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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.
About the course
Earn Your Black Belt Online from the University of Michigan
Effective quality analysis requires finding the right tool for the right problem. Using a blend of case studies and examples drawn from transactional and healthcare processes, this online course provides advanced continuous improvement and quality engineering analysis skills used in Lean Six Sigma problem solving.
Using examples and case studies, this course focuses on applications primarily drawn from office and healthcare processes. Project results include reduced internal processing time, improved customer/patient satisfaction scores, reduced service 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.
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/Swimlane; 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 Analysis (Cp and Cpk) – Mean vs. Variation; 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
- Transactional Measurement Systems Analysis (MSA) (Sources of Measurement Error, Accuracy and Repeated Measurement Studies)
- Attribute Agreement Analysis
- Minitab Tutorial – Transactional 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)
- DOE – 2k Factorial
- Minitab Tutorial – DOE
- 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
Administrative/Online Technical Support
Support staff are available via phone and email to help with administrative and technical issues during our normal business hours (Monday through Friday 8:00 a.m. to 5:00 p.m. Eastern Time).
Content Questions/Certification Project Support
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.
TIME COMMITMENT AND WORK PACE
Estimated: 120 self-paced hours
- 90 hours (approximately) for lecture recordings and exercises
- 20-40 hours for project work
All requirements must be completed within 365 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 46 lecture modules with 10 test exercises (multiple choice tests to complete after each learning module) and 2 case study assignments. Most lecture recordings are approximately one hour in length. While the course is self-paced, we recommend completing two sessions/week.
Lean Six Sigma DMAIC analysis may be applied to a vast array of process improvement opportunities. Participants are expected to complete a project to practice and apply course concepts.
Participants pursuing their University of Michigan Lean Six Sigma Black Belt Certification are required to:
- Complete all required online lecture modules
- Complete all testing exercises and case studies with an overall cumulative score > 80%
- Obtain an 80% or above on Black Belt Certification Exam
- Obtain approval of Black Belt Project Proposal by U-M faculty
- Successfully complete Black Belt Project (reviewed by U-M faculty)
Upon successful completion, you will be mailed your 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
The assignment exercises and case studies involve the extensive use of Minitab (not Minitab Express) Statistical Software for analysis. Lectures and assignments are developed for Minitab 17 or higher or equivalent software (SPSS, STAT SOFT, SAS).
Minitab 13-16 also are acceptable, though some menus may appear different than those shown in lecture.
Install VMWare Fusion for Mac. Download: https://caenfaq.engin.umich.edu/vmap/what-is-vmap
Create a Windows Virtual machine using VMWare Fusion.
Install Minitab (Full version) inside the newly created Virtual Windows machine.
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...
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...
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...
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...
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