Session 1: Managing & Defining Black Belt Projects
- DMAIC versus DMADV projects
- Scoping complex cross-functional projects
- Value stream mapping as a scoping tool
- Aligning the project to business strategy
- Managing project reviews (tollgates)
- Considering project risks
Session 2: Value Stream Mapping
- Introduction to Value Stream Mapping
- Creating a Current State Map
- Using VSM as a Scoping Tool
- Identifying the Opportunities
Session 3: Data Collection Planning
- The role of data collection planning throughout a DMAIC project
- Use of Is/Is Not to find gaps in knowledge
- Understanding variation
- Selecting what to measure
- Calculating sample size
- Sampling considerations
- Developing a robust data collection plan
- Guidelines for survey sampling
Session 4: Measurement System Analysis
- MSA fundamentals
- Type 1 studies for repeatability
- Type II studies (reproducibility)
- Nested gauge R&Rs for destructive tests
- Assessing linearity & bias aspects of calibration
- Assessing stability (of bias during calibration)
- Attribute agreement analysis for pass/fail judgements
Session 5: Process Capability & Process Control
- Assessing process control
- Anatomy & use of control charts
- Applications of SPC charts for variable and attribute data
- Understanding process capability
- Calculating process capability for continuous and attribute data
- Selecting appropriate capability metrics & indices
Session 6: Advanced Statistical Approaches
- Understanding probability distributions for variable and attribute data
- Dealing with non- normal data
- Capability analysis for non-normal data
- Statistical process control charts for non-normal data
- The central limit theorem
- Understanding and using data transformations
Session 7: Analyse Phase
- Verifying the root cause
- Taking a structured approach to data analysis
- Links to the cause and effect diagram
- 5 Why approach to problem solving
- Box plots & scatter diagrams
- Significance testing approach
- Tips for summarising and presenting the analysis
Session 8: Hypothesis Testing
- Use of inferential statistics
- Writing a hypothesis statement
- Setting a confidence level
- Understanding the P-Value
- Tests for variable and attribute data
- Power & sample size
- Non-parametric techniques
Session 9: Intro to DOE
- Overview of DOE techniques
- Optimisation challenge
- Applications of DOE techniques
Session 10: Optimising the Process
- Regression analysis
- Understanding correlation
- Introduction to simple linear regression
- Introduction to multiple regression
Session 11: Screening & Taguchi Methods
- Introduction to fractional factorial
- Screening designs
- Advanced techniques e.g. EVOP, RSM
- Taguchi loss function
- Taguchi designs that deliver robust solutions in the presence of noise
Session 12: Advanced DOE Techniques
- Mixed and multi-level designs
- Response surface designs
- Botched runs
- Randomisation and grey coding
- DOE with historical data
Session 13: Situational Leadership
- Transformational leadership
- Influencing change
- Concepts and models for change
- Persuasion campaigning
Session 14: Coaching Improvement Teams
- The key skills of coaching
- The coaching continuum expert to discovery
- Using the GROW model
Session 15: Implementing Control
- Key steps of the Control phase
- Confirming the improvement
- Developing a control plan
- Different types of process control
- The principle of mistake proofing
- Monitoring effectiveness
- Closing the improvement project
