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E-raamat: Six Sigma Handbook, 5E

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  • Formaat: 704 pages
  • Ilmumisaeg: 07-Sep-2018
  • Kirjastus: McGraw-Hill Education
  • Keel: eng
  • ISBN-13: 9781260121834
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Six Sigma Handbook, 5ESuurem pilt
  • Formaat: 704 pages
  • Ilmumisaeg: 07-Sep-2018
  • Kirjastus: McGraw-Hill Education
  • Keel: eng
  • ISBN-13: 9781260121834
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The most important reference to Lean Six Sigma fully updated for the latest advances

This thoroughly revised, industry standard guide delivers all the information you need to apply Lean Six Sigma techniques and dramatically improve processes, profitability, sustainability, and long-term growth. Written by two of the foremost authorities in the field, the book contains full explanations of the latest lean, problem solving and change management principles and methods. You will discover how to build the best teams and foster effective leadership while maximizing customer satisfaction and boosting profits. The book includes coverage of the recently released Minitab 18.

The Six Sigma Handbook, Fifth Edition covers:
•Building the responsive Six Sigma organization
•Recognizing and capitalizing on opportunity
•Data-driven management
•Maximizing resources
•Project management using DMAIC and DMADV
•The define phase
•The measure phase
•Process behavior charts
•Measurement systems evaluation
•The analyze phase
•The improve/design phase
•The control/verify phase


