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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(r), Minitab(r), MindPro(r), or Oracle's Crystal Ball(r) 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 along 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.
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(r), Minitab(r), MindPro(r), or Oracle's Crystal Ball(r) 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 along 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.
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.
1 Principles of Six Sigma.
1.1 Overview.
1.2 Six Sigma Essentials.
1.2.1 Driving Need.
1.2.2 Customer Focus.
1.2.3 Core Beliefs.
1.2.4 Deterministic Reasoning.
1.2.5 Leverage Principle.
1.3 Quality Definition.
1.4 Value Creation.
1.4.1 Value.
1.5 Business, Operations, Process and Individual (BOPI) Goals.
1.5.1 Differences between Product and Process Capability from a Six Sigma Perspective.
1.6 Underpinning Economics.
1.6.1 Sigma Benchmarking.
1.6.2 Breakthrough Goals.
1.6.3 Performance Benchmark.
1.7 Performance Metrics.
1.8 Process.
1.8.1 Process Models.
1.9 Design Complexity.
1.10 Nature and Purpose of Six Sigma.
1.10.1 Not Just Defect Reduction.
1.11 Needs That Underlie Six Sigma.
1.11.1 Looking Across the Organization.
1.11.2 Processing for Six Sigma.
1.11.3 Designing for Six Sigma.
1.11.4 Managing for Six Sigma.
1.11.5 Risk Orientation.
1.12 Why Focusing on The Customer is Essential to Six Sigma.
1.13 Success Factors.
1.14 Software Applications.
Explore Excel.
Explore MINITAB.
Explore JMP.
Glossary.
References.
2 Six Sigma Installation.
2.1 Overview.
2.2 Six Sigma Leadership-The Fuel of Six Sigma.
2.3 Deployment Planning.
2.3.1 Executive Management.
2.3.2 Six Sigma Champion.
2.3.3 Line Management.
2.3.4 Master Black Belts.
2.3.5 Black Belts.
2.3.6 Green Belts.
2.3.7 White Belts.
2.3.8 Six Sigma Roadmap.
2.3.9 Characteristics of Effective Metrics.
2.3.10 The Role of Metrics.
2.3.11 Six Sigma Performance Metrics.
2.3.12 Profit and Measurement
2.3.13 Twelve Criteria for Performance Metrics.
2.4 Application 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.
2.9.1 Tollgate Criteria.
2.9.2 Project Closure.
2.9.3 Project Documentation.
2.9.4 Personal Recognition.
2.9.5 Authenticating Agent.
2.10 Summary.
Glossary.
References and Notes.
3 Lean Sigma Projects.
3.1 Overview.
3.2 Introduction.
3.3 Project Description.
3.4 Project Guidelines.
3.5 Project Selection.
3.5.1 Project Selection Guidelines.
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.
Glossary.
References.
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 Value-Added Versus 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 Approach.
4.11 The Idea of Perfection (Kaizen).
4.12 Replication-Translate.
4.13 Poka-Yoke System-Mistakeproofing.
4.14 SMED System.
4.15 7W + 1 Approach-Seven Plus One Deadly Waste(s).
4.16 6M Approach.
4.17 Summary.
Glossary.
References.
5 Value Stream Mapping.
5.1 Overview.
5.2 Introduction.
5.3 Value Stream Mapping.
5.3.1 Waste Review.
5.3.2 Value-Added and Non-Value-Added Activities.
5.3.3 Elements of a Value Stream Map.
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.
5.15.1 Drawing Tips.
5.15.2 Common Failure Modes.
5.15.3 Common Definitions.
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.
Glossary.
References and Notes.
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.
6.6.1 Quantitative Variables and Data.
6.6.2 Qualitative/Categorical Variables and Data.
6.7 Data Collection.
6.8 The Importance of Data Collection.
6.8.1 Control Cards.
6.8.2 Data Collection Sheet.
6.9 Sampling in Six Sigma.
6.9.1 Random Sampling.
6.9.2 Sequential Sampling.
6.9.3 Stratified Sampling.
6.10 Sources of Data.
6.11 Database.
6.12 Summary.
Glossary.
References.
