Practitioners Guide to Statistics and Lean Six Sigma for Process Improvements

This hands–on book presents a complete understanding of Six Sigma and Lean Six Sigma through data analysis and statistical concepts
In today′s business world, Six Sigma, or Lean Six Sigma, is a crucial tool utilized by companies to improve customer satisfaction, increase profitability, and enhance productivity. Practitioner′s Guide to Statistics and Lean Six Sigma for Process Improvements provides a balanced approach to quantitative and qualitative statistics using Six Sigma and Lean Six Sigma methodologies.
Emphasizing applications and the implementation of data analyses as they relate to this strategy for business management, this book introduces readers to the concepts and techniques for solving problems and improving managerial processes using Six Sigma and Lean Six Sigma. Written by knowledgeable professionals working in the field today, the book offers thorough coverage of the statistical topics related to effective Six Sigma and Lean Six Sigma practices, including:
Discrete random variables and continuous random variables
Sampling distributions
Estimation and hypothesis tests
Chi–square tests
Analysis of variance
Linear and multiple regression
Measurement analysis
Survey methods and sampling techniques
The authors provide numerous opportunities for readers to test their understanding of the presented material, as the real data sets, which are incorporated into the treatment of each topic, can be easily worked with using Microsoft Office Excel®, Minitab®, MindPro®, or Oracle′s Crystal Ball® software packages. Examples of successful, complete Six Sigma and Lean Six Sigma projects are supplied in many chapters along with extensive exercises that range in level of complexity. The book is accompanied by an extensive FTP site that features manuals for working with the discussed software packages alo ng with additional exercises and data sets. In addition, numerous screenshots and figures guide readers through the functional and visual methods of learning Six Sigma and Lean Six Sigma.
Practitioner′s Guide to Statistics and Lean Six Sigma for Process Improvements is an excellent book for courses on Six Sigma and statistical quality control at the upper–undergraduate and graduate levels. It is also a valuable reference for professionals in the fields of engineering, business, physics, management, and finance.

Spis treści:
Chapter 1 Principles of Six Sigma.
1.1 Overview.
1.2 Six Sigma Essentials.
1.3 Quality Definition.
1.4 Value Creation.
1.5 Business, Operations, Process and Individual BOPI Goals.
1.6 Underpinning Economics.
1.7 Performance Metrics.
1.8 Process.
1.9 Design Complexity.
1.10 Nature and Purpose of Six Sigma.
1.11 Needs That Underlie Six Sigma.
1.12 Why Focusing on The Customer is Essential To Six Sigma.
1.13 Success Factors.
1.14 Software applications.
References.
Glossary.
Chapter 2 Six Sigma Installation.
2.1 Overview.
2.2 Six Sigma Leadership – The Fuel of Six Sigma.
2.3 Deployment Planning.
2.4 Applications Projects.
2.5 Deployment Timeline.
2.6 Design for Six Sigma [DFSS] Principles.
2.7 Processing for Six Sigma [PFSS] Principles.
2.8 Managing for Six Sigma [MPSS] Principles.
2.9 Project Review.
2.10 Summary.
References.
Glossary.
Chapter 3 Lean Six Sigma Projects.
3.1 Overview.
3.2 Introduction.
3.3 Project Description.
3.4 Project Guidelines (General).
3.5 Project Selection.
3.6 Project Scope.
3.7 Project Leadership.
3.8 Project Teams.
3.9 Project Financials.
3.10 Project Management.
3.11 Project Payback.
3.12 Project Milestones.
3.13 Project Roadmap.
3.14 Project Charters (General).
3.15 Six Sigma Projects.
3.16 Project Summary.
References .
Glossary.
Chapter 4 Lean Practices.
4.1 Overview.
4.2 Introduction.
4.3 The Idea of Lean Thinking.
4.4 Theory of Constraints [TOC].
4.5 Lean Concept.
4.6 Definition of Value Added Activities and Non Value Added Activities.
4.7 Why Companies Think Lean.
4.8 Visual Controls – Visual Factory.
4.9 The Idea of Pull – Kanban.
4.10 5S – 6S System.
