Practitioner's Guide to Statistics and Lean Six Sigma for Process Improvements
, by Harry, Mikel J.; Mann, Prem S.; De Hodgins, Ofelia C.; Hulbert, Richard L.; Lacke, Christopher J.
- ISBN: 9780470114940 | 0470114940
- Cover: Hardcover
- Copyright: 1/19/2010
The only textbook of its kind, this authoritative account provides a balanced approach to quantitative and qualitative statistics using the Six Sigma and Lean Sigma methodologies. The emphasis is on applications and the implementation of data analyses as they relate to Six Sigma. The primary purpose of the book is to serve as a reference guide and textbook for both experienced practitioners and new students of Six Sigma. The book includes plentiful examples, exercises, and case studies based on real data and real-life analytical and business problems. It is written by a broad spectrum of academic and industry professionals.
Mikel J. Harry, PhD, is President and Chairman of the Board of the Six Sigma Management Institute. He is considered the principal architect of Six Sigma and one of the world's leading authorities in the field. Dr. Harry also focuses his research on applications of experimental design, inferential statistics, and statistical process control.
Prem S. Mann, PhD, is Professor and Chair of the Department of Economics at Eastern Connecticut State University. Dr. Mann has published numerous articles in the areas of labor economics, microeconomics, and statistics. He is the author of Introductory Statistics, Seventh Edition (Wiley).
Ofelia C. De Hodgins, MS, is a Six Sigma Global Master Black Belt. She has over twenty-five years of consulting experience in manufacturing and finance and has published more than thirty journal articles in the areas of physics, industrial engineering, statistics, and Statistical Process Control (SPC).
Richard L. Hulbert, MBA, is Vice President of Systems and Technology for the Bank of New York Mellon. He has more than thirty-five years of industry experience in the areas of network engineering, installation, implementation, network operations of technology infrastructure, distributed systems, market data, and government telecommunications.
Christopher J. Lacke, PhD, is Associate Professor of Mathematics at Rowan University. He has published numerous journal articles in his areas of research interest, which include decision analysis, Bayesian analysis, and operations research.
| Principles of Six Sigma | |
| Overview | |
| Six Sigma Essentials | |
| Quality Definition | |
| Value Creation | |
| Business, Operations, Process and Individual BOPI Goals | |
| Underpinning Economics | |
| Performance Metrics | |
| Process | |
| Design Complexity | |
| Nature and Purpose of Six Sigma | |
| Needs That Underlie Six Sigma | |
| Why Focusing on The Customer is Essential To Six Sigma | |
| Success Factors | |
| Software applications | |
| References | |
| Glossary | |
| Six Sigma Installation | |
| Overview | |
| Six Sigma Leadership - The Fuel of Six Sigma | |
| Deployment Planning | |
| Applications Projects | |
| Deployment Timeline | |
| Design for Six Sigma [DFSS] Principles | |
| Processing for Six Sigma [PFSS] Principles | |
| Managing for Six Sigma [MPSS] Principles | |
| Project Review | |
| Summary | |
| References | |
| Glossary | |
| Lean Six Sigma Projects | |
| Overview | |
| Introduction | |
| Project Description | |
| Project Guidelines (General) | |
| Project Selection | |
| Project Scope | |
| Project Leadership | |
| Project Teams | |
| Project Financials | |
| Project Management | |
| Project Payback | |
| Project Milestones | |
| Project Roadmap | |
| Project Charters (General) | |
| Six Sigma Projects | |
| Project Summary | |
| References | |
| Glossary | |
| Lean Practices | |
| Overview | |
| Introduction | |
| The Idea of Lean Thinking | |
| Theory of Constraints [TOC] | |
| Lean Concept | |
| Definition of Value Added Activities and Non Value Added Activities | |
| Why Companies Think Lean | |
| Visual Controls - Visual Factory | |
| The Idea of Pull - Kanban | |
| 5S - 6S System | |
| The Idea of Perfection - Kaizen | |
| Replicate - Translate | |
| PokaYoke System - Mistake Proofing | |
| SMED System | |
| 7W + 1 Approach - Seven + one Deadly Waste(s) | |
| 6M Approach | |
| Summary | |
| References | |
| Glossary | |
| Value Stream Mapping | |
| Overview | |
| Introduction | |
| Value Stream Mapping | |
| Focused Brainstorming | |
| Graphical representation of a Process in a Value Stream Map | |
| Effective Working Time | |
| Customer Demand | |
