| Define Phase | |
| 1.1 The Basics of Six Sigma | 1.1.1 Meanings of Six Sigma |
| 1.1.2 General History of Six Sigma & Continuous Improvement | |
| 1.1.3 Deliverables of a Lean Six Sigma Project | |
| 1.1.4 The Problem Solving Strategy Y = f(x) | |
| 1.1.5 Voice of the Customer, Business and Employee | |
| 1.1.6 Six Sigma Roles & Responsibilities | |
| 1.2 The Fundamentals of Six Sigma | 1.2.1 Defining a Process |
| 1.2.2 Critical to Quality Characteristics (CTQ’s) | |
| 1.2.3 Cost of Poor Quality (COPQ) | |
| 1.2.4 Pareto Analysis (80:20 rule) | |
| 1.2.5 Basic Six Sigma Metrics | |
| 1.3 Selecting Lean Six Sigma Projects | 1.3.1 Building a Business Case & Project Charter |
| 1.3.2 Developing Project Metrics | |
| 1.3.3 Financial Evaluation & Benefits Capture | |
| 1.4 The Lean Enterprise | 1.4.1 Basics of Lean |
| 1.4.2 History of Lean | |
| 1.4.3 Lean & Six Sigma Integration | |
| 1.4.4 The Seven Elements of Waste | |
| 1.4.5 5S - Straighten, Shine, Standardize, Self-Discipline, Sort | |
| Measure Phase | |
| 2.1 Process Definition | 2.1.1 Cause & Effect / Fishbone Diagrams |
| 2.1.2 Process Mapping, SIPOC, Value Stream Map | |
| 2.1.3 X-Y Diagram | |
| 2.1.4 Failure Modes & Effects Analysis (FMEA) | |
| 2.2 Lean Six Sigma Statistics | 2.2.1 Basic Applied Statistics |
| 2.2.2 Descriptive Statistics | |
| 2.2.3 Distributions | |
| 2.2.4 Graphical Analysis | |
| 2.3 Measurement System Analysis | 2.3.1 Precision & Accuracy |
| 2.3.2 Bias, Linearity & Stability | |
| 2.3.3 Gage Repeatability & Reproducibility | |
| 2.3.4 Variable & Attribute MSA | |
| 2.4 Process Capability | 2.4.1 Capability Analysis |
| 2.4.2 Concept of Stability | |
| 2.4.3 Attribute & Discrete Capability | |
| 2.4.4 Monitoring Techniques | |
| Analyze Phase | |
| 3.1 Patterns of Variation | 3.1.1 Multi-Vari Analysis |
| 3.1.2 Classes of Distributions | |
| 3.2 Inferential Statistics | 3.2.1 Understanding Inference |
| 3.2.2 Sampling Techniques & Uses | |
| 3.2.3 Central Limit Theorem | |
| 3.3 Hypothesis Testing | 3.3.1 General Concepts & Goals of Hypothesis Testing |
| 3.3.2 Significance; Practical vs. Statistical | |
| 3.3.3 Risk; Alpha & Beta | |
| 3.4 Hypothesis Testing with Normal Data | 3.4.1 1-sample & 2 sample t-tests |
| 3.4.2 One-Way ANOVA | |
| 3.4.3 Two-Way ANOVA | |
| 3.5 Hypothesis Testing with Non-Normal Data | 3.5.1 Mann-Whitney |
| 3.5.2 Kruskal-Wallis | |
| 3.5.3 Mood’s Median | |
| 3.5.4 Friedman | |
| 3.5.5 1 Sample Sign | |
| 3.5.6 1 Sample Wilcoxon | |
| 3.5.7 One and Two Sample Proportion | |
| 3.5.8 Chi-Squared (Contingency Tables) | |
| Improve Phase | |
| 4.1 Simple Linear Regression | 4.1.1 Correlation |
| 4.1.2 Regression Equations | |
| 4.1.3 Residuals Diagnostics Analysis | |
| 4.2 Multiple Regression Analysis | 4.2.1 Non-Linear Regression |
| 4.2.2 Multiple Linear Regression | |
| 4.2.3 Confidence & Prediction Intervals | |
| 4.2.4 Residuals Diagnostics Analysis | |
| 4.2.5 Data Transformation, Box Cox Technique | |
| 4.3 Designed Experiments | 4.3.1 Experimental Objectives |
| 4.3.2 Experimental Methods | |
| 4.3.3 Experiment Design Considerations | |
| 4.4 Full Factorial Experiments | 4.4.1 2k Full Factorial Designs |
| 4.4.2 Linear & Quadratic Mathematical Models | |
| 4.4.3 Balanced & Orthogonal Designs | |
| 4.4.4 Fit, Diagnose Model and Center Points | |
| 4.5 Fractional Factorial Experiments | 4.5.1 Designs |
| 4.5.2 Confounding Effects | |
| 4.5.3 Experimental Resolution | |
| 4.6 Advanced Experiments | 4.6.1 Steepest Ascent Analysis |
| Control Phase | |
| 5.1 Lean Controls | 5.3.1 Control Methods for 5S |
| 5.3.2 Kanban (Pull Systems) | |
| 5.3.3 Poka-Yoke (Mistake Proofing) | |
| 5.2 Statistical Process Control (SPC) | 5.4.1 Data Collection for SPC |
| 5.4.2 I-MR Chart | |
| 5.4.3 Xbar-R Chart | |
| 5.4.4 U Chart | |
| 5.4.5 P Chart | |
| 5.4.6 NP Chart | |
| 5.4.7 Xbar-S Chart | |
| 5.4.8 CumSum Chart | |
| 5.4.9 EWMA Chart | |
| 5.4.10 Control Methods | |
| 5.3 Six Sigma Control Plans | 5.6.1 Cost Benefit Analysis |
| 5.6.2 Elements of the Control Plan | |
| 5.6.3 Elements of the Response Plan | |