Six Sigma for Students: A Problem-Solving Methodology

✍ Scribed by Fatma Pakdil

• Publisher: Palgrave Macmillan
• Year: 2020
• Tongue: English
• Leaves: 506
• Category: Library

✦ Synopsis

This textbook covers the fundamental mechanisms of the Six Sigma philosophy, while showing how this approach is used in solving problems that affect the variability and quality of processes and outcomes in business settings. Further, it teaches readers how to integrate a statistical perspective into problem solving and decision-making processes. Part I provides foundational background and introduces the Six Sigma methodology while Part II focuses on the details of DMAIC process and tools used in each phase of DMAIC. The student-centered approach based on learning objectives, solved examples, practice and discussion questions is ideal for those studying Six Sigma.

✦ Table of Contents

Preface
Acknowledgments
Contents
Abbreviations
List of Figures
List of Images
List of Tables
I: Organization of Six Sigma
1: Overview of Quality and Six Sigma
1.1 Introduction
1.2 The Six Sigma Philosophy
1.3 Quality Definitions
1.3.1 The Product-Based Approach
1.3.2 The Manufacturing-Based Approach
1.3.3 The Value-Based Approach
1.3.4 The Customer-Based Approach
1.4 Quality Gurus and Thinkers
1.4.1 Walter Shewhart
1.4.2 W. Edwards Deming
1.4.3 Joseph M. Juran
1.4.4 Armand V. Feigenbaum
1.4.5 Kaoru Ishikawa
1.4.6 Taiichi Ohno
1.4.7 Dr. Shigeo Shingo
1.4.8 Genichi Taguchi
1.4.9 Philip B. Crosby
1.4.10 David Garvin
1.4.11 Douglas Montgomery
1.5 The Historical Background of Six Sigma
1.6 Standards in Six Sigma
1.7 Quality Costs
1.7.1 Quality Cost Definition
1.7.2 Quality Cost Categories
1.7.3 Performance Metrics in Quality Costs
References
2: Organization for Six Sigma
2.1 Introduction
2.2 Six Sigma Leaders’ Approaches and Organizational Vision
2.3 Roles and Responsibilities in Six Sigma Organization
2.3.1 Executive Committee
2.3.2 Project Champions
2.3.3 Deployment Manager
2.3.4 Process Owners
2.3.5 Master Black Belts
2.3.6 Black Belts
2.3.7 Green Belts
2.3.8 Finance Representatives
2.3.9 Team Members
References
3: Cultural Considerations for Effective Six Sigma Teams
3.1 Introduction
3.2 Different Faces of Culture
3.3 Organizational Culture
3.4 Professional Culture
3.5 Societal Culture
3.6 Cultural Change
3.6.1 Changing Organizational Culture
3.6.2 Diagnosing Potential Organizational Culture to Implement Six Sigma
References
II: Six Sigma Process: DMAIC
4: Define Phase: D Is for Define
4.1 Introduction
4.2 Process Analysis and Documentation Tools
4.2.1 Transformation Process
4.2.2 Value Stream Analysis and Map
4.2.3 Flow Chart
4.2.4 SIPOC Diagram
4.2.5 Swim Lane
4.2.6 Spaghetti Diagram
4.3 Stakeholder Analysis
4.4 Project Prioritization and Selection
4.4.1 Qualitative Approaches
4.4.2 Quantitative Approaches
4.5 Project Charter
4.5.1 Problem Statement
4.5.2 Goal Statement
4.5.3 Project Scope
4.5.4 Project Metrics
4.5.5 Project CTQ Characteristics
4.5.6 Project Deliverables
4.6 Project Planning
4.7 Quality Function Deployment
References
Further Reading
5: Measure Phase: M Is for Measure
5.1 Introduction
5.2 What Are Data?
5.3 Data Collection Plans
5.4 Types of Variables
5.5 Types of Sampling
5.5.1 Probability Sampling Methods
5.5.1.1 Simple Random Sampling
