Booking options
£4,750
+ VAT£4,750
+ VATDelivered Online
Two days
All levels
This course is specifically for people wanting to become Lean Six Sigma Black Belts, who are already Lean Six Sigma practitioners.
If advanced statistical analysis is needed to identify root causes and optimal process improvements, (Lean) Six Sigma Green Belts typically ask Black Belts or Master Black Belts to conduct these analyses. This course will change that. Green Belts wanting to advance their statistical abilities will have a considerable amount of hands-on practice in techniques such as Statistical Process Control, MSA, Hypothesis Testing, Correlation and Regression, Design of Experiments, and many others.
Participants will also work throughout the course on a real-world improvement project from their own business environment. This provides participants with hands-on learning and provides the organization with an immediate ROI once the project is completed. IIL instructors will provide free project coaching throughout the course.
At the end of this program, you will be able to:
Use Minitab for advanced data analysis
Develop appropriate sampling strategies
Analyze differences between samples using Hypothesis Tests
Apply Statistical Process Control to differentiate common cause and special cause variation
Explain and apply various process capability metrics
Conduct Measurement System Analysis and Gage R&R studies for both discrete and continuous data
Conduct and analyze simple and multiple regression analysis
Plan, execute, and analyze designed experiments
Drive sustainable change efforts through leadership, change management, and stakeholder management
Successfully incorporate advanced analysis techniques while moving projects through the DMAIC steps
Explain the main concepts of Design for Six Sigma including QFD
IIL Black Belt Certification Requirements
Review Project Selection
Review Define
Review Measure
Review Analyze
Review Improve
Review Control
Introduction to Minitab
Minitab basic statistics and graphs
Special features
Overview of Minitab menus
The Central Limit Theorem
Confidence Interval of the mean
Sample size for continuous data (mean)
Confidence Interval for proportions
Sample size for discrete data (proportions)
Sampling strategies (review)
Appendix: CI and sample size for confidence levels other than 95%
Why use advanced stat tools?
What are hypothesis tests?
The seven steps of hypothesis tests
P value errors and hypothesis tests
1 factor ANOVA and ANOM
Main Effect Plots, Interaction Plots, and Multi-Vari Charts
2 factor ANOVA and ANOM
Testing for equal variance
Testing for normality
Choosing the right hypothesis test
Chi-square test for 1 factor
ANOM test for 1 factor
Chi-square test for 2 factors
Exercise hypothesis tests - shipping
Non-parametric tests
Review of Common Cause and Special Cause Variation
Review of the Individuals Control Charts
How to calculate Control Limits
Four additional tests for Special Causes
Control Limits after Process Change
Discrete Data Control Charts
Control Charts for Discrete Proportion Data
Control Charts for Discrete Count Data
Control Charts for High Volume Processes with Continuous Data
Test for normal distribution
Box-Cox Transformation
Box-Cox Transformation for Individuals Control Charts
Introduction to Time Series Analysis
Decomposition
Smoothing: Moving Average
Smoothing: EWMA
Process capability
Discrete Data: Defect metrics
Discrete Data: Yield metrics
Process Capability for Continuous Data: Sigma Value
Short- and long-term capabilities
Cp, Cpk, Pp, Ppk capability indices
What is Measurement System Analysis?
What defines a good measurement system?
Gage R&R Studies
Attribute / Discrete Gage R&R
Continuous Gage R&R
Correlation Coefficient
Simple linear regression
Checking the fit of the Regression Model
Leverage and influence analysis
Correlation and regression pitfalls
Introduction to Multiple Regression
Multicollinearity
Multiple Regression vs. Simple Linear Regression
Introduction
Creating indicator variables
Method 1: Going straight to the intercepts
Method 2: Testing for differences in intercepts
Introduction to Logistic Regression
Logistic Regression - Adding a Discrete X
Design of Experiment
OFAT experimentation
Full factorial design
Fractional factorial design
DOE road map, hints, and suggestions
Creating 2k Full Factorial designs in Minitab
Randomization
Replicates and repetitions
Analysis of results: Factorial plots
Analysis of results: Factorial design
Analysis of results: Fits and Residuals
Analysis of results: Response Optimizer
Analysis of results: Review
Designs with no replication
Fractional factorial designs
Screening Design of Experiment Case Study Repair Time
Blocking
Organizational change management
Assuring project sponsorship
Emphasizing shared need for change
Mobilizing stakeholder commitment
Introduction to project management
Project management for Lean Six Sigma
The project baseline plan
Work Breakdown Structure (WBS)
Resource planning
Project budget
Project risk
Project schedule
Project executing
Project monitoring and controlling and Closing
Introduction to Design for Lean Six Sigma (DMADV)
Introduction to Quality Function Deployment (QFD)
IIL's Lean Six Sigma Black Belt Certification Program also prepares you to pass the IASSC Certified Black Belt Exam (optional)