Six Sigma for Green Belts and Champions: Foundations, DMAIC, Tools, Cases, and Certification (paperback)
©2005 |FT Press |
Howard S. Gitlow, University of Miami, FL
David M. Levine
©2005 |FT Press |
I. SIX SIGMA BASICS.
1. Overview of Six Sigma Management.
Successful Applications of Six Sigma Management.
Timeline for Six Sigma Management.
Key Ingredients for Success with Six Sigma Management.
Benefits of Six Sigma Management.
Process Basics (Voice of the Process).
Definition of a Process.
Variation in a Process.
Definition of Quality (Voice of the Customer).
Goalpost View of Quality.
Continuous Improvement View of Quality.
Definitions of Six Sigma Management (Relationship Between VoC and VoP).
Nontechnical Definitions of Six Sigma.
Technical Definition of Six Sigma Management.
What Is New about Six Sigma Management?
2. Six Sigma Roles, Responsibilities, and Terminology.
Roles and Responsibilities in Six Sigma Management.
Executive Committee Member.
Master Black Belt.
Technical Terminology of Six Sigma Management.
Beginning Six Sigma Management.
Starting Six Sigma Management.
Responding to a Crisis.
Initiating Action for Six Sigma Management.
Retaining Outside Counsel.
Window of Opportunity Opens.
Develop a Six Sigma Transformation Plan.
Window of Opportunity Begins to Close.
II. SIX SIGMA MODEL.
3. Macro Model of Six Sigma Management (Dashboards).
Structure of a Dashboard.
Components of a Dashboard.
Tasks and Projects.
Example of a Dashboard.
Managing with a Dashboard.
Coordinating Projects in a Department or Area.
Prioritization of Six Sigma Projects.
Management Decides Whether a Project Team Is Necessary.
4. Define Phase of the DMAIC Model.
Activating a Six Sigma Project Team.
Structure of the Define Phase.
Background for the Business Case.
A Schedule with Milestones.
Benefits and Costs.
Roles and Responsibilities.
Prepare a Draft Project Objective.
Conducting an SIPOC Analysis.
Voice of the Customer Analysis.
Background on Market Segmentation.
Proactive Voice of the Customer Data.
Finalize Project Objective.
Champion and Process Owner Tollgate Reviews.
Tollgate Style Questions.
Presidential Tollgate Reviews.
Reasons for Conducting Presidential Reviews.
Benefits of Presidential Reviews.
Barriers to the Presidential Review.
Selecting the Departments and Topics to Review.
Informing the Departments to Be Reviewed.
Ground Rules for the Presidential Review.
Preparing for the Review.
Conducting the Review.
Keys to Successful Reviews.
Define Phase Tollgate Checklist.
5. Measure Phase of the DMAIC Model.
Constructing Operational Definitions for CTQs.
Effect of No Operational Definition.
Examples of an Operational Definition.
Operational Definitions of the CTQs in the MSD Case Study.
Establishing the Validity of the Measurement System for Each CTQ.
Questions to Ask About a Measurement System.
Measurement System Studies.
Establishing the Baseline Capabilities for CTQs.
Collect and Analyze VoP Data for Each CTQ.
Estimate Process Capability for Each CTQ.
Baseline for Durability and Functionality from the MSD
Measure Phase Tollgate Review Checklist.
Appendix 5.1 Using Minitab for Gage R&R Studies.
Obtaining a Gage Run?Chart.
Obtaining a Gage R&R Study (Crossed).
6. Analyze Phase of the DMAIC Model.
Identify the Xs for the Process Under Study.
Identify the Xs Related to Each CTQ.
Identify the High-Risk Xs for Each CTQ.
Develop Operational Definitions for High-Risk Xs.
Establish Measurement System for High-Risk Xs.
Establish Baseline Process Capabilities for Xs.
Stabilize High-Risk Xs.
Consider Major Nuisance Variables.
Using Screening Designs to Reduce the Number of High-Risk Xs.
Develop Hypotheses About the Relationships Between the High-Risk Xs and the CTQs.
Analyze Phase Tollgate Review Checklist.
The Analyze Phase for Processes with a Well-Established Dashboards.
Appendix 6 Using Minitab to Obtain a Multivari Chart.
Obtaining a Multivari Chart.
7. Improve Phase of the DMAIC Model.
Purpose of Designed Experiments.
Level of Process Knowledge.
Some Flawed Experimental Designs.
Two-Factor Factorial Designs.
Example of a Designed Experiment.
Avoid Potential Problems in the Xs.
Conduct a Pilot Study.
Example of a Pilot Study.
Identify Actions Needed to Implement Optimized Process.
Improve Phase Tollgate Review Checklist.
8. Control Phase of the DMAIC Model.
Reduce the Effects of Collateral Damage to Related Processes.
Standardize Process Improvements in the Xs.
ISO 9000 and ISO 14000.
Generic Table of Contents of an ISO Standard.
Maintain Control of the Xs.
Develop a Control Plan for the Process Owner.
Identify and Document the Benefits and Costs of the Project.
