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About the course
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
- DMAIC Problem Solving Process and DEFINE Phase
- Sampling, Descriptive Statistics, and Basic Graphical Tools (Run Chart, Histogram, Box Plot)
- Introduction to Minitab (Tutorial)
Tuesday: Process and Value Stream Mapping Analysis
- Process Maps (Review of SIPOC/Swim Lane, Current and Future State Maps)
- Value Stream Mapping (VSM) Analysis (Value Stream Process Redesign, Current State VSM, Value Add Timeline, Future State VSM)
- Value Stream Productivity Analysis (Takt, Nominal vs. Effective Process Time, Detractors, Operator Bar Charts, Capacity and Utilization)
Wednesday: Measuring the Current State
- MEASURE: Measure the Current State – Continuous Outputs (Yield, PPM Defective, Mean vs. Variation)
- Measure Current State – Defect Count Data (DPMO, Rolled Yield, Tabulation, Check Sheets, and Pareto)
- Minitab Tutorial – Measure Phase
- Measuring Current State Using Survey Methods
Thursday: Statistical Process Control and Process Capability Analysis
- Assessing Process Stability: Variable Control Charts (X-Bar/Range, I/MR)
- Statistical Process Control: Attribute Charts (e.g., p-chart, u-chart)
- Minitab Tutorial – SPC
- Process Capability Analysis (Cp and Cpk) – Mean vs. Variation, Normal/Non-Normal Distributions
- Minitab Tutorial – Process Capability Analysis
Friday: Data Collection and Hypothesis Testing
- Data Collection and Qualitative Process Analysis (Data Collection, Cause and Effect, P-Diagram)
- Two Group Hypothesis Tests (F-tests, t-tests, 2 Proportion, ANOVA)
- One-Factor ANOVA – Operating Windows
- Power and Sample Size Planning
- Minitab Tutorial – Hypothesis Testing
Monday: Improve and Control
- IMPROVE Phase – Countermeasures and Short Term Verification
- IMPROVE Phase – Standardized Work and Load Leveling
- CONTROL – Methods of Control, Visual Controls, and Control Plans
- Failure Mode and Effects Analysis (FMEA) – Improving Methods of Control (Detection)
Tuesday: Categorical Data Analysis and Transactional Measurement Systems Analysis
- Nonparametric Hypothesis Tests
- Categorical Data Analysis (Measures of Association)
- Minitab Tutorial – Categorical Data Analysis
- Transactional Measurement Systems Analysis (MSA) (Sources of Measurement Error, Accuracy and Repeated Measurement Studies)
- Attribute Agreement Analysis
- Minitab Tutorial – Transactional MSA
Wednesday: Regression Analysis
- Two Variable Analysis – Simple Linear Regression/Correlation
- Multiple Regression/Stepwise Regression/Best Subset
- Binary Logistic Regression Analysis
- Minitab Tutorial – Regression Analysis
Thursday: Design of Experiments and General Linear Model
- Multi-Vari Studies
- Principles of Design of Experiments (DOE)
- DOE – 2k Factorial
- Minitab Tutorial – DOE
- General Linear Model (GLM)
- Minitab Tutorial – GLM
Friday: Project Selection and DMAIC Gate Review Process
- Tolerance Analysis and Adjustment
- Project Identification and Selection Techniques
- DMAIC Project Management
- Course Summary 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.
Trust the experts
Dr. Patrick Hammett is the Lead Faculty for the University of Michigan College of Engineering's Six Sigma Programs and teaches related Quality and Statistical Analysis Method courses as a Lecturer for the Integrative Systems +Design Department. As lead instructor for live and online Six Sigma tra...
Dr. Guzman is lead faculty in the University of Michigan College of Engineering in Industrial and Operations Engineering (IOE) and the Division of Integrative Systems + Design (ISD), where he has taught several courses since 2003. His teaching and research is focused on the application of data a...
Donald P. Lynch, Ph.D. received his B.S. in Mechanical Engineering from Michigan Technological University, MBA from Eastern Michigan University, Ph.D. in Mechanical (Industrial) Engineering from Colorado State University, and a post Graduate Certificate in Lean Six Sigma from the University of Mi...
Nicole has 10 years engineering and lean six sigma experience in the defense, aerospace, automotive, and financial industries. She received her Bachelor of Science in Industrial and Operations Engineering from the University of Michigan and earned her MBA from Drexel University with a concentrati...