| Takt Time | |
| Pitch Time | |
| Queuing Time | |
| Cycle Time | |
| Total Cycle Time | |
| Calculation of Total Lead Time(s) | |
| Value Added Percentage and Six Sigma Level | |
| Drawing the Current Value Stream Map | |
| Drawing the Value Stream Map? | |
| What Makes a Value Stream Lean? | |
| The Future Value Stream Map | |
| Summary | |
| References | |
| Glossary | |
| Introductory Statistics and Data | |
| Overview | |
| Introduction | |
| Genetic Code of Statistics | |
| Population and Samples | |
| The Idea of Data | |
| Nature of Data | |
| Data Collection | |
| The Importance of Data Collection | |
| Sampling in Six sigma | |
| Sources of Data | |
| Database | |
| Chapter Summary | |
| References | |
| Glossary | |
| Quality Tools | |
| Overview | |
| Introduction | |
| Nature of Six Sigma Variables | |
| Quality Function Deployment (QFD) | |
| Scales of Measurement | |
| Diagnostic Tools | |
| Analytical Methods | |
| Graphical Tools | |
| Graphical Representation of a Process | |
| SIPOC Diagram | |
| IPO Diagram - General Model of a Process System | |
| Force Field Analysis | |
| Matrix Analysis - The Importance of Statistical Thinking | |
| Check Sheets | |
| Score Cards | |
| Affinity Diagram | |
| Concept Integration | |
| Reference | |
| Glossary | |
| Making Sense of Data in Six Sigma and Lean | |
| Overview | |
| Summarizing Quantitative Data: Graphical Methods | |
| Summarizing Quantitative Data: Numerical Methods | |
| Organizing and Graphing Qualitative Data | |
| Summarizing Bivariate Data | |
| Glossary | |
| Fundamentals of Capability and Rolled Throughput Yield | |
| Overview | |
| Introduction | |
| Why Capability | |
| Six Sigma Capability Metric | |
| Discrete Capability | |
| Continuous Capability - Example | |
| Fundamentals of Capability | |
| Short versus Long Term Capability | |
| Capability and Performance | |
| Indices of Capability | |
| Shift - Calibrating the Shift | |
| Applying the 1.5Concept | |
| Yield | |
| Hidden Factory | |
| Glossary | |
| References | |
| Probability | |
| Overview | |
| Experiments, Outcomes, and Sample Space | |
| Calculating Probability | |
| Combinatorial Probability | |
| Marginal and Conditional Probabilities | |
| Union of Events | |
| Intersection of Events | |
| Glossary | |
| Discrete Random Variables and Their Probability Distributions | |
| Overview | |
| Six Sigma Performance Variables | |
| Six Sigma Leverage Variables | |
| Random Variable | |
| Probability Distributions of a Discrete Random Variable | |
| Mean of a Random Variable | |
| Standard Deviation of a Discrete Random Variable | |
| The Binomial Distribution | |
| The Poisson Probability Distribution | |
| The Geometrical Distribution | |
| The Hypergeometric Probability Distribution | |
| Glossary | |
| Continuous Random Variables and Their Distributions | |
| Overview | |
| Continuous Probability Distributions | |
| The Normal Distribution | |
| The Exponential Distribution | |
| Glossary | |
| Sampling Distributions | |
| Overview | |
| Sampling Distribution of a Sample Mean | |
| Sampling Distribution of a Sample Proportion | |
| The Central Limit Theorem | |
| Glossary | |
| Single Population Estimation | |
| Overview | |
| What Does a Confidence Interval Mean? | |
| Estimating a Population Mean | |
| Estimating a Population Proportion | |
| Estimating a Population Variance | |
| Glossary | |
| Control Methods | |
| Overview | |
| Introduction | |
| Control Logic | |
| Statistical Control Systems | |
| Statistical Control | |
| Prevention VS Detection | |
| What is a Process Control System? | |
| Variation | |
| Process Out-of-Control | |
| Fundamentals of Process Control | |
| Continuous Statistical Process Control (SPC) Tools | |
| Interpreting Process Control | |
| Statistical Process Control and Statistical Process Monitoring | |
| The Foundation of Statistical Process Control (SPC) | |
| Tools for Process Controls - Control Charts | |
| Control Limits | |
| Process Out-of-Control | |
| Western Electric Rules | |
| Why Control Charts and How are These Used | |
| Pre-Control Method | |
| Control Charts for Variables | |
| Control Chart for Attributes | |
| Glossary | |
| References | |
| Single Population Hypothesis Tests | |
| Overview | |
| Introduction to Hypothesis Testing | |
| Testing a Claim About a Population Mean | |
| Hypothesis Test About a Population Proportion | |
| Glossary | |
| Estimation and Hypothesis Tests: Two Populations | |
| Overview | |
| Inferences About the Differences Between Two Population Means for Independent Samples | |