7 Quality Tools.
7.1 Overview.
7.2 Introduction.
7.3 Nature of Six Sigma Variables.
7.3.1 CT Concept.
7.3.2 CTQ and CTP Characteristics.
7.3.3 CTX Tree (Process Tree).
7.3.4 CTY Tree (Process Tree).
7.3.5 The Focus of Six Sigma.
7.3.6 The Leverage Principle.
7.4 Quality Function Deployment (QFD).
7.5 Scales of Measurement.
7.5.1 Likert Scale.
7.5.2 Logarithm Scale.
7.6 Diagnostic Tools.
7.6.1 Elements for Problem Solving-Diagnostic Tools and Methods.
7.6.2 Problem Definition-Defining Project Objective.
7.7 Analytical Methods.
7.7.1 Cause-Effect (CE) Analysis.
7.7.2 Failure Mode-Effects Analysis (FMEA)
7.7.3 XY Matrix.
7.8 Graphical Tools.
7.8.1 Graphical Summary.
7.8.2 Boxplot or Box-and-Whisker Plot.
7.8.3 Normal Probability Plot.
7.8.4 Main-Effects Plot.
7.8.5 Pareto Chart.
7.8.6 Run Chart.
7.8.7 Time-Series Plot.
7.8.8 Multi-Vari Charts.
7.8.9 Scatterplot.
7.9 Graphical Representation of a Process.
7.9.1 Process Flowcharts.
7.9.2 Process Mapping.
7.9.3 Cross-Functional Mapping.
7.9.4 Process Mapping-Deployment Diagram.
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 Checksheets.
7.15 Scorecards.
7.16 Affinity Diagram.
7.17 Concept Integration.
Glossary.
Reference.
8 Making Sense of Data in Six Sigma and Lean.
8.1 Overview.
8.2 Summarizing Quantitative Data: Graphical Methods.
8.2.1 Analytical Charts.
8.2.2 Dotplots.
8.2.3 Stem-and-Leaf Plots.
8.2.4 Frequency Tables.
8.2.5 Histograms and Performance Histograms.
8.2.6 Run Charts.
8.2.7 Time-Series Plots.
8.3 Summarizing Quantitative Data: Numerical Methods.
8.3.1 Measures of Center.
8.3.2 Measures of Variation.
8.3.3 Identifying Potential Outliers.
8.3.4 Measures of Position and the Idea of z Scores in Six Sigma.
8.3.5 Measure of Spread and Lean Sigma.
8.4 Organizing and Graphing Qualitative Data.
8.4.1 Organizing Qualitative Data.
8.4.2 Graphing Qualitative Data.
8.4.3 Pareto Analysis with Lorenz Curve.
8.5 Summarizing Bivariate Data.
8.5.1 Scatterplot.
8.5.2 Correlation Coefficient.
8.6 Multi-Vari Charts.
Glossary.
Exercises.
9 Fundamentals of Capability and Rolled Throughput Yield.
9.1 Overview.
9.2 Introduction.
9.3 Why Capability.
9.3.1 Performance Specifications.
9.3.2 Fundamental Concepts of Defect-Based Measurement.
9.4 Six Sigma Capability Metric.
9.4.1 Criteria for Performance Metrics.
9.4.2 Computing the Sigma Level from Discrete Data.
9.4.3 Defective Proportions.
9.4.4 Six-Sigma-Level Calculations (DPU, DPO, DPMO, PPM)-Examples.
9.5 Discrete Capability.
9.6 Continuous Capability-Example.
9.6.1 Data Collection for Capability Studies.
9.7 Fundamentals of Capability.
9.8 Short- Versus Long-Term Capability.
9.8.1 Short-Term Capability.
9.8.2 Long-Term Capability
9.8.3 Introduction to Calibrating the Shift.
9.9 Capability and Performance.
9.10 Indices of Capability.
9.10.1 Cp Index.
9.10.2 Cpk Index.
9.10.3 Pp Index.
9.10.4 Ppk Index.
9.11 Calibrating the Shift.
9.12 Applying the 1.5¿ Shift Concept.
9.13 Yield.
9.13.1 Final Test Yield (FTY).
9.13.2 Yield Related to Defects.
9.13.3 Rolled Throughput Yield (RTY).
9.13.4 In-Process Yield (IPY).
9.13.5 In-Process Yield (IPY) and Rolled Throughput Yield (RTY).
9.14 Hidden Factory.
9.14.1 Hidden Factory Composition.
Glossary.
References.
10 Probability.
10.1 Overview.
10.2 Experiments, Outcomes, and Sample Space.
10.3 Calculating Probability.
10.3.1 Equally Likely Events.
10.3.2 Probability as Relative Frequency.
10.3.3 Subjective Probability.
10.4 Combinatorial Probability.
10.5 Marginal and Conditional Probabilities.
10.6 Union of Events.
10.6.1 Addition Role.
10.6.2 Mutually Exclusive Events.
10.6.3 Complementary...
| Erscheinungsjahr: | 2010 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: | 832 S. |
| ISBN-13: | 9780470114940 |
| ISBN-10: | 0470114940 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Harry, Mikel J
Mann, Prem S de Hodgins, Ofelia C Hulbert, Richard L Lacke, Christopher J |
| Hersteller: |
Wiley
John Wiley & Sons |
| Maße: | 260 x 183 x 48 mm |
| Von/Mit: | Mikel J Harry (u. a.) |
| Erscheinungsdatum: | 19.01.2010 |
| Gewicht: | 1,724 kg |
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.