Input Project into the Six Sigma Database.
Diffuse the Improvements Throughout the Organization.
Control Phase Tollgate Review.
III. SIX SIGMA TOOLS AND METHODS.
9. Basics of Statistical Studies.
Introduction to Statistics.
Enumerative and Analytic Studies.
Distinguishing Enumerative and Analytic Studies.
Types of Sampling.
Simple Random Sample.
Types of Variables.
Introduction to Graphics.
Graphing Attribute Data.
The Bar Chart.
The Pareto Diagram.
Graphing Measurement Data.
The Dot Plot.
The Run Chart.
Measures of Central Tendency.
The Arithmetic Mean.
Measures of Variation.
The Variance and the Standard Deviation.
The Shape of Distributions.
The Five-Number Summary.
The Box-and-Whisker Plot.
Appendix 9.1 Using Windows.
Making Mistakes and Correcting Entries.
Appendix 9.2 Introduction to Minitab.
Using Minitab Worksheets.
Opening and Saving Worksheets and Other Components.
Printing Worksheets, Graphs, and Sessions.
Appendix 9.3 Using Minitab for Charts and Descriptive Statistics.
Obtaining a Bar Chart.
Obtaining a Pareto Diagram.
Obtaining a Run Chart.
Obtaining a Histogram.
Using Minitab to Obtain a Dot Plot.
Obtaining Descriptive Statistics.
Using Minitab to Obtain a Box-and-Whisker Plot.
Using Minitab to Select a Random Sample.
10. Probability and Probability Distributions.
Introduction to Probability.
Some Rules of Probability.
The Average or Expected Value of a Random Variable.
Standard Deviation of a Random Variable (s).
Characteristics of the Binomial Distribution.
Characteristics of the Poisson Distribution.
Normal Probability Plot.
Appendix 10.1 Using Minitab for Probability Distributing.
Using Minitab to Obtain Binomial Probabilities.
Using Minitab to Obtain Poisson Probabilities.
Using Minitab to Obtain Normal Probabilities.
Using Minitab to Obtain a Normal Probability Plot.
11. Sampling Distributions and Interval Estimation.
Sampling Distribution of the Mean.
Sampling Distribution of the Proportion.
Basic Concepts of Confidence Intervals.
Confidence Interval Estimate for the Mean (s unknown).
Prediction Interval Estimate for a Future Individual Value.
Confidence Interval Estimation for the Proportion.
Appendix 11.1 Using Minitab to Obtain Confidence Intervals.
Obtaining the Confidence Interval Estimate for the Mean.
Obtaining the Confidence Interval Estimate for the Proportion.
12. Hypothesis Testing.
Fundamental Concepts of Hypothesis Testing.
The Critical Value of the Test Statistic.
Regions of Rejection and Nonrejection.
Risks in Decision Making Using Hypothesis-Testing Methodology.
Level of Significance.
The Confidence Coefficient.
The b Risk.
The Power of a Test.
The p-Value Approach to Hypothesis Testing.
Testing for the Difference Between Two Proportions.
Testing for the Difference Between the Means of Two Independent Groups.
Pooled-Variance t Test for the Difference in Two Means.
Separate-Variance t Test for Differences in Two Means.
Testing for the Differences Between Two Variances.
The F Test for the Ratio of Two Variances.
The Levene Test for the Difference Between Variances.
One-Way ANOVA: Testing for Differences Among the Means of Three or More Groups.
F Test for Differences in Three or More Means.
Multiple Comparisons: The Tukey Procedure.
Levene’s Test for Homogeneity of Variance.
Appendix 12.1 Using Minitab for Hypothesis Testing.
Testing for the Difference Between Two Proportions.
Testing for the Difference Between the Means of Two Independent Samples.
Testing for the Difference Between Two Variances.
Obtaining a One-Way ANOVA with Multiple Comparisons.
Testing for Equal Variances in the Analysis of Variance.
13. Design of Experiments.
Design of Experiments: Background and Rationale.
Two-Factor Factorial Designs.
2k Factorial Designs.
Fractional Factorial Designs.
Choosing the Treatment Combinations.
Summary and Overview.
Appendix 13.1 Using Minitab for the Design of Experiments.
Using Minitab for the Two-Way ANOVA.
Using Minitab for a Main Effects Plot.
Using Minitab for an Interaction Plot.
Using Minitab for a Factorial Design.
Using Minitab for a Fractional Factorial Design.
14. Control Charts for Six Sigma Management.
Basic Concepts of Control Charts.
The Funnel Experiment.
Control Limits and Patterns.
Rules for Determining Out-Of-Control Points.
Control Charts for the Mean and Range.
Control Charts for the Mean and the Standard Deviation.
Individual Value and Moving Range Charts.
Appendix 14.1 Using Minitab for Control Charts.
Using Minitab to Obtain Zone Limits.
Using Minitab for the p-Chart.
Using Minitab for the c-Chart.
Using Minitab for the u-Chart.
Using Minitab for the R and Charts.
Using Minitab for the S and Charts.
