Lean Six Sigma Black Belt Certification Training

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