1 Principles of Six Sigma.
1.1 Overview.
1.2 Six Sigma Essentials.
1.2.1 Driving Need.
1.2.2 Customer Focus.
1.2.3 Core Beliefs.
1.2.4 Deterministic Reasoning.
1.2.5 Leverage Principle.
1.3 Quality Definition.
1.4 Value Creation.
1.4.1 Value.
1.5 Business, Operations, Process and Individual (BOPI) Goals.
1.5.1 Differences between Product and Process Capability from a Six Sigma Perspective.
1.6 Underpinning Economics.
1.6.1 Sigma Benchmarking.
1.6.2 Breakthrough Goals.
1.6.3 Performance Benchmark.
1.7 Performance Metrics.
1.8 Process.
1.8.1 Process Models.
1.9 Design Complexity.
1.10 Nature and Purpose of Six Sigma.
1.10.1 Not Just Defect Reduction.
1.11 Needs That Underlie Six Sigma.
1.11.1 Looking Across the Organization.
1.11.2 Processing for Six Sigma.
1.11.3 Designing for Six Sigma.
1.11.4 Managing for Six Sigma.
1.11.5 Risk Orientation.
1.12 Why Focusing on The Customer is Essential to Six Sigma.
1.13 Success Factors.
1.14 Software Applications.
Explore Excel.
Explore MINITAB.
Explore JMP.
Glossary.
References.
2 Six Sigma Installation.
2.1 Overview.
2.2 Six Sigma Leadership-The Fuel of Six Sigma.
2.3 Deployment Planning.
2.3.1 Executive Management.
2.3.2 Six Sigma Champion.
2.3.3 Line Management.
2.3.4 Master Black Belts.
2.3.5 Black Belts.
2.3.6 Green Belts.
2.3.7 White Belts.
2.3.8 Six Sigma Roadmap.
2.3.9 Characteristics of Effective Metrics.
2.3.10 The Role of Metrics.
2.3.11 Six Sigma Performance Metrics.
2.3.12 Profit and Measurement
2.3.13 Twelve Criteria for Performance Metrics.
2.4 Application 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.
2.9.1 Tollgate Criteria.
2.9.2 Project Closure.
2.9.3 Project Documentation.
2.9.4 Personal Recognition.
2.9.5 Authenticating Agent.
2.10 Summary.
Glossary.
References and Notes.
3 Lean Sigma Projects.
3.1 Overview.
3.2 Introduction.
3.3 Project Description.
3.4 Project Guidelines.
3.5 Project Selection.
3.5.1 Project Selection Guidelines.
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.
Glossary.
References.
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 Value-Added Versus 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 Approach.
4.11 The Idea of Perfection (Kaizen).
4.12 Replication-Translate.
4.13 Poka-Yoke System-Mistakeproofing.
4.14 SMED System.
4.15 7W + 1 Approach-Seven Plus One Deadly Waste(s).
4.16 6M Approach.
4.17 Summary.
Glossary.
References.
5 Value Stream Mapping.
5.1 Overview.
5.2 Introduction.
5.3 Value Stream Mapping.
5.3.1 Waste Review.
5.3.2 Value-Added and Non-Value-Added Activities.
5.3.3 Elements of a Value Stream Map.
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.
5.15.1 Drawing Tips.
5.15.2 Common Failure Modes.
5.15.3 Common Definitions.
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.
Glossary.
References and Notes.
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.
6.6.1 Quantitative Variables and Data.
6.6.2 Qualitative/Categorical Variables and Data.
6.7 Data Collection.
6.8 The Importance of Data Collection.
6.8.1 Control Cards.
6.8.2 Data Collection Sheet.
6.9 Sampling in Six Sigma.
6.9.1 Random Sampling.
6.9.2 Sequential Sampling.
6.9.3 Stratified Sampling.
6.10 Sources of Data.
6.11 Database.
6.12 Summary.
Glossary.
References.
