Lean six Sigma Black Belt
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Earn Your Black Belt From the University of Michigan
Effective quality analysis requires finding the right tool for the right problem.
The purpose of this two-week course is to develop advanced continuous improvement and quality engineering analysis skills used in Lean-Six Sigma problem solving, equipping candidates to be able to identify and lead improvement projects at the Black Belt level.
Extensive case studies are used to demonstrate and practice their application so that candidates are prepared to effectively identify and sustainably solve problems that affect performance in quality, lead time, and cost.
Upon completion of the course, participants are expected to demonstrate their understanding of key course concepts through passing a Black Belt Certification Exam and successful completion of an industry project.
- Understand and characterize variability through the graphical representation of data
- Describe a process visually through process mapping techniques
- Apply DMAIC problem solving process toward process improvement at the Black Belt skill level
- Develop data collection plans and design experiments to test hypotheses
- Interpret test results and draw conclusions based on data and the application of advanced statistical analysis techniques
- Integrate statistical analysis tools, software, and problem solving methodologies
- Develop recommendations and control plans to improve processes
- Complete a process improvement project outside of class that demonstrates the application of the full DMAIC methodology
Black Belt Program Overview
- Monday: Six Sigma Overview and Define Phase
- Tuesday: Process and Value Stream Mapping Analysis
- Wednesday: Measuring the Current State
- Thursday: Statistical Process Control and Process Capability Analysis
- Friday: Data Collection and Hypothesis Testing
- Monday: Improve and Control
- Tuesday: Categorical Data Analysis and Transactional Measurement Systems Analysis
- Wednesday: Regression Analysis
- Thursday: Design of Experiments and General Linear Model
- Friday: Project Selection and DMAIC Gate Review Process
Who should attend
Participants are expected to have knowledge in statistical concepts and linear statistical models along with their application to data analysis. Recommended prerequisite topics include:
- Descriptive statistics
- Sampling and distributions (e.g., Normal)
- Simple linear regression and correlation
- Hypothesis testing
Successful completion of an undergraduate Statistics and/or Linear Statistical Models course is desired. Completion of Green Belt certification is desired but not required, especially if candidates have background in the above prerequisite topics.