Using Minitab for the Individual Value and Moving Range Charts.
15. Additional Tools and Methods.
Cause-and-Effect Diagram and Matrix.
Constructing a Cause-and-Effect Diagram Using an Affinity Diagram.
Attribute Check Sheet.
Measurement Check Sheet.
Defect Location Check Sheet.
Stratification and Pareto Diagrams.
Stratification and Cause-and-Effect (C&E) Diagrams.
Stratification with Pareto Diagrams and Cause-and-Effect Diagrams.
Stratification with Control Charts, Pareto Diagrams, and Cause-and-Effect Diagrams.
Other Combinations of Tools for Stratification.
Appendix 15.1 Using Minitab for the Cause-and-Effect Diagram.
IV. SIX SIGMA CASE STUDIES.
16. Paper Organizers International: A Fictitious Six Sigma Green Belt Case Study.
Background of the Case Study.
Origin of the MSD Six Sigma Project.
Prepare a Business Case with a Project Objective.
Do A SIPOC Analysis.
Conduct a “Voice of the Customer” Analysis.
Operationally Define Each CTQ.
Perform a Gage R&R Study on Each CTQ.
Develop a Baseline for Each CTQ.
17. A Paper Helicopter Case Study.
Operationally Defining Each CTQ.
Conducting a Gage R&R Study of the Flight Time.
Interpretation of the Minitab Analysis of the Gage R&R.
Develop a Baseline for Each CTQ.
Operationally Defining Each X.
Gage R&R Studies of Each X.
Developing Baselines for Each X.
Relationships among the Xs and the CTQ.
Design of Experiments.
Interpretation of the Experiments.
Returning Process Control to the Process Owner.
V. SIX SIGMA CERTIFICATION.
18. Six Sigma Champion Certification at the University of Miami.
Certification at the University of Miami.
Champion Certification Examination.
Cost for Champion Certification Examination.
Sample Champion Certification Examination.
Questions with Answers.
19. Six Sigma Green Belt Certification at the University of Miami.
Green Belt Basics.
Green Belt Certification Examinations and Project Reviews.
Costs for Certification Examinations and Dossier Reviews.
Sample Green Belt Certification Examination Questions with Answers.
Case Study for Green Belt Certification.
Sun Super Catapult Supplies.
Practical Aspects of the Case Study.
Completing Your Case Study.
Appendix A. Review of Arithmetic and Algebra.
Part 1 Fill in the Correct Answer.
Part 2 Select the Correct Answer.
Exponents and Square Roots.
Answers to Quiz.
Appendix B. Summation Notation.
Appendix C. Statistical Tables.
Appendix D. Documentation of Data Files.
Glossary of Terms.
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David M. Levine is Professor Emeritus of Statistics and Computer Information Systems at Bernard M. Baruch College (City University of New York). He received B.B.A. and M.B.A. degrees in Statistics from City College of New York and a Ph.D. degree from New York University in Industrial Engineering and Operations Research. He is nationally recognized as a leading innovator in business statistics education and is the co-author of such best-selling statistics textbooks as Statistics for Managers using Microsoft Excel, Basic Business Statistics: Concepts and Applications, Business Statistics: A First Course, and Applied Statistics for Engineers and Scientists using Microsoft Excel and Minitab, He has published articles in various journals including Psychometrika, The American Statistician, Communications in Statistics, Multivariate Behavioral Research, Journal of Systems Management, Quality Progress, and The American Anthropologist.
Dr. Howard S. Gitlow is Executive Director of the University of Miami Institute for the Study of Quality in Manufacturing and Service and a Professor of Management Science, University of Miami, Coral Gables, Florida. He was a Visiting Professor at the Science University of Tokyo in 1990 where he studied Quality Management with Dr. Noriaki Kano. He received his Ph.D. in Statistics (1974), M.B.A. (1972), and B.S. in Statistics (1969) from New York University. His areas of specialization are Six Sigma Management, Dr. Deming's theory of management, Japanese Total Quality Control, and statistical quality control.
Dr. Gitlow is a Six Sigma Master Black Belt, a senior member of the American Society for Quality Control and a member of the American Statistical Association. He has consulted on quality, productivity, and related matters with many organizations, including several Fortune 500 companies.
Dr. Gitlow has co-authored several books. These include: Quality Management: Tools and Methods for Improvement, Richard D. Irwin Publishers (2005), third edition; Quality Management Systems, CRC Press (2000), Total Quality Management in Action, Prentice-Hall, (1994); The Deming Guide to Quality and Competitive Position, Prentice-Hall, (1987), fifteenth printing; Planning for Quality, Productivity, and Competitive Position, Dow Jones-Irwin Publishers (1990); and Stat City: Understanding Statistics Through Realistic Applications, Richard D. Irwin Publishers (1987), second edition. He has published over 45 academic articles in the areas of quality, statistics, management, and marketing.
While at the University of Miami, Dr. Gitlow has received awards for Outstanding Teaching, Outstanding Writing, and Outstanding Published Research Articles.
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