| Inferences About the Differences Between Two Population Means for Paired Samples | |
| Inferences About the Differences Between Two Population Proportions | |
| Glossary | |
| Chi-Square Tests | |
| Overview | |
| A Goodness-of-Fit Test | |
| Contingency Tables | |
| Tests of Independence and Homogeneity | |
| Glossary | |
| Analysis of Variance | |
| Overview | |
| The F- Distribution | |
| One-Way Analysis of Variance | |
| One-Way Analysis of Variance | |
| Pairwise Comparisons | |
| Multi-Factor Analysis of Variance | |
| What Do We Do When the Assumptions Are Unreasonable? | |
| Glossary | |
| Linear and Multiple Regression | |
| Overview | |
| Simple Regression Model | |
| Linear Regression | |
| Coefficient of Determination and Correlation | |
| Multiple Regression | |
| Regression Analysis | |
| Using the Regression Model | |
| Residual Analysis | |
| Cautions in Using Regression | |
| Glossary | |
| Measurement Analysis | |
| Overview | |
| Introduction | |
| Measurement | |
| Measurement Error | |
| Accuracy and Precision | |
| Measurement System as a Process | |
| Categories of Measurement Error Which Affect the Location | |
| Categories of Measurement Which Affect the Spread | |
| Gage-Accuracy and Precision | |
| Exploring Linearity Error | |
| Gage R & R [Repeatability & Reproducibility] | |
| Gage R&R - Variable | |
| Gage R&R - Crossed | |
| Attribute Gage R & R Study | |
| ANOVA Method versus R Method | |
| ANOVA/Variance Component Analysis | |
| Rules of Thumb | |
| Acceptability Criteria | |
| Chapter Review | |
| References | |
| Glossary | |
| Fundamentals of Design of Experiments | |
| Overview | |
| Introduction | |
| What is Design of Experiments (DOE)? | |
| Role of Experimental Design in Process Improvement | |
| Experiment Design Tools | |
| Principles of an Experimental Design | |
| Different Types of Experiments | |
| Introduction to Factorial Designs | |
| Features of Factorial Designs - Orthogonality | |
| Full Factorial Designs | |
| Residual Analysis 22 | |
| Modeling 22 | |
| Multi-Factor Experiment | |
| Fractional Factorial Designs | |
| The ANOVA Table | |
| Normal Probability Plot of the Effects | |
| Main Effects Plot | |
| Blocking Variable | |
| Statistically Significant | |
| Practically Significant | |
| Fundamentals of Residual Analysis | |
| Center Points | |
| Noise Factors | |
| Strategy of Good Experimentation | |
| Selecting the Variable Levels | |
| Selecting the Experimental Design | |
| Replication | |
| Analyzing the data [ANOVA] | |
| Recommendations | |
| Achieving the Objective | |
| Chapter Summary | |
| Chapter Example | |
| References | |
| Glossary | |
| Design for Six Sigma [DFSS], Simulation, and Optimization | |
| Overview | |
| Introduction | |
| Six Sigma as Stretch Target | |
| Producibility | |
| Statistical Tolerances | |
| Design Application | |
| Design Margin | |
| Design Qualification | |
| Design for Six Sigma (DFSS) Principles | |
| Decision Power | |
| Experimentation | |
| Experiment Design | |
| Response Surface Designs | |
| Factorial Producibility | |
| Toolbox Overview | |
| Monte Carlo Simulations | |
| Design for Six Sigma Project Selection Example | |
| Defining Simulation Inputs | |
| Defining Outputs and Running a Simulation | |
| Stochastic Optimization: Discovering the Best Portfolio with the Least Risk | |
| Conclusions | |
| References | |
| Glossary | |
| Survey Methods and Sampling Techniques | |
| Overview | |
| The Sample Survey | |
| The Survey System | |
| Clear Goals | |
| Target Population and Sample Size | |
| Interviewing Method | |
| Response Rate, Respondents and Non-respondents | |
| Survey Methods | |
| Sources of Information and Data | |
| The Order of the Questions | |
| Pilot Test the Questionnaire | |
| Biased Sample or Response Error | |
| Sampling - Random and Non-Random Sample | |
| Population Distribution | |
| Sampling Distribution | |
| Sampling and Non-sampling Errors | |
| References | |
| Glossary | |
| Statistical Tables | |
| Table of Binomial Probabilities | |
| Standard Normal Distribution Table | |
| The t Distribution Table | |
| Chi-Square Distribution Table | |
| The F Distribution Table | |
| Critical Values for the Mann-Whitney Test | |
| Critical Values for the Wilcoxon Signed-Rank Test | |
| Sigma Conversion Table | |
| Answers to Selected Odd-Numbered Exercises | |
| Index | |
| Table of Contents provided by Publisher. All Rights Reserved. |
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