7 Quality Tools.
7.1 Overview.
7.2 Introduction.
7.3 Nature of Six Sigma Variables.
7.3.1 CT Concept.
7.3.2 CTQ and CTP Characteristics.
7.3.3 CTX Tree (Process Tree).
7.3.4 CTY Tree (Process Tree).
7.3.5 The Focus of Six Sigma.
7.3.6 The Leverage Principle.
7.4 Quality Function Deployment (QFD).
7.5 Scales of Measurement.
7.5.1 Likert Scale.
7.5.2 Logarithm Scale.
7.6 Diagnostic Tools.
7.6.1 Elements for Problem Solving-Diagnostic Tools and Methods.
7.6.2 Problem Definition-Defining Project Objective.
7.7 Analytical Methods.
7.7.1 Cause-Effect (CE) Analysis.
7.7.2 Failure Mode-Effects Analysis (FMEA)
7.7.3 XY Matrix.
7.8 Graphical Tools.
7.8.1 Graphical Summary.
7.8.2 Boxplot or Box-and-Whisker Plot.
7.8.3 Normal Probability Plot.
7.8.4 Main-Effects Plot.
7.8.5 Pareto Chart.
7.8.6 Run Chart.
7.8.7 Time-Series Plot.
7.8.8 Multi-Vari Charts.
7.8.9 Scatterplot.
7.9 Graphical Representation of a Process.
7.9.1 Process Flowcharts.
7.9.2 Process Mapping.
7.9.3 Cross-Functional Mapping.
7.9.4 Process Mapping-Deployment Diagram.
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 Checksheets.
7.15 Scorecards.
7.16 Affinity Diagram.
7.17 Concept Integration.
Glossary.
Reference.
8 Making Sense of Data in Six Sigma and Lean.
8.1 Overview.
8.2 Summarizing Quantitative Data: Graphical Methods.
8.2.1 Analytical Charts.
8.2.2 Dotplots.
8.2.3 Stem-and-Leaf Plots.
8.2.4 Frequency Tables.
8.2.5 Histograms and Performance Histograms.
8.2.6 Run Charts.
8.2.7 Time-Series Plots.
8.3 Summarizing Quantitative Data: Numerical Methods.
8.3.1 Measures of Center.
8.3.2 Measures of Variation.
8.3.3 Identifying Potential Outliers.
8.3.4 Measures of Position and the Idea of z Scores in Six Sigma.
8.3.5 Measure of Spread and Lean Sigma.
8.4 Organizing and Graphing Qualitative Data.
8.4.1 Organizing Qualitative Data.
8.4.2 Graphing Qualitative Data.
8.4.3 Pareto Analysis with Lorenz Curve.
8.5 Summarizing Bivariate Data.
8.5.1 Scatterplot.
8.5.2 Correlation Coefficient.
8.6 Multi-Vari Charts.
Glossary.
Exercises.
9 Fundamentals of Capability and Rolled Throughput Yield.
9.1 Overview.
9.2 Introduction.
9.3 Why Capability.
9.3.1 Performance Specifications.
9.3.2 Fundamental Concepts of Defect-Based Measurement.
9.4 Six Sigma Capability Metric.
9.4.1 Criteria for Performance Metrics.
9.4.2 Computing the Sigma Level from Discrete Data.
9.4.3 Defective Proportions.
9.4.4 Six-Sigma-Level Calculations (DPU, DPO, DPMO, PPM)-Examples.
9.5 Discrete Capability.
9.6 Continuous Capability-Example.
9.6.1 Data Collection for Capability Studies.
9.7 Fundamentals of Capability.
9.8 Short- Versus Long-Term Capability.
9.8.1 Short-Term Capability.
9.8.2 Long-Term Capability
9.8.3 Introduction to Calibrating the Shift.
9.9 Capability and Performance.
9.10 Indices of Capability.
9.10.1 Cp Index.
9.10.2 Cpk Index.
9.10.3 Pp Index.
9.10.4 Ppk Index.
9.11 Calibrating the Shift.
9.12 Applying the 1.5¿ Shift Concept.
9.13 Yield.
9.13.1 Final Test Yield (FTY).
9.13.2 Yield Related to Defects.
9.13.3 Rolled Throughput Yield (RTY).
9.13.4 In-Process Yield (IPY).
9.13.5 In-Process Yield (IPY) and Rolled Throughput Yield (RTY).
9.14 Hidden Factory.
9.14.1 Hidden Factory Composition.
Glossary.
References.
10 Probability.
10.1 Overview.
10.2 Experiments, Outcomes, and Sample Space.
10.3 Calculating Probability.
10.3.1 Equally Likely Events.
10.3.2 Probability as Relative Frequency.
10.3.3 Subjective Probability.
10.4 Combinatorial Probability.
10.5 Marginal and Conditional Probabilities.
10.6 Union of Events.
10.6.1 Addition Role.
10.6.2 Mutually Exclusive Events.
10.6.3 Complementary...
| Erscheinungsjahr: | 2010 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: | 832 S. |
| ISBN-13: | 9780470114940 |
| ISBN-10: | 0470114940 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Harry, Mikel J
Mann, Prem S de Hodgins, Ofelia C Hulbert, Richard L Lacke, Christopher J |
| Hersteller: |
Wiley
John Wiley & Sons |
| Maße: | 260 x 183 x 48 mm |
| Von/Mit: | Mikel J Harry (u. a.) |
| Erscheinungsdatum: | 19.01.2010 |
| Gewicht: | 1,724 kg |
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