Preface xiii
Part I Six Sigma Implementation and Management
Chapter 1 Building the Responsive Six Sigma Organization
3(58)
What Is Six Sigma?
3(14)
Why Six Sigma?
4(2)
The Six Sigma Philosophy
6(2)
Six Sigma Versus Traditional Three Sigma Performance
8(4)
The Change Imperative
12(5)
Implementing Six Sigma
17(44)
Timetable
18(3)
Infrastructure
21(17)
Integrating Six Sigma and Related Initiatives
38(14)
Deployment to the Supply Chain
52(2)
Communications and Awareness
54(7)
Chapter 2 Recognizing and Capitalizing on Opportunity
61(84)
Methods for Collecting Customer Data
61(14)
Surveys
62(9)
Focus Groups
71(1)
Operational Feedback Systems
72(3)
Cost of Poor Quality
75(7)
Cost of Quality Examples
78(3)
Quality Cost Bases
81(1)
Benchmarking
82(5)
The Benchmarking Process
82(1)
Getting Started with Benchmarking
83(2)
Why Benchmarking Efforts Fail
85(1)
The Benefits of Benchmarking
86(1)
Some Dangers of Benchmarking
87(1)
Innovation
87(19)
Kano Model
88(1)
Quality Function Deployment
89(4)
Translating Customer Demands
93(8)
Creative Destruction
101(5)
Strategic Planning
106(7)
Organizational Vision
107(2)
Strategy Development
109(1)
Strategic Styles
110(1)
Possibilities-Based Strategic Decisions
111(2)
Strategic Development Using Constraint Theory
113(32)
The Systems Approach
114(3)
Basic Constraint Management Principles and Concepts
117(9)
Tools of Constraint Management
126(12)
Constraint Management Measurements
138(5)
Summary and Conclusion
143(2)
Chapter 3 Data-Driven Management
145(32)
Attributes of Good Metrics
145(4)
Measuring Causes and Effects
147(2)
The Balanced Scorecard
149(28)
Translating the Vision
151(8)
Communicating and Linking
159(3)
Business Planning
162(4)
Feedback and Learning
166(11)
Chapter 4 Maximizing Resources
177(34)
Choosing the Right Projects
177(18)
Types of Projects
178(1)
Analyzing Project Candidates
179(8)
Using Pareto Analysis to Identify Six Sigma Project Candidates
187(2)
Throughput-Based Project Selection
189(6)
Ongoing Management Support
195(6)
Internal Roadblocks
196(1)
External Roadblocks
197(1)
Individual Barriers to Change
197(1)
Ineffective Management Support Strategies
198(1)
Effective Management Support Strategies
199(1)
Cross-Functional Collaboration
200(1)
Tracking Six Sigma Project Results
201(10)
Financial Results Validation
204(1)
Team Performance Evaluation
204(1)
Team Recognition and Reward
205(2)
Lessons-Learned Capture and Replication
207(4)
Part II Six Sigma Tools and Techniques
Chapter 5 Project Management Using DMAIC and DMADV
211(32)
DMAIC and DMADV Deployment Models
211(21)
Project Scheduling
216(12)
Project Reporting
228(2)
Project Budgets
230(1)
Project Records
231(1)
Six Sigma Teams
232(11)
Team Membership
233(1)
Team Dynamics Management, Including Conflict Resolution
233(1)
Stages in Group Development
234(2)
Member Roles and Responsibilities
236(2)
Management's Role
238(1)
Facilitation Techniques
238(5)
Chapter 6 The Define Phase
243(26)
Project Charters
243(3)
Project Decomposition
246(2)
Work Breakdown Structures
246(1)
Pareto Analysis
247(1)
Deliverables
248(17)
Critical to Quality Metrics
250(7)
Critical to Schedule Metrics
257(2)
Critical to Cost Metrics
259(6)
Top-Level Process Definition
265(2)
Process Maps
266(1)
Assembling the Team
267(2)
Chapter 7 The Measure Phase
269(22)
Process Definition
269(6)
Flowcharts
270(1)
SIPOC
271(4)
Metric Definition
275(3)
Measurement Scales
276(2)
Discrete and Continuous Data
278(1)
Process Baseline Estimates
278(13)
Enumerative and Analytic Studies
280(3)
Principles of Statistical Process Control
283(6)
Estimating Process Baselines Using Process Capability Analysis
289(2)
Chapter 8 Process Behavior Charts
291(100)
Distributions
291(19)
Methods of Enumeration
291(2)
Frequency and Cumulative Distributions
293(1)
Sampling Distributions
294(1)
Binomial Distribution
295(2)
Poisson Distribution
297(1)
Hypergeometric Distribution
298(1)
Normal Distribution
299(7)
Lognormal Distribution
306(1)
Exponential Distribution
307(2)
Weibull Distribution
309(1)
Control Charts for Variables Data
310(13)
Averages and Ranges Control Charts
310(4)
Averages and Standard Deviation (Sigma) Control Charts
314(3)
Control Charts for Individual Measurements (X Charts)
317(6)
Control Charts for Attributes Data
323(13)
Control Charts for Proportion (p Charts)
323(4)
Control Charts for Count of Items (np Charts)
327(2)
Control Charts for Average Occurrences-Per-Unit (u Charts)
329(3)
Control Charts for Counts of Occurrences-Per-Unit (c Charts)
332(4)
Control Chart Selection
336(5)
Rational Subgroup Sampling
336(5)
Control Chart Interpretation
341(7)
Run Tests
347(1)
Short-Run Statistical Process Control Techniques
348(21)
Variables Data
350(11)
Attribute SPC for Small and Short Runs
361(7)
Summary of Short-Run SPC
368(1)
SPC Techniques for Automated Manufacturing
369(11)
Problems with Traditional SPC Techniques
369(1)
Special and Common Cause Charts
370(1)
EWMA Common Cause Charts
371(7)
EWMA Control Charts Versus Individuals Charts
378(2)
