SIX SIGMