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The Six Sigma Handbook

The Six Sigma Handbook

By Thomas Pyzdek, Paul A. Keller

Upgrade your business and your employees with the theories and ideas presented in this essential text.

Price: $109.00
Product Code/ISBN: 9781260121827
Year: 2018
Binding: Hardcover     Page Count: 720
Publisher: McGraw-Hill Education


How do you build the best teams? How do you create effective leadership? How do you boost your profits while still having satisfied customers? All of these answers and more are now available through the Six Sigma Handbook. Upgrade your business and your employees with the theories and ideas presented in this essential text.

Table of Contents

  1. Building the Responsive Six Sigma Organization
    1. What is Six Sigma?
    2. Why Six Sigma?
    3. The Six Sigma Philosophy
    4. Six Sigma Versus Traditional Three Sigma Performance
    5. The Change Imperative
    6. Implementing Six Sigma
    7. Timetable
    8. Infrastructure
    9. Integrating Six Sigma and Related Initiatives
    10. Deployment to the Supply Chain
    11. Communications and Awareness
  2. Recognizing and Capitalizing on Opportunity
    1. Methods for Collecting Customer Data
    2. Surveys
    3. Focus Groups
    4. Operational Feedback Systems
    5. Cost of Poor Quality
    6. Cost of Quality Examples
    7. Quality Cost Bases
    8. Benchmarking
    9. The Benchmarking Process
    10. Getting Started with Benchmarking
    11. Why Benchmarking Efforts Fail
    12. The Benefits of Benchmarking
    13. Some Dangers of Benchmarking
    14. Innovation
    15. Kano Model
    16. Quality Function Deployment
    17. Translating Customer Demands
    18. Creative Destruction
    19. Strategic Planning
    20. Organizational Vision
    21. Strategy Development
    22. Strategic Styles
    23. Possibilities-Based Strategic Decisions
    24. Strategic Development Using Constraint Theory
    25. The Systems Approach
    26. Basic Constraint Management Principles and Concepts
    27. Tools of Constraint Management
    28. Constraint Management Measurements
    29. Summary and Conclusion
  3. Data-Driven Management
    1. Attributes of Good Metrics
    2. Measuring Causes and Effects
    3. The Balanced Scorecard
    4. Translating the Vision
    5. Communicating the Linking
    6. Business Planning
    7. Feedback and Learning
  4. Maximizing Resources
    1. Choosing the Right Projects
    2. Types of Projects
    3. Analyzing Project Candidates
    4. Using Pareto Analysis to Identify Six Sigma Project Candidates
    5. Throughput-Based Project Selection
    6. Ongoing Management Support
    7. Internal Roadblocks
    8. External Roadblocks
    9. Individual Barriers to Change
    10. Ineffective Management Support Strategies
    11. Effective Management Support Strategies
    12. Cross-Functional Collaboration
    13. Tracking Six Sigma Project Results
    14. Financial Results Validation
    15. Team Performance Evaluation
    16. Team Recognition and Reward
    17. Lessons-Learned Capture and Replication
  5. Project Management Using DMAIC and DMADV
    1. DMAIC and DMADV Deployment Models
    2. Project Scheduling
    3. Project Reporting
    4. Project Budgets
    5. Project Records
    6. Six Sigma Teams
    7. Team Membership
    8. Team Dynamics Management, Including Conflict Resolution
    9. Stages in Group Development
    10. Member Roles and Responsibilities
    11. Management’s Role
    12. Facilitation Techniques
  6. The Define Phase
    1. Project Charters
    2. Project Decomposition
    3. Work Breakdown Structures
    4. Pareto Analysis
    5. Deliverables
    6. Critical to Quality Metrics
    7. Critical to Schedule Metrics
    8. Critical to Cost Metrics
    9. Top-Level Process Definition
    10. Process Maps
    11. Assembling the Team
  7. The Measure Phase
    1. Process Definition
    2. Flowcharts
    3. SIPOC
    4. Metric Definition
    5. Measurement Scales
    6. Discrete and Continuous Data
    7. Process Baseline Estimates
    8. Enumerative and Analytic Studies
    9. Principles of Statistical Process Control
    10. Estimating Process Baselines Using Process Capability Analysis
  8. Process Behavior Charts
    1. Distributions
    2. Methods of Enumeration
    3. Frequency and Cumulative Distributions
    4. Sampling Distributions
    5. Binomial Distribution
    6. Hypergeometric Distribution
    7. Normal Distribution
    8. Lognormal Distribution
    9. Exponential Distribution
    10. Weibull Distribution
    11. Control Charts for Variable Data
    12. Averages and Ranges Control Charts
    13. Averages and Standard Deviation (Sigma) Control Charts
    14. Control Charts for Individual Measurements (X Charts)
    15. Control Charts for Attributes Data
    16. Control Charts for Proportion (p Charts)
    17. Control Charts for Count of Items (np Charts)
    18. Control Charts for Average Occurrences-Per-Unit (u Charts)
    19. Control Charts for Counts of Occurrences-Per-Unit (c Charts)
    20. Control Chart Selection
    21. Rational Subgroup Sampling
    22. Control Chart Interpretation
    23. Run Tests
    24. Short-Run Statistical Process Control Techniques
    25. Variables Data
    26. Attribute SPC for Small and Short Runs
    27. Summary of Short-Run SPC
    28. SPC Techniques for Automated Manufacturing
    29. Problems with Traditional SPC Techniques
    30. Special and Common Cause Charts
    31. EWMA Common Cause Charts
    32. EWMA Control Charts Versus Individuals Charts
    33. Process Capability Indices
    34. Example of Non-Normal Capability Analysis Using Minitab
  9. Measurement Systems Evaluation
    1. Definitions
    2. Measurement system Discrimination
    3. Stability
    4. Bias
    5. Repeatability
    6. Reproducibility
    7. Part-to-part Variation
    8. Summary Reporting
    9. Gage R&R Analysis Using Minitab
    10. Linearity
    11. Linearity Analysis Using Minitab
    12. Attribute Measurement Error Analysis
    13. Operational Definitions
    14. How to Conduct Attribute Inspection Studies
    15. Minitab Attribute Gage R&R Example
  10. Analyze Phase
    1. Value Stream Analysis
    2. Value Stream Mapping
    3. Spaghetti Charts
    4. Analyzing the Sources of Variation
    5. Cause and Effect Diagrams
    6. Boxplots
    7. Statistical Inference
    8. Chi-Square, Student’s t, and f Distributions
    9. Point and Interval Estimation
    10. Hypothesis Testing
    11. Resampling (Bootstrapping)
    12. Regression and Correlation Analysis
    13. Linear Models
    14. Least-Square Fit
    15. Correlation Analysis
    16. Designed Experiments
    17. The Traditional Approach Versus Statistically Designed Experiments
    18. Terminology
    19. Design Characteristics
    20. Types of Design
    21. One-Factor ANOVA
    22. Two-Way ANOVA with NO Replicates
    23. Two-Way ANOVA with Replicates
    24. Full and Fractional Factorial
    25. Power and Sample Size
    26. Testing Common Assumptions
    27. Analysis of Categorical Data
    28. Making Comparisons Using Chi-Square Tests
    29. Logistic Regression
    30. Binary Logistic Regression
    31. Ordinal Logistic Regression
    32. Nominal Logistic Regression
    33. Non-Parametric Methods
  11. The Improve/Design Phase
    1. Using Customer Demands to Make Design and Improvement Decisions
    2. Pugh Concept Selection Method
    3. Lean Techniques for Optimizing Flow
    4. Unnecessary Process Steps
    5. Excessive Movement of Material of Personnel
    6. Bottleneck or Constraint
    7. Process Errors Requiring Rework
    8. Excess In-Process Inventory
    9. Understanding Queues to Balance Processes
    10. Using Empirical Model Buildings to Optimize
    11. Phase 0: Getting Your Bearings
    12. Phase I: The Screening Experiment
    13. Phase II: Steepest Ascent (Descent)
    14. Phase III: The Factorial Experiment
    15. Phase IV: The Composite Design
    16. Phase V: Robust Product and Process Design
    17. Data Mining, Artificial Neural Networks, and Virtual Process Mapping
    18. Example of Neural Net Models
    19. Optimization Using Simulation
    20. Predicting CTQ Performance
    21. Simulation Tools
    22. Random Number Generators
    23. Model Development
    24. Virtual DOE Using Simulation Software
    25. Risk Assessment Tools
    26. Design Review
    27. Fault-Tree Analysis
    28. Fault-Tree Analysis
    29. Safety Analysis
    30. Failure Mode and Effect Analysis
    31. Defining New Performance Standards Using Statistical Tolerancing
    32. Assumptions of Formula
    33. Tolerance Intervals
  12. Control/Verify Phase
    1. Validating the New Process or Product Design
    2. Business Process Control Planning
    3. Maintaining Gains
    4. Tools and Techniques Useful for Control Planning
    5. Preparing for the Process Control Plan
    6. Process Control Planning for Short and Small Runs
    7. Process Audits
    8. Selecting Process Control Elements
    9. Other Elements of the Process Control Plan
    10. Multivariate Control Charts
    11. Principle Component Analysis

Appendix 1 – Glossary of Basic Statistical Terms

Appendix 2 – Area Under the Standard Normal Curve

Appendix 3 – Critical Values of the t-Distribution

Appendix 4 – Chi-Square Distribution

Appendix 5 – F Distribution (a=1%)

Appendix 6 – F Distribution (a=5%)

Appendix 7 – Poisson Probability Sums

Appendix 8 – Tolerance Interval Factors

Appendix 9 – Control Chart Constants

Appendix 10 – Control Chart Equations

Appendix 11 – Table of d2* Values

Appendix 12 Factors for Short Run Control Charts for Individuals, X, and R Charts

Appendix 13 – Sample Customer Survey

Appendix 14 – Process s Levels and Equivalent PPM Quality Levels

Appendix 15 – Black Belt Effectiveness Certification

Appendix 16 – Green Belt Effectiveness Certification

Appendix 17 – AHP Using Microsoft Excel