www.EngineeringEbooksPdf.comThe
Six Sigma
Handbook
A Complete Guide for Green Belts,
Black Belts, and Managers at All Levels
Thomas Pyzdek
Paul A. Keller
Third Edition
New York Chicago San Francisco
Lisbon London Madrid Mexico City
Milan New Delhi San Juan
Seoul Singapore Sydney Toronto
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www.EngineeringEbooksPdf.comContents
Preface
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xi
Part I Six Sigma Implementation and Management
1 Building the Responsive Six Sigma Organization
What Is Six Sigma?
Why Six Sigma?
The Six Sigma Philosophy
The Change Imperative
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Implementing Six Sigma
Timetable
Infrastructure
Integrating Six Sigma and Related Initiatives
Deployment to the Supply Chain
Communications and Awareness
2 Recognizing Opportunity
Becoming a Customer and Market-Driven Enterprise
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Customer Expectations, Priorities, Needs, and “Voice”
Quality Function Deployment
The Six Sigma Process Enterprise
Elements of the Transformed Organization
Strategies for Communicating with
Customers and Employees
Survey Development Case Study
Calculating the Value of Customer Retention
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The Source of Confl ict
A Resolution to the Confl ict
Six Sigma and the Process Enterprise
Linking Six Sigma Projects to Strategies
The Strategy Deployment Matrix
Deploying Differentiators to Operations
Deploying Operations Plans to Projects
Interpretation
Linking Customer Demands to Budgets
Structured Decision-Making
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Publicité
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C o n t e n t s
3 Data-Driven Management
Attributes of Good Metrics
The Balanced Scorecard
Measuring Causes and Effects
Customer Perspective
Internal Process Perspective
Innovation and Learning Perspective
Financial Perspective
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Cost of Quality Examples
Strategy Deployment Plan
Dashboard Design
Information Systems Requirements
Cost of Poor Quality
Integrating Six Sigma with Other Information
Benchmarking
Systems Technologies
Data Warehousing
OLAP
Data Mining
OLAP, Data Mining, and Six Sigma
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The Benchmarking Process
Getting Started with Benchmarking
Why Benchmarking Efforts Fail
The Benefi ts of Benchmarking
Some Dangers of Benchmarking
4 Maximizing Resources
Choosing the Right Projects
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Types of Projects
Analyzing Project Candidates
Using Pareto Analysis to Identify
Six Sigma Project Candidates
Throughput-Based Project Selection
Ongoing Management Support
Internal Roadblocks
External Roadblocks
Individual Barriers to Change
Ineffective Management Support Strategies
Effective Management Support Strategies
Cross-Functional Collaboration
Tracking Six Sigma Project Results
Financial Results Validation
Team Performance Evaluation
Team Recognition and Reward
Lessons-Learned Capture and Replication
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PART II Six Sigma Tools and Techniques
C o n t e n t s
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5 Project Management Using DMAIC and DMADV
DMAIC and DMADV Deployment Models
Project Reporting
Project Budgets
Project Records
Six Sigma Teams
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Team Membership
Team Dynamics Management, Including
Confl ict Resolution
Stages in Group Development
Member Roles and Responsibilities
Management’s Role
Facilitation Techniques
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Deliverables
6 The Defi ne Phase
Project Charters
Project Decomposition
Work Breakdown Structures
Pareto Analysis
Critical to Quality Metrics
Critical to Schedule Metrics
Critical to Cost Metrics
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Publicité
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Project Scheduling
Gantt Charts
PERT-CPM
Control and Prevention of Schedule Slippage
Cost Considerations in Project Scheduling
Top-Level Process Defi nition
Process Maps
Assembling the Team
7 The Measure Phase
Process Defi nition
Flowcharts
SIPOC
Metric Defi nition
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Enumerative and Analytic Studies
Principles of Statistical Process Control
Measurement Scales
Discrete and Continuous Data
Process Baseline Estimates
Estimating Process Baselines Using Process
Capability Analysis
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vi
C o n t e n t s
8 Process Behavior Charts
Control Charts for Variables Data
Averages and Ranges Control Charts
Averages and Standard Deviation (Sigma)
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Control Charts for Attributes Data
Control Charts
Control Charts for Individual Measurements (X Charts)
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Control Charts for Proportion Defective (p Charts)
Control Charts for Count of Defectives (np Charts)
Control Charts for Average
Control Chart Selection
SPC Techniques for Automated Manufacturing
Control Chart Interpretation
Run Tests
Tampering Effects and Diagnosis
Short Run Statistical Process Control Techniques
Occurrences-Per-Unit (u Charts)
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Control Charts for Counts of
Rational Subgroup Sampling
Occurrences-Per-Unit (c Charts)
Variables Data
Attribute SPC for Small and Short Runs
Summary of Short-Run SPC
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Methods of Enumeration
Frequency and Cumulative Distributions
Sampling Distributions
Binomial Distribution
Poisson Distribution
Hypergeometric Distribution
Normal Distribution
Exponential Distribution
Problems with Traditional SPC Techniques
Special and Common Cause Charts