5.5.1.2 Stratified Random Sampling
5.5.1.3 Systematic Sampling
5.5.1.4 Cluster Sampling
5.5.2 Non-probability Sampling Methods
5.5.2.1 Quota Sampling
5.5.2.2 Snowball Sampling
5.5.2.3 Convenience Sampling
5.5.2.4 Purposive Sampling
5.6 Measuring Limits of the CTQ Characteristics
5.7 Six Sigma Measurements
References
Further Reading
6: Measurement System Analysis: Gage R&R Analysis
6.1 Introduction
6.2 Gage R&R Analysis
References
7: Analyze Phase: A Is for Analyze
7.1 Introduction
7.2 Descriptive Statistics
7.2.1 Measures of Central Tendency
7.2.1.1 Mean
7.2.1.2 Mode
7.2.1.3 Median
7.2.2 Measures of Variability (Dispersion)
7.2.2.1 Range
7.2.2.2 Standard Deviation
7.2.2.3 Variance
7.3 Other Descriptive Measures
7.3.1 Quartiles
7.3.2 The Five-Measure Summary
7.4 The Shape of Distribution
7.5 Types of Variation
7.6 Statistical Distributions
7.6.1 Random Variables
7.6.1.1 Discrete Random Variables
7.6.1.2 Continuous Random Variables
7.6.2 Cumulative Distribution Function (CDF)
7.6.3 Discrete Distributions
7.6.3.1 Bernoulli Distribution
7.6.3.2 Binomial Distribution
7.6.3.3 Hypergeometric Distribution
7.6.3.4 Geometric Distribution
7.6.3.5 Poisson Distribution
7.6.4 Continuous Distributions
7.6.4.1 Uniform Distribution
7.6.4.2 Exponential Distribution
7.6.4.3 Triangular Distribution
7.6.4.4 Normal Distribution (Gaussian Distribution)
7.6.4.5 Weibull Distribution
7.7 Inferential Statistics: Fundamentals of Inferential Statistics
7.7.1 Sampling Distribution
7.7.2 Properties of Sampling Distributions
7.7.2.1 First Property: The Standard Error of the Mean
7.7.2.2 Second Property: The Central Limit Theorem
7.7.3 Estimation
7.7.3.1 Point Estimates
7.8 Inferential Statistics: Interval Estimation for a Single Population
7.8.1 Interval Estimates
7.8.2 Confidence Interval Estimation
7.8.2.1 Confidence Interval Estimation for the Mean
Confidence Interval for the Mean (σ Is Known)
One-Sided Confidence Interval for the Mean (σ Is Known)
Confidence Interval for the Mean (σ Is Unknown, Large Sample)
Confidence Interval for the Mean (σ Is Unknown, Small Sample)
7.8.2.2 Confidence Interval Estimation for the Variance and Standard Deviation
7.8.2.3 Confidence Interval Estimation for the Proportion (Large Sample)
7.8.3 Tolerance Interval Estimation
7.8.4 Prediction Interval Estimation
7.9 Inferential Statistics: Hypothesis Testing for a Single Population
7.9.1 Concepts and Terminology of Hypothesis Testing
7.9.1.1 Assumptions and Conditions
7.9.1.2 Formulation of Null and Alternative Hypotheses
7.9.1.3 Decisions and Errors in a Hypothesis Test
7.9.1.4 Test Statistics and Rejection Regions
7.9.1.5 Reporting Test Results: p-Values
7.9.2 Hypothesis Tests for a Single Population
7.9.3 Testing of the Population Mean
7.9.3.1 Tests of the Mean of a Normal Distribution (Population Standard Deviation Known)
7.9.3.2 Tests of the Mean of a Normal Distribution (Population Standard Deviation Unknown)
7.9.4 Testing the Population Variance of a Normal Distribution
7.9.5 Testing the Population Proportion (Large Samples)
7.10 Inferential Statistics: Comparing Two Populations
7.10.1 Connection Between Hypothesis Test and Confidence Interval Estimation
7.10.2 Comparing Two Population Means: Independent Samples
7.10.2.1 Population Variances Unknown and Assumed to Be Equal
