Training: Black Belt Lean Six Sigma

Module 1 : Define Phase

• Lesson 1: Voice of the customer
• Lesson 2: Project charter
• Lesson 3: Problem statement
• Lesson 4: Project scope
• Lesson 5: Goals and objectives
• Lesson 6: Project performance measures
• Lesson 7: Project tracking
• Lesson 8: Project Stakeholder Analysis
• Lesson 9: Measurable Customer Requirements
• Lesson 10: Requirements Statements
• Lesson 11: Process Mapping
• Lesson 12: SIPOC

Module 2 : Measure Phase

• Lesson 1: Process characteristics
• Lesson 2: Input and output variables
• Lesson 3: Process flow metrics
• Lesson 4: Process analysis tools
• Lesson 5: Data collection
• Lesson 6: Types of data
• Lesson 7: Measurement scales
• Lesson 8: Sampling methods
• Lesson 9: Collecting data
• Lesson 10: Measurement systems, methods, systems analysis
• Lesson 11: Basic statistics
• Lesson 12: Basic terms
• Lesson 13: Central limit theorem
• Lesson 14: Descriptive statistics
• Lesson 15: Graphical methods
• Lesson 16: Valid statistical-conclusions
• Lesson 17: Probability
• Lesson 18: Basic concepts and Distributions
• Lesson 19: Process capability
• Lesson 20: Process capability indices
• Lesson 21: Process performance indices
• Lesson 22: Short-term and long-term capability
• Lesson 23: Process capability for non-normal data
• Lesson 24: Process capability for attributes data
• Lesson 25: Process capability studies and Process performance vs. specification

Module 3 : Analyse Phase

• Lesson 1: Data Analysis Overview
• Lesson 2: Pareto Analysis
• Lesson 3: Gap analysis
• Lesson 4: Root cause analysis
• Lesson 5: Waste analysis
• Lesson 6: Run Charts
• Lesson 7: Histogram/Frequency Plot
• Lesson 8: Cause and Effect Analysis
• Lesson 9: Scatter Plot or Correlation Diagram
• Lesson 10: Multi-Variant Analysis
• Lesson 11: Correlation coefficient
• Lesson 12: Regression
• Lesson 13: Multivariate tools
• Lesson 14: Multi-vari studies
• Lesson 15: Attributes data analysis
• Lesson 16: Inferential Statistics Primer
• Lesson 17: Hypothesis testing
• Lesson 18: Terminology
• Lesson 19: Statistical vs. practical
• Lesson 20: Significance
• Lesson 21: Sample size
• Lesson 22: Design of Experiments Overview
• Lesson 23: Failure mode and effects analysis (FMEA)

Module 4 : Improve Phase

• Lesson 1: Generating Creative Solutions
• Lesson 2: Brainstorming
• Lesson 3: Analysing and Selecting Solutions
• Lesson 4: Decision Matrix
• Lesson 5: Autonomous Maintenance / TPM
• Lesson 6: Quick Changeover / SMED
• Lesson 7: Line Balancing/Operator Balance Charts
• Lesson 8: Continuous Flow Layouts
• Lesson 9: Kanban/Pull Systems
• Lesson 10: Kaizen Events
• Lesson 11: Pilot Testing
• Lesson 12: Full-Scale Implementation
• Lesson 13: Creativity and Innovation
• Lesson 14: Eliminate, Combine, Redesign, Simplify (ECRS)
• Lesson 15: Design of experiments (DOE)
• Lesson 16: Waste elimination
• Lesson 17: Cycle-time reduction
• Lesson 18: Kaizen and Kaizen Blitz-Theory of constraints (TOC)
• Lesson 19: TRIZ
• Lesson 20: Risk analysis and mitigation

Module 5 : Control Phase

• Lesson 1: Control Plan Elements
• Lesson 2: Statistical Process Control
• Lesson 3: Statistical process control
• Lesson 4: Objectives
• Lesson 5: Selection of variables
• Lesson 6: Rational sub-grouping
• Lesson 7: Control chart selection
• Lesson 8: Control chart analysis
• Lesson 9: Other control tools
• Lesson 10: Total productive maintenance
• Lesson 11: Visual factory
• Lesson 12: Maintain controls
• Lesson 13: Measurement system re-analysis
• Lesson 14: Control plan
• Lesson 15: Sustain improvements
• Lesson 16: Documentation
• Lesson 17: Design for Six Sigma (DFSS) Frameworks and Methodologies
• Lesson 18: Common DFSS methodologies
• Lesson 19: Customer Expectations
• Lesson 20: House of Quality
• Lesson 21: Critical to Quality Deployment
• Lesson 22: Critical Parameter Management
• Lesson 23: Design for X (DFX)
• Lesson 24: Robust design and process (Special design tools)