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About the course
Earn Your Black Belt Online from the University of Michigan
Develop advanced continuous improvement and quality engineering analysis skills used in Lean Six Sigma problem solving.
This course will provide you with the necessary skills to execute Lean Six Sigma techniques and strategies at the Black Belt level. Effective quality analysis often requires finding the right tool for the right problem, and this course examines many Lean Six Sigma analytical and problem solving techniques from descriptive statistics to advanced design of experiments.
After completing the program—including a series of case studies, a certification exam, and an improvement project at your organization—you'll earn a University of Michigan Lean Six Sigma Black Belt certification.
Using examples and case studies, this course focuses on applications primarily drawn from manufacturing companies. Project results include increased throughput, improved equipment utilization, reduced maintenance costs, and more.
Demonstrate your ability to effectively apply Lean Six Sigma techniques to solve actual problems that affect performance in quality, lead time, and cost with a University of Michigan Lean Six Sigma Black Belt certification.
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
Time Commitment and Work Pace
Estimated: 120 self-paced hours
- 90 hours (approximately) for lecture recordings and exercises
- 20-40 hours for project work
The following modules are required, and you will also receive access to optional supplemental material.
- Course Overview (A) and Six Sigma Overview (B)
- DMAIC Problem Solving Process and DEFINE Phase
- 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)
- Sampling, Graphical Analysis Tools, and Descriptive Statistics (Normality, Hypothesis Tests)
- Introduction to Minitab (Tutorial)
- 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
- 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 Indices and Analysis – Cp/Cpk, Pp/Ppk; PPM, Normal/Non-Normal Distributions
- Sigma Level and Six Sigma (Supplemental)
- Minitab Tutorial – Process Capability Analysis
- 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 (Optional)
- Minitab Tutorial – Hypothesis Testing
- 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)
- Nonparametric Hypothesis Tests
- Categorical Data Analysis (Measures of Association)
- Minitab Tutorial – Categorical Data Analysis
- Measurement Systems Analysis (MSA) – Part I (Accuracy and Gage R&R Studies)
- Measurement Systems Analysis (MSA) – Part II (Repeated Measurement and Attribute Agreement)
- Minitab Tutorial – MSA
- Two Variable Analysis – Simple Linear Regression/Correlation
- Multiple Regression/Stepwise Regression/Best Subset
- Binary Logistic Regression Analysis
- Minitab Tutorial – Regression Analysis
- Multi-Vari Studies
- Principles of Design of Experiments (DOE)
- Part A: DOE – 2k Factorial and
- Part B: DOE Fractional Factorial, 3k and 2k Center Point
- Minitab Tutorial – DOE
- Mixed Level DOE and General Linear Model (GLM)
- Minitab Tutorial – GLM
- Tolerance Analysis and Adjustment
- Project Identification and Selection Techniques
- DMAIC Project Management
- Course Summary and DMAIC Gate Review Process
- Certification Exam Review
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...