Process Capability Indices
380(11)
Example of Non-Normal Capability Analysis Using Minitab
385(6)
Chapter 9 Measurement Systems Evaluation
391(32)
Definitions
391(19)
Measurement System Discrimination
395(1)
Stability
395(1)
Bias
396(1)
Repeatability
397(3)
Reproducibility
400(3)
Part-to-Part Variation
403(1)
Summary Reporting
403(1)
Gage R&R Analysis Using Minitab
404(3)
Linearity
407(2)
Linearity Analysis Using Minitab
409(1)
Attribute Measurement Error Analysis
410(13)
Operational Definitions
412(1)
How to Conduct Attribute Inspection Studies
413(4)
Minitab Attribute Gage R&R Example
417(6)
Chapter 10 Analyze Phase
423(94)
Value Stream Analysis
423(11)
Value Stream Mapping
427(6)
Spaghetti Charts
433(1)
Analyzing the Sources of Variation
434(26)
Cause and Effect Diagrams
435(2)
Boxplots
437(2)
Statistical Inference
439(1)
Chi-Square, Student's t, and f Distributions
440(4)
Point and Interval Estimation
444(8)
Hypothesis Testing
452(7)
Resampling (Bootstrapping)
459(1)
Regression and Correlation Analysis
460(12)
Linear Models
463(3)
Least-Squares Fit
466(4)
Correlation Analysis
470(2)
Designed Experiments
472(27)
The Traditional Approach Versus Statistically Designed Experiments
472(1)
Terminology
472(2)
Design Characteristics
474(1)
Types of Design
475(2)
One-Factor ANOVA
477(2)
Two-Way ANOVA with No Replicates
479(1)
Two-Way ANOVA with Replicates
480(2)
Full and Fractional Factorial
482(9)
Power and Sample Size
491(1)
Testing Common Assumptions
492(7)
Analysis of Categorical Data
499(14)
Making Comparisons Using Chi-Square Tests
499(2)
Logistic Regression
501(2)
Binary Logistic Regression
503(3)
Ordinal Logistic Regression
506(4)
Nominal Logistic Regression
510(3)
Non-Parametric Methods
513(4)
Chapter 11 The Improve/Design Phase
517(70)
Using Customer Demands to Make Design and Improvement Decisions
517(1)
Pugh Concept Selection Method
517(1)
Lean Techniques for Optimizing Flow
518(11)
Unnecessary Process Steps
519(1)
Excessive Movement of Material or Personnel
519(1)
Bottleneck or Constraint
520(1)
Process Errors Requiring Rework
521(1)
Excess In-process Inventory
521(5)
Understanding Queues to Balance Processes
526(3)
Using Empirical Model Building to Optimize
529(19)
Phase 0: Getting Your Bearings
531(1)
Phase I: The Screening Experiment
532(4)
Phase II: Steepest Ascent (Descent)
536(2)
Phase III: The Factorial Experiment
538(2)
Phase IV: The Composite Design
540(4)
Phase V: Robust Product and Process Design
544(4)
Data Mining, Artificial Neural Networks, and Virtual Process Mapping
548(4)
Example of Neural Net Models
549(3)
Optimization Using Simulation
552(20)
Predicting CTQ Performance
552(1)
Simulation Tools
553(4)
Random Number Generators
557(3)
Model Development
560(10)
Virtual DOE Using Simulation Software
570(2)
Risk Assessment Tools
572(9)
Design Review
573(1)
Fault-Tree Analysis
574(1)
Safety Analysis
575(3)
Failure Mode and Effect Analysis
578(3)
Defining New Performance Standards Using Statistical Tolerancing
581(6)
Assumptions of Formula
585(1)
Tolerance Intervals
585(2)
Chapter 12 Control/Verify Phase
587(30)
Validating the New Process or Product Design
587(1)
Business Process Control Planning
588(11)
Maintaining Gains
588(2)
Tools and Techniques Useful for Control Planning
590(1)
Preparing the Process Control Plan
591(2)
Process Control Planning for Short and Small Runs
593(3)
Process Audits
596(1)
Selecting Process Control Elements
596(3)
Other Elements of the Process Control Plan
599(1)
Multivariate Control Charts
599(9)
Principal Component Analysis
608(9)
Appendix 1 Glossary of Basic Statistical Terms 617(6)
Appendix 2 Area Under the Standard Normal Curve 623(4)
Appendix 3 Critical Values of the t-Distribution 627(2)
Appendix 4 Chi-Square Distribution 629(2)
Appendix 5 F Distribution (α = 1%) 631(2)
Appendix 6 F Distribution (α = 5%) 633(2)
Appendix 7 Poisson Probability Sums 635(4)
Appendix 8 Tolerance Interval Factors 639(4)
Appendix 9 Control Chart Constants 643(2)
Appendix 10 Control Chart Equations 645(2)
Appendix 11 Table of d2* Values 647(2)
Appendix 12 Factors for Short Run Control Charts for Individuals, X, and R Charts 649(2)
Appendix 13 Sample Customer Survey 651(2)
Appendix 14 Process a Levels and Equivalent PPM Quality Levels 653(2)
Appendix 15 Black Belt Effectiveness Certification 655(12)
Appendix 16 Green Belt Effectiveness Certification 667(12)
Appendix 17 AHP Using Microsoft Excel 679(4)
Notes 683(2)
References 685(8)
Index 693
Thomas Pyzdek is a quality consultant, entrepreneur, author, educator, software developer, and speaker. He writes a regular column for Quality Digest and is a Fellow of the American Society for Quality (ASQ). He conducts seminars on Six Sigma for the American Society for Quality, and has received that organizations "Edwards" award. His clients and employers cover a wide range of industries including Ford, GM, McDonald's, Avon, Hughes Aircraft Company Missile Systems Group, and the Tucson Medical Center.





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