EWMA Common Cause Charts
EWMA Control Charts versus Individuals Charts
Distributions
9 Measurement Systems Evaluation
Defi nitions
Measurement System Discrimination
Stability
Bias
Repeatability
Reproducibility
Part-to-Part Variation
Example of Measurement System Analysis Summary
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Gage R&R Analysis Using Minitab
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C o n t e n t s
vii
Linearity
Linearity Analysis Using Minitab
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Attribute Measurement Error Analysis
Operational Defi nitions
How to Conduct Attribute Inspection Studies
Publicité
Example of Attribute Inspection Error Analysis
Minitab Attribute Gage R&R Example
10 Analyze Phase
Value Stream Analysis
Regression and Correlation Analysis
Value Stream Mapping
Spaghetti Charts
Analyzing the Sources of Variation
Cause and Effect Diagrams
Boxplots
Statistical Inference
Chi-Square, Student’s T, and F Distributions
Point and Interval Estimation
Hypothesis Testing
Resampling (Bootstrapping)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
. . . . . . . . . . . . . . . 332
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 336
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341
. . . . . . . . . . . . . . . . . . . . . . . . . . . 342
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356
. . . . . . . . . . . . . . . . . . . . 359
. . . . . . . . . . . . . . . . . . . . . . . 360
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369
. . . . . . . . . . . . . . . . . . . . . . . . . . 369
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376
Terminology
Design Characteristics
Types of Design
One-Factor ANOVA
Two-Way ANOVA with No Replicates
Two-Way ANOVA with Replicates
Full and Fractional Factorial
Power and Sample Size
Testing Common Assumptions
Linear Models
Least-Squares Fit
Correlation Analysis
Analysis of Categorical Data
Designed Experiments
Making Comparisons Using
Chi-Square Tests
Logistic Regression
Binary Logistic Regression
Ordinal Logistic Regression
Nominal Logistic Regression
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 385
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389
Non-Parametric Methods
11 The Improve/Design Phase
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393
Using Customer Demands to Make Design and
Improvement Decisions
Category Importance Weights
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393
. . . . . . . . . . . . . . . . . . . . . . . . . . . 394
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C o n t e n t s
Tools to Help Improve Flow
Lean Techniques for Optimizing Flow
Using Empirical Model Building to Optimize
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
. . . . . . . . . . . . . . . . . . . . . . . . . . 400
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400
. . . . . . . . . . . . . . . . . . . . 402
. . . . . . . . . . . . . . . . . . . . . . . . . . . 403
. . . . . . . . . . . . . . . . . . . . . . . 404
. . . . . . . . . . . . . . . . . . . . . . . 408
. . . . . . . . . . . . . . . . . . . . . . . 408
. . . . . . . . . . . . . . . . . . . . . . . . . 411
. . . . . . . . . . . . . . 415
Data Mining, Artifi cial Neural Networks, and Virtual
Process Mapping
Example of Neural Net Models
Optimization Using Simulation
Predicting CTQ Performance
Simulation Tools
Random Number Generators
Model Development
Virtual Doe Using Simulation Software
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419
. . . . . . . . . . . . . . . . . . . . . . . . . . 420
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 423
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 427
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431
. . . . . . . . . . . . . . . . . . . 438
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444
. . . . . . . . . . . . . . . . . . . . . . . . 447
Risk Assessment Tools
Design Review
Fault-Tree Analysis
Safety Analysis
Failure Mode and Effect Analysis
Defi ning New Performance Standards Using
Statistical Tolerancing
Assumptions of Formula
Tolerance Intervals
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454
12 Control/Verify Phase
Validating the New Process or Product Design
Business Process Control Planning
Maintaining Gains
Tools and Techniques Useful for Control Planning
Preparing the Process Control Plan
Process Control Planning for Short and Small Runs
Process Audits
Selecting Process Control Elements
Other Elements of the Process Control Plan
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455
. . . . . . . . . . . . . . . . . . . 455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456
. . . . . . . . . . 457
Publicité
. . . . . . . . . . . . . . . . . . . . . . . 458
. . . . . . . . . 460
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462
. . . . . . . . . . . . . . . . . . . . . . . 462
. . . . . . . . . . . . . . . . 465
Appendices
A1 Glossary of Basic Statistical Terms
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 469
A2 Area Under the Standard Normal Curve
. . . . . . . . . . . . . . . . . . . . . . . 475
A3 Critical Values of the t-Distribution
. . . . . . . . . . . . . . . . . . . . . . . . . . . 479
A4 Chi-Square Distribution
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481
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C o n t e n t s
ix
A5 F Distribution (a = 1%)
A6 F Distribution (a = 5%)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485
A7 Poisson Probability Sums
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487
A8 Tolerance Interval Factors
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491
A9 Control Chart Constants
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495
A10 Control Chart Equations
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497
