts, to a high level of proficiency in the
application of these techniques. To be sure, some of the methods Black Belts
use are highly advanced, including up-to-date computer technology. But the
tools are applied within a simple performance improvement model known as
Define-Measure-Analyze-Improve-Control, or DMAIC. DMAIC is described
briefly as follows:
D

De¢ne the goals of the improvement activity.

M

Measure the existing system.

A

Analyze the system to identify ways to eliminate the gap
between the current performance of the system or
process and the desired goal.

I

Improve the system.

C

Control the new system.

Why Six Sigma?
When a Japanese firm took over a Motorola factory that manufactured
Quasar television sets in the United States in the 1970s, they promptly set
about making drastic changes in the way the factory operated. Under Japanese
management, the factory was soon producing TV sets with 1/20th as many
defects as they had produced under Motorola’s management. They did this
using the same workforce, technology, and designs, and did it while lowering
costs, making it clear that the problem was Motorola’s management. It took a
while but, eventually, even Motorola’s own executives finally admitted ‘‘Our
quality stinks’’ (Main, 1994).
It took until nearly the mid-1980s before Motorola figured out what to do
about it. Bob Galvin, Motorola’s CEO at the time, started the company on
the quality path known as Six Sigma and became a business icon largely as a
result of what he accomplished in quality at Motorola. Using Six Sigma
Motorola became known as a quality leader and a profit leader. After
Motorola won the Malcolm Baldrige National Quality Award in 1988 the
secret of their success became public knowledge and the Six Sigma revolution
was on. Today it’s hotter than ever. Even though Motorola has been struggling


What Is Six Sigma?

5

the past few years, companies such as GE and AlliedSignal have taken up the

Six Sigma banner and used it to lead themselves to new levels of customer service and productivity.
It would be a mistake to think that Six Sigma is about quality in the traditional sense. Quality, defined traditionally as conformance to internal requirements, has little to do with Six Sigma. Six Sigma is about helping the
organization make more money by improving customer value and efficiency.
To link this objective of Six Sigma with quality requires a new definition of
quality. For Six Sigma purposes I define quality as the value added by a productive endeavor. Quality comes in two flavors: potential quality and actual
quality. Potential quality is the known maximum possible value added per
unit of input. Actual quality is the current value added per unit of input. The
difference between potential and actual quality is waste. Six Sigma focuses
on improving quality (i.e., reducing waste) by helping organizations produce
products and services better, faster and cheaper. There is a direct correspondence between quality levels and ‘‘sigma levels’’ of performance. For example,
a process operating at Six Sigma will fail to meet requirements about 3 times
per million transactions. The typical company operates at roughly four
sigma, which means they produce roughly 6,210 failures per million transactions. Six Sigma focuses on customer requirements, defect prevention, cycle
time reduction, and cost savings. Thus, the benefits from Six Sigma go straight
to the bottom line. Unlike mindless cost-cutting programs which also reduce
value and quality, Six Sigma identifies and eliminates costs which provide no
value to customers, waste costs.
For non-Six Sigma companies, these costs are often extremely high.
Companies operating at three or four sigma typically spend between 25 and 40
percent of their revenues fixing problems. This is known as the cost of quality,
or more accurately the cost of poor quality. Companies operating at Six Sigma
typically spend less than 5 percent of their revenues fixing problems (Figure
1.1). COPQ values shown in Figure 1.1 are at the lower end of the range of
results reported in various studies. The dollar cost of this gap can be huge.
General Electric estimated that the gap between three or four sigma and Six
Sigma was costing them between $8 billion and $12 billion per year.
One reason why costs are directly related to sigma levels is very simple: sigma
levels are a measure of error rates, and it costs money to correct errors. Figure
1.2 shows the relationship between errors and sigma levels. Note that the error
rate drops exponentially as the sigma level goes up, and that this correlates

well to the empirical cost data shown in Figure 1.1. Also note that the errors
are shown as errors per million opportunities, not as percentages. This is
another convention introduced by Six Sigma. In the past we could tolerate percentage error rates (errors per hundred opportunities), today we cannot.