7.10.2.2 Population Variances Unknown and Assumed to Be Unequal
7.10.3 Comparing Two Population Means: Dependent (Paired) Samples
7.10.4 Comparing Two Normally Distributed Population Variances
7.10.5 Comparing Two Population Proportions (Large Samples)
7.11 Correlation Analysis
7.12 Regression Analysis
7.13 ANOVA – Analysis of Variance
7.13.1 One-Way ANOVA
7.14 Process Capability Analysis
7.15 Taguchi’s Loss Function
7.15.1 Nominal Is the Best
7.15.2 Smaller Is the Best
7.15.3 Larger Is the Best
References
8: Analyze Phase: Other Data Analysis Tools
8.1 Introduction
8.2 Seven Old Tools
8.2.1 Check Sheet
8.2.2 Histogram
8.2.3 Fishbone Diagram  Cause-and-Effect Diagram
8.2.4 Pareto Analysis and Diagram
8.2.5 Scatter Diagram
8.2.6 Stratification Analysis
8.2.7 Control Charts
8.3 Seven New Tools
8.3.1 Affinity Diagram
8.3.2 Systematic Diagram
8.3.3 Arrow Diagram
8.3.4 Relations Diagram
8.3.5 Matrix Diagram
8.3.6 Matrix Data Analysis
8.3.7 Process Decision Program Chart (PDPC)
8.4 Other Tools
8.4.1 Brainstorming
8.4.2 5 Whys Analysis
8.4.3 Dot Plot
8.4.4 Run Chart
8.4.5 Box-and-Whisker Plot
8.4.6 Probability Plot
8.4.7 Bar Chart
8.4.8 Line Graph
8.4.9 Stem-and-Leaf Plot
References
9: Control Charts
9.1 Introduction
9.2 Elements of Control Charts
9.3 Implementation of Control Charts
9.4 Decision-Making on Control Charts
9.5 Control Charts for Variables
9.5.1 Charts
9.5.2 Charts
9.5.2.1 The and S Charts When the Sample Size Is Constant
9.5.2.2 The and S Charts When the Sample Size Is Not Constant
9.5.3 X − MR Charts
9.6 Control Charts for Attributes
9.6.1 Control Charts for Fraction Nonconforming
9.6.1.1 P Charts
9.6.1.2 np Charts
9.6.2 Control Charts for Nonconformities
9.6.2.1 c Charts
9.6.2.2 u Charts
References
10: Improve Phase: I Is for Improve
10.1 Introduction
10.2 Experimental Design – Design of Experiment (DOE)
10.2.1 DOE Steps
10.2.2 DOE Methods
10.2.2.1 Single Factor Experiments
10.2.2.2 Two-Factor Factorial Designs
10.2.2.3 Full Factorial Experiments
10.2.2.4 Fractional Factorial Experiment
10.2.2.5 Screening Experiments
10.2.2.6 Response Surface Designs
10.3 Simulation
10.3.1 Introduction
10.3.2 What Is Simulation?
10.3.3 Types of Simulation Models
10.3.4 How Are Simulations Performed?
10.3.4.1 Simulation by Hand (Manual Simulation)
10.3.4.2 Simulation with General Purpose Languages
10.3.4.3 Special Purpose Simulation Languages
10.3.5 Concepts of the Simulation Model
10.3.5.1 The System
10.3.5.2 Steps of Building a Simulation Model
10.3.6 Simulation Modeling Features
10.3.6.1 Discrete Event Simulation (DES)
10.3.6.2 Start and Stop of Simulation
10.3.6.3 Queueing Theory
10.3.6.4 Performance Measures
10.3.7 Performing an Event-Driven Simulation
10.3.7.1 Simulation Clock and Time Advancement Mechanism
10.3.7.2 Event-Driven Simulation by Hand
10.3.7.3 Randomness in Simulation
10.4 Lean Philosophy and Principles
10.5 Failure Modes and Effects Analysis
References
Further Readings
11: Control Phase: C Is for Control
11.1 Introduction
11.2 Steps in the Control Phase
11.2.1 Implementing Ongoing Measurements
11.2.2 Standardization of the Solutions
11.2.3 Monitoring the Improvements
11.2.4 Project Closure
11.3 Tools in Control Phase
11.3.1 Statistical Process Control
11.3.2 Control Plans
References
Appendix
Index