R code and data sets can be found at the website listed.
Table of Contents
Part I Basics
1 Six Sigma in a Nutshell
1.1 Introduction
1.2 Brief History
1.3 What Is Six Sigma?
1.4 DMAIC Cycle
1.4.1 Define Phase
1.4.2 Measure Phase
1.4.3 Analyze Phase
1.4.4 Improve Phase
1.4.5 Control Phase
1.5 Six Sigma Operational Structure
1.6 Summary and Further Reading
Case Study
2 R from the Beginning
2.1 Introduction
2.2 First Steps
2.2.1 Get and Install
2.2.2 Run and Interact
2.2.3 Ask for Help
2.3 Coping with Data
2.3.1 Data Types
2.3.2 Creating Data Objects
2.3.3 Accessing Data
2.3.4 Importing and Exporting Data
2.4 Objects and Functions
2.4.1 Objects
2.4.2 Functions
2.5 Operators and Functions Commonly Used
2.5.1 Operators
2.5.2 Mathematical Functions
2.5.3 Functions for Vectors
2.5.4 Loop and Summary Functions
2.6 Graphics in R
2.6.1 Plotting Functions
2.6.2 Bivariate Plots
2.6.3 Pie Plots
2.7 Statistics
2.7.1 Samples and Combinatorial Computations
2.7.2 Random Variables
2.8 Summary and Further Reading
Case Study
Practice
Part II R Tools for the Define Phase
3 Process Mapping with R
3.1 Introduction
3.2 Process Mapping as a Problem-solving Method
3.3 Strategies for Process Mapping
3.3.1 Two-stage Process Mapping
3.3.2 Drilling Down into the Process Steps
3.4 Step-by-Step Process Mapping
3.4.1 Identifying Inputs and Outputs
3.4.2 Listing the Project Steps
3.4.3 Identifying the Outputs of Each Step
3.4.4 Identifying the Parameters of Each Step
3.4.5 Classifying the Parameters
3.5 Drawing a Process Map with the Six Sigma Package
3.6 Why Should I Use R for Drawing Diagrams?
3.7 Summary and Further Reading
Case Study
Practice
4 Loss Function Analysis with R
4.1 Introduction
4.2 Cost of Poor Quality
4.3 Modeling the Loss Function
4.3.1 Some Notation
4.3.2 Taguchi Loss Function
4.4 Average Loss Function
4.5 Use of Loss Function Within DMAIC Cycle
4.6 Loss Function Analysis with Six Sigma Package
4.7 Other Models
4.8 Summary and Further Reading
Case Study
Practice
Part III R Tools for the Measure Phase
5 Measurement System Analysis with R
5.1 Introduction
5.1.1 Definitions
5.2 Data Analysis
5.2.1 Data Collection
5.2.2 First Approach to Analysis of Variance (ANOVA)
5.2.3 Assessing the Measurement System
5.3 Using the SixSigma Package
5.3.1 Interpreting the Charts
5.4 Summary and Further Reading
Case Study
Practice
6 Pareto Analysis with R
6.1 Introduction
6.2 Pareto Principle
6.2.1 Pareto Principle as a Problem-solving Technique
6.3 Pareto Analysis in Six Sigma
6.3.1 Identifying Causes
6.3.2 Measuring the Effect
6.3.3 Building a Pareto Chart
6.4 Pareto Charts in R
6.5 Other Uses of the Pareto Chart
6.6 Summary and Further Reading
Case Study
Practice
7 Process Capability Analysis with R
7.1 Introduction
7.2 Specifications
7.3 Process Performance
7.4 Process vs. Specifications
7.5 Capability Indices
7.6 Capability Study with SixSigma Package
7.7 Summary and Further Reading
Case Study
Practice
Part IV R Tools for the Analyze Phase
8 Charts with R
8.1 Introduction
8.1.1 Use of Charts
8.1.2 Background Concepts
8.2 Bar Chart
8.3 Histogram
8.4 Scatterplot
8.5 Run Chart
8.6 Tier Chart
8.7 Box–Whisker Chart
8.8 Other Charts
8.8.1 Group Chart
8.8.2 Location Charts
8.8.3 Multivariate Chart
8.8.4 More About Charts
8.9 Summary and Further Reading
Case Study
Practice
9 Statistics and Probability with R
9.1 Introduction
9.1.1 Variables and Observations
9.1.2 Summary Tables
9.1.3 Population and Sample
9.1.4 Special Data Values
9.2 Descriptive
9.2.1 Measures of Central Tendency
9.2.2 Measures of Variability
9.3 Probability
9.3.1 Random Variables
9.3.2 Binomial Distribution
9.3.3 Normal Distribution
9.3.4 Other Useful Distributions
9.4 Summary and Further Reading
Case Study
Practice
10 Statistical Inference with R
10.1 Introduction
10.2 Confidence Intervals
10.2.1 Sampling Distribution and Point Estimation
10.2.2 Proportion Confidence Interval
10.2.3 Mean Confidence Interval
10.2.4 Variance Confidence Interval
10.3 Hypothesis Testing
10.4 Regression
10.4.1 Model Identification
10.4.2 Model Fitting
10.4.3 Model Validation
10.4.4 Other Models
10.5 Analysis of Variance
10.5.1 Model Identification
10.5.2 Model Fitting and Validation
10.5.3 Additional Models and Related Tools
10.6 Summary and Further Reading
Case Study
Practice
Part V R Tools for the Improve Phase
11 Design of Experiments with R
11.1 Introduction
11.2 Importance of Experimenting
11.2.1 Inconsistent Data
11.2.2 Variable Value Range
11.2.3 Correlated Variables
11.3 Experimentation Strategies
11.3.1 Planning Strategies
11.3.2 Factor Levels and Replications
11.3.3 Progressive Experimentation
11.3.4 Model Assumptions
11.4 2 k-Factorial Designs
11.5 Design of Experiments for Process Improvement
11.6 Summary and Further Reading
Case Study
Practice
Part VI R Tools for the Control Phase
12 Process Control with R
12.1 Introduction
12.2 Mistake-proofing Strategies (Poka-Yoke)
12.3 What to Control
12.4 Control Chart Basics
12.4.1 The Chart
12.4.2 Chart Interpretation
12.4.3 Sampling Strategy
12.5 Plotting Process Control Charts
12.5.1 Notation for Computing Control Lines
12.5.2 Variable Control Charts
12.5.3 Attribute Control Charts
12.6 Summary and Further Reading
Case Study
Practice
Part VII Further and Beyond
13 Other Tools and Methodologies
13.1 Introduction
13.2 Failure Mode, Effects, and Criticality Analysis
13.3 Design for Six Sigma
13.4 Lean
13.5 Gantt Chart
13.6 Some Advanced R Topics
13.6.1 Programming
13.6.2 R User Interfaces
13.6.3 Reporting
13.7 Summary and Further Reading
Case Study
Appendix A - R Basic Reference Guide
References
Solutions
R Packages and Functions Used in the Book
Subject Index