Comprehensive course analysis
Who should attend
PREREQUISITES & TECHNICAL REQUIREMENTS
A basic understanding of statistical analysis methods is recommended, including:
- Basic Microsoft Excel Skills
- Basic Statistics
About the course
Earn Your Green Belt Online from the University of Michigan
Learn how to effectively solve problems by integrating Lean and Six Sigma within the DMAIC approach. Using case studies and examples primarily drawn from manufacturing companies, this online course will set you up with a structure to systematically frame problems, collect data productively, and implement sustainable solutions.
This course focuses on applications primarily drawn from manufacturing companies. Project results include increased throughput, improved equipment utilization, reduced maintenance costs, and more.
The following modules are required, and you will also receive access to optional supplemental material.
- Continuous Improvement Overview – Integrating Lean and Six Sigma, Classic Forms of Waste, Kaizen
- DMAIC Problem Solving Process
- DEFINE: Requirements Flow Down, Linking Customers to Business Performance Metrics (VOC/VOB), Project Charters
- Process Maps: SIPOC, Swimlane, Process Mapping Diagram
- MEASURE: Exploring Data Patterns & Distributions (Run Charts, Histograms, Box Plots), Outliers (Supplement: Course Software Tutorial)
- Descriptive Statistics (Sampling, Mean, Median, Variation)
- Measuring Current State Capability (Yield, PPM Defective, DPMO)
- Rolled Yield Analysis (Types of Yield, Rolled Yield, Normalized Yield)
- Process Stability - Overview of Statistical Process Control (Variable and Attribute Control Charts)
- Measurement Systems Analysis (MSA)
- Value Stream Mapping – Part 1 (Current State Map, Value Add Timeline)
- Value Stream Mapping – Part 2 (Value Stream Productivity Analysis, Effective Process Time, Lean Levers: Pitch Interval, Volume/Mix Leveling, and Future State Maps)
- ANALYZE: Qualitative Analysis (Affinity, P-Diagram, Cause-Effect Diagram, 5 Whys)
- Stratification Analysis (Grouping Variables, Multiple Box Plots)
- Check Sheets and Pareto Analysis
- Two Group Hypothesis Tests (F-tests, t-tests, 2 proportion tests)
- Two-Variable Analysis: Scatter Plot/Linear Regression/Correlation
- Hypothesis Test: One Factor ANOVA
- IMPROVE: 5S Process, Standardized Work, Training, Error Proofing, Visual Aids, Process Monitoring
- Flow Improvements (Push vs. Pull Systems, Little's Law, Batch Size Reduction, Layout Improvement)
- Failure Mode and Effects (FMEA) Analysis
- CONTROL: Methods of Control
- Project Selection and Scoping
- Applying the Six Sigma Methodology and Course Summary
TIME COMMITMENT AND WORK PACE Estimated: 60 self-paced hours
- 40 hours (approximately) for lecture recordings and exercises
- 10-30 hours for project work
All requirements must be completed within 180 days after your start date. If you do not complete the course within one year of your start date, you will be required to re-enroll at a reduced cost of $500.
This is a self-paced online course consisting of 24 lecture modules with 22 exercises (multiple choice tests to complete after each learning module). Most lecture recordings are approximately one hour in length. While the course is self-paced, we recommend completing two sessions/week.
Participants pursuing their University of Michigan Lean Six Sigma Green Belt Certification are required to:
- Complete all required online lecture modules
- Complete all testing exercises with an overall cumulative score > 80%
- Successfully complete Green Belt Project (reviewed by U-M faculty)
Upon successful completion, you will be awarded your University of Michigan Lean Six Sigma Green Belt Certification.
Following the live course, candidates are welcome to contact the course instructors for content questions and project support. The instructors will provide support via e-mail, phone consultation, and/or online videoconferencing.
- Understand 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 Green Belt level
- Interpret test results and draw conclusions based on data
- 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
U-M's Green Belt Certification courses include a copy of QE Tools statistical analysis software. QE Tools is a user-friendly Excel add-in tool designed for Lean Six Sigma Green Belts. Students will use QE Tools to apply the various problem solving and statistical analysis methods both within the course and for their Lean Six Sigma project.
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...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.