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1.1 The Basics of Six Sigma
- Meanings of Six Sigma
- General History of Six Sigma & Continuous Improvement
- Deliverables of a Lean Six Sigma Project
- The Problem Solving Strategy Y = f(x)
- Voice of the Customer, Business, and Employee
- Six Sigma Roles & Responsibilities
1.2 The Fundamentals of Six Sigma
- Defining a Process
- Critical to Quality Characteristics (CTQ’s)
- Cost of Poor Quality (COPQ)
- Pareto Analysis (80:20 rule)
- Basic Six Sigma Metrics
- including DPU, DPMO, FTY, RTY Cycle Time; deriving these metrics
1.3 Selecting Lean Six Sigma Projects
- Building a Business Case & Project Charter
- Developing Project Metrics
- Financial Evaluation & Benefits Capture
1.4 The Lean Enterprise
- Understanding Lean
- The History of Lean
- Lean & Six Sigma
- The Seven Elements of Waste
- Overproduction, Correction, Inventory, Motion, Overprocessing, Conveyance, Waiting.
- 5S
- Sort, Straighten, Shine, Standardize, Self-Discipline
2.1 Process Definition
- Cause & Effect / Fishbone Diagrams
- Process Mapping, SIPOC, Value Stream Map
- X-Y Diagram
- Failure Modes & Effects Analysis (FMEA)
2.2 Six Sigma Statistics
- Basic Statistics
- Descriptive Statistics
- Normal Distributions & Normality
- Graphical Analysis
2.3 Measurement System Analysis
- Precision & Accuracy
- Bias, Linearity & Stability
- Gage Repeatability & Reproducibility
- Variable & Attribute MSA
2.4 Process Capability
- Capability Analysis
- Concept of Stability
- Attribute & Discrete Capability
- Monitoring Techniques
3.1 Patterns of Variation
- Multi-Vari Analysis
- Classes of Distributions
3.2 Inferential Statistics
- Understanding Inference
- Sampling Techniques & Uses
- Central Limit Theorem
3.3 Hypothesis Testing
- General Concepts & Goals of Hypothesis Testing
- Significance; Practical vs. Statistical
- Risk; Alpha & Beta
- Types of Hypothesis Test
3.4 Hypothesis Testing with Normal Data
- 1 & 2 sample t-tests
- 1 sample variance
- One Way ANOVA
- Including Tests of Equal Variance, Normality Testing, and Sample Size calculation, performing tests, and interpreting results.
3.5 Hypothesis Testing with Non-Normal Data
- Mann-Whitney
- Kruskal-Wallis
- Mood’s Median
- Friedman
- 1 Sample Sign
- 1 Sample Wilcoxon
- One and Two Sample Proportion
- Chi-Squared (Contingency Tables)
- Including Tests of Equal Variance, Normality Testing, and Sample Size calculation, performing tests, and interpreting results.
4.1 Simple Linear Regression
- Correlation
- Regression Equations
- Residuals Analysis
4.2 Multiple Regression Analysis
- Non- Linear Regression
- Multiple Linear Regression
- Confidence & Prediction Intervals
- Residuals Analysis
- Data Transformation, Box-Cox
4.3 Designed Experiments
- Experiment Objectives
- Experimental Methods
- Experiment Design Considerations
4.4 Full Factorial Experiments
- 2k Full Factorial Designs
- Linear & Quadratic Mathematical Models
- Balanced & Orthogonal Designs
- Fit, Diagnose Model, and Center Points
4.5 Fractional Factorial Experiments
- Designs
- Confounding Effects
- Experimental Resolution
5.1 Lean Controls
- Control Methods for 5S
- Kanban
- Poka-Yoke (Mistake Proofing)
5.2 Statistical Process Control (SPC)
- Data Collection for SPC
- I-MR Chart
- Xbar-R Chart
- U Chart
- P Chart
- NP Chart
- Xbar-S Chart
- CuSum Chart
- EWMA Chart
- Control Methods
- Control Chart Anatomy
- Subgroups, Impact of Variation, Frequency of Sampling
- Center Line & Control Limit Calculations
5.3 Six Sigma Control Plans
- Cost-Benefit Analysis
- Elements of the Control Plan
- Elements of the Response Plan
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