4.11 The Idea of Perfection – Kaizen.
4.12 Replicate – Translate.
4.13 PokaYoke System – Mistake Proofing.
4.14 SMED System.
4.15 7W + 1 Approach – Seven + one Deadly Waste(s).
4.16 6M Approach .
4.17 Summary.
References .
Glossary.
Chapter 5 Value Stream Mapping.
5.1 Overview.
5.2 Introduction.
5.3 Value Stream Mapping.
5.4 Focused Brainstorming.
5.5 Graphical representation of a Process in a Value Stream Map.
5.6 Effective Working Time.
5.7 Customer Demand.
5.8 Takt Time.
5.9 Pitch Time .
5.10 Queuing Time.
5.11 Cycle Time.
5.12 Total Cycle Time.
5.13 Calculation of Total Lead Time(s).
5.14 Value Added Percentage and Six Sigma Level.
5.15 Drawing the Current Value Stream Map.
5.16 Drawing the Value Stream Map?.
5.17 What Makes a Value Stream Lean?.
5.18 The Future Value Stream Map.
5.19 Summary.
References.
Glossary.
Chapter 6 Introductory Statistics and Data.
6.1 Overview.
6.2 Introduction.
6.3 Genetic Code of Statistics.
6.4 Population and Samples .
6.5 The Idea of Data.
6.6 Nature of Data.
6.7 Data Collection.
6.8 The Importance of Data Collection.
6.9 Sampling in Six sigma.
6.10 Sources of Data.
6.11 Database.
6.12 Chapter Summary.
References.
Glossary.
Chapter 7 Quality Tools.
7.1 Overview.
7.2 Introduction.
7.3 Nature of Six Sigma Variables.
7.4 Quality Function Deployment (QFD).
7.5 Scales of Measurement.
7.6 Diagnostic Tools.
7.7 Analytical Methods.
7 .8 Graphical Tools.
7.9 Graphical Representation of a Process.
7.10 SIPOC Diagram.
7.11 IPO Diagram – General Model of a Process System.
7.12 Force Field Analysis.
7.13 Matrix Analysis – The Importance of Statistical Thinking.
7.14 Check Sheets.
7.15 Score Cards.
7.16 Affinity Diagram.
7.17 Concept Integration.
Reference.
Glossary.
Chapter 8 Making Sense of Data in Six Sigma and Lean.
8.1 Overview.
8.2 Summarizing Quantitative Data: Graphical Methods.
8.3 Summarizing Quantitative Data: Numerical Methods.
8.4 Organizing and Graphing Qualitative Data.
8.5 Summarizing Bivariate Data.
Glossary.
Chapter 9 Fundamentals of Capability and Rolled Throughput Yield.
9.1 Overview.
9.2 Introduction.
9.3 Why Capability.
9.4 Six Sigma Capability Metric.
9.5 Discrete Capability.
9.6 Continuous Capability – Example.
9.7 Fundamentals of Capability.
9.8 Short versus Long Term Capability.
9.9 Capability and Performance.
9.10 Indices of Capability.
9.11 Shift – Calibrating the Shift.
9.12 Applying the 1.5Concept.
9.13 Yield.
9.14 Hidden Factory.
Glossary.
References.
Chapter 10 Probability.
10.1 Overview.
10.2 Experiments, Outcomes, and Sample Space.
10.3 Calculating Probability.
10.4 Combinatorial Probability.
10.5 Marginal and Conditional Probabilities.
10.6 Union of Events.
10.7 Intersection of Events.
Glossary.
Chapter 11 Discrete Random Variables and Their Probability Distributions.