A11 Table of d2* Values
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499
A12
Factors for Short Run Control Charts for Individuals,
x-bar, and R Charts
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501
A13 Sample Customer Survey
A14 Process s Levels and Equivalent PPM Quality Levels
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503
. . . . . . . . . . . . 505
A15 Black Belt Effectiveness Certifi cation
Introduction
Process
[COMPANY] Black Belt Skill Set Certifi cation Process
. . . . . . . . . . . . . . . . . . . . . . . . . . 507
. . . . . . . . . . . . . 507
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507
. . . . . . . . 508
. . . . . . . . . . . . . . . . . . . . . 509
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509
. . . . . . . . . . . . . . 510
[COMPANY] Black Belt Effectiveness Certifi cation Criteria
[COMPANY] Black Belt Certifi cation Board
Effectiveness Questionnaire
[COMPANY] Black Belt Notebook and Oral Review
A16 Green Belt Effectiveness Certifi cation
Introduction
Green Belt Skill Set Certifi cation Process
. . . . . . . . . . . . . . . . . . . . . . . . . 519
. . . . . . . . . . . . . . . . . . . . . . . . 519
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519
. . . . . . . . . . . . . . . . . . . . 520
. . . . . . . . . . . . . . . . . . . . . . . . . . . 521
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 522
Green Belt Effectiveness Certifi cation Criteria
Green Belt Certifi cation Board
Effectiveness Questionnaire
Scoring Guidelines
Green Belt Notebook
A17 AHP Using Microsoft ExcelTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531
Example
References
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533
Index
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537
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About the Authors
THOMAS PYZDEK is author or coauthor of more than 50
books including The Six Sigma Handbook, The Quality Engi-
neering Handbook, and The Handbook of Quality Manage-
ment. His works are used by thousands of universities and
organizations around the world to teach process excel-
lence. Mr. Pyzdek has provided training and consulting to
employers and clients in all industries since 1967. He pro-
vides consulting guidance from the executive suite to “Belts”
working in the trenches. In his online and live public semi-
nars and client classes Mr. Pyzdek has taught Six Sigma,
Lean, Quality and other business process improvement
methodologies to thousands.
Mr. Pyzdek is a Fellow of ASQ and recipient of the
ASQ Edward’s Medal and the Simon Collier Quality
Award, both for outstanding contributions to the field of
quality management, and the ASQ E.L. Grant Medal for
outstanding contributions to Quality Education. He serves
on numerous editorial boards, including The Quality Man-
agement Journal, Quality Engineering, and International Jour-
nal of Six Sigma and Competitive Advantage.
PAUL KELLER is vice president of and senior consultant with
Quality America, Inc. He has developed and implemented
successful Six Sigma and Quality Improvement programs
in service and manufacturing environments. Mr. Keller
(just for consistency!) has been with Quality America since
1992, where he has:
• Developed and managed systems for overall operations,
including quality improvement, product development,
partner relations, marketing, sales, order fulfillment, and
technical support.
• Provided primary statistical expertise to customers, as
well as to internal software development, sales, and
technical support teams.
• Developed and implemented Six Sigma related courses,
including Quality Management, Statistical Process Con-
trol (SPC), and Designed Experiments, to hundreds of
companies in a wide variety of industries including
Roche Pharmaceuticals, Core3 Inc. Business Process Out-
sourcing, U.S. Army, MacDermid Printing Solutions,
Boeing Satellite, Dow Corning, Antec, Pfizer, Warner
Lambert, and many others.
www.EngineeringEbooksPdf.comPreface
The Six Sigma approach has been adopted by a growing majority of the Fortune
500 companies, as well as many small and mid-sized organizations. Its application
in both for-profit and non-profit organizations is a reflection of its broad objectives
in improving processes at the core of an organization’s mission. While initial perceptions
often focus on quality improvements, successful deployments look beyond to profitability,
sustainability, and long term growth.
As these words are written, what is now the longest and deepest recession since
the Great Depression has upset a record period of global growth and expansion. During
the expansion, Six Sigma proved a valuable strategy to meet the strong market demand
for products and services through capacity and productivity improvements and
focus on reduced time to market. Where competitive pressures from emerging global
markets were especially strong, service improvement, cost of delivery and cost to
manufacture strategies proved successful. This recession has been labeled a “game
changer” by more than a few economists, upsetting supply chains and forcing entire
industries to rethink their business model. There will certainly be many organizational
casualties of this recession in a wide array of industries. Yet, there will undoubtedly be
survivors, who will gain market share and become the pillars of this new century.
Those organizations will focus first on core businesses, ensuring continued market
share and profitability. They will apply structured Six Sigma efforts directed at key
cost, quality and service objectives. This will demand a fresh look at their internal
processes, from the eyes of their customer base, to maximize value and reduce cost.
They will then seize new opportunities, left open by the weakened competition. Their
ability...