11.1 Overview.
11.2 Six Sigma Performance Variables.
11.3 Six Sigma Leverage Variables.
11.4 Random Variable.
11.5 Probability Distributions of a Discrete Random Variable.
11.6 Mean of a Random Variable.
11.7 Standard Deviation of a Discrete Random Variable.
11.8 The Binomial Distribution.
11.9 The Poisson Probability Distribution.
11.10 The Geometrical Distribution.
11.11 The Hypergeom etric Probability Distribution.
Glossary.
Chapter 12 Continuous Random Variables and Their Distributions.
12.1 Overview.
12.2 Continuous Probability Distributions.
12.3 The Normal Distribution.
12.4 The Exponential Distribution.
Glossary.
Chapter 13 Sampling Distributions.
13.1 Overview.
13.2 Sampling Distribution of a Sample Mean.
13.3 Sampling Distribution of a Sample Proportion.
13.4 The Central Limit Theorem.
Glossary.
Chapter 14 Single Population Estimation.
14.1 Overview.
14.2 What Does a Confidence Interval Mean?.
14.3 Estimating a Population Mean.
14.4 Estimating a Population Proportion.
14.5 Estimating a Population Variance.
Glossary.
Chapter 15 Control Methods.
15.1 Overview.
15.2 Introduction.
15.3 Control Logic.
15.4 Statistical Control Systems.
15.5 Statistical Control.
15.6 Prevention VS Detection.
15.7 What is a Process Control System?.
15.8 Variation.
15.9 Process Out–of–Control.
15.10 Fundamentals of Process Control.
15.11 Continuous Statistical Process Control (SPC) Tools.
15.12 Interpreting Process Control.
15.13 Statistical Process Control and Statistical Process Monitoring.
15.14 The Foundation of Statistical Process Control (SPC).
15.15 Tools for Process Controls – Control Charts.
15.16 Control Limits.
15.17 Process Out–of–Control.
15.18 Western Electric Rules.
15.19 Why Control Charts and How are These Used.
15.20 Pre–Control Method.
15.21 Control Charts for Variables.
15.22 Control Chart for Attributes.
Glossary.
References.
Chapter 16 Single Population Hypothesis Tests.
16.1 Overview.
16.2 Introduction to Hypothesis Testing.
16.3 Testing a Claim About a Population Mean.
16.4 Hypothesis Test About a Population Proportion.
Glossary.
Chapter 17 Estimation and Hypothesis Tests: Two Populations.
17.1 Overview.
17.2 Inferences About the Differences Between Two Population Means for.
Independent Samples.
17.3 Inferences About the Differences Between Two Population Means for Paired Samples.
17.4 Inferences About the Differences Between Two Population Proportions.
Glossary.
Chapter 18 Chi–Square Tests.
18.1 Overview.
18.2 A Goodness–of–Fit Test.
18.3 Contingency Tables.
18.4 Tests of Independence and Homogeneity.
Glossary.
Chapter 19 Analysis of Variance.
19.1 Overview.
19.2 The F– Distribution.
19.3 One–Way Analysis of Variance.
19.4 One–Way Analysis of Variance.
19.5 Pairwise Comparisons.
19.6 Multi–Factor Analysis of Variance.
19.7 What Do We Do When the Assumptions Are Unreasonable?.
Glossary.
Chapter 20 Linear and Multiple Regression.
20.1 Overview.
20.2 Simple Regression Model.
20.3 Linear Regression.
20.4 Coefficient of Determination and Correlation.
20.5 Multiple Regression.
20.6 Regression Analysis.
20.7 Using the Regression Model.
20.8 Residual Analysis.
20.9 Cautions in Using Regression.
Glossary.
Chapter 21 Measurement Analysis.
21.1 Overview.
21.2 Introduction.
21.3 Measurement.
21.4 Measurement Error.
21.5 Accuracy and Precision.
21.6 Measurement System as a Process.
21.7 Categories of Measurement Error Which Affect the Location.
21.8 Categories of Measurement Which Affect the Spread.
21.9 Gage–Accuracy and Precision.
21.10 Exploring Linearity Error.
21.11 Gage R & R [Repeatability & Reproducibility].
21.12 Gage R&R – Variable.
21.13 Gage R&R – Crossed.
21.14 Attribute Gage R & R Study.
21.15 ANOVA Method versus R Method.
21.16 ANOVA/Variance Component Analysis.
21.17 Rules of Thumb.
21.18 Acceptability Criteria.
21.19 Chapter Review.
References.
Glossary.
Chapter 22 Fundament als of Design of Experiments.
22.1 Overview.
22.2 Introduction.
22.3 What is Design of Experiments (DOE)?.
22.4 Role of Experimental Design in Process Improvement.
22.5 Experiment Design Tools.
22.6 Principles of an Experimental Design.
22.7 Different Types of Experiments.
22.8 Introduction to Factorial Designs.
22.9 Features of Factorial Designs – Orthogonality.
22.10 Full Factorial Designs.
22.11 Residual Analysis 22.
22.12 Modeling 22.
22.13 Multi–Factor Experiment.
22.14 Fractional Factorial Designs.
22.15 The ANOVA Table.
22.16 Normal Probability Plot of the Effects.
22.17 Main Effects Plot.
22.18 Blocking Variable.
22.19 Statistically Significant.
22.20 Practically Significant.
22.21 Fundamentals of Residual Analysis.
22.22 Center Points.
22.23 Noise Factors.
22.24 Strategy of Good Experimentation.
22.25 Selecting the Variable Levels.
22.26 Selecting the Experimental Design.
22.27 Replication.
22.28 Analyzing the data [ANOVA].
22.29 Recommendations.
22.30 Achieving the Objective.
22.31 Chapter Summary.
22.32 Chapter Example.
References.
Glossary.
Chapter 23 Design for Six Sigma [DFSS], Simulation, and Optimization.
23.1 Overview.
23.2 Introduction.
23.3 Six Sigma as Stretch Target.
23.4 Producibility.
23.5 Statistical Tolerances.
23.6 Design Application.
23.7 Design Margin.
23.8 Design Qualification.
23.9 Design for Six Sigma (DFSS) Principles.
23.10 Decision Power.
23.11 Experimentation.
23.12 Experiment Design.
23.13 Response Surface Designs.
23.14 Factorial Producibility.
23.15 Toolbox Overview.
23.16 Monte Carlo Simulations.
23.17 Design for Six Sigma Project Selection Example.
23.18 Defining Simulation Inputs.
23.19 Defining Outputs and Running a Simulation.
23.20 Stochastic Optimization: Discovering the Best Portfolio with the Least Risk.
23.21 Conclusions.
Refer ences.
Glossary.
Chapter 24 Survey Methods and Sampling Techniques.
24.1 Overview.
24.2 The Sample Survey.
24.3 The Survey System.
24.4 Clear Goals.
24.5 Target Population and Sample Size.
24.6 Interviewing Method.
24.7 Response Rate, Respondents and Non–respondents.
24.8 Survey Methods.
24.9 Sources of Information and Data.
24.10 The Order of the Questions.
24.11 Pilot Test the Questionnaire.
24.12 Biased Sample or Response Error.
24.13 Sampling – Random and Non–Random Sample.
24.14 Population Distribution.
24.15 Sampling Distribution.
24.16 Sampling and Non–sampling Errors.
References.
Glossary.
Appendix A: Statistical Tables.
Table I: Table of Binomial Probabilities.
Table II: Standard Normal Distribution Table.
Table III: The t Distribution Table.
Table IV: Chi–Square Distribution Table.
Table V: The F Distribution Table.
Table VI: Critical Values for the Mann–Whitney Test.
Table VII: Critical Values for the Wilcoxon Signed–Rank Test.
Table VIII: Sigma Conversion Table.
Appendix B: Answers to Selected Odd–Numbered Exercises.
Index.

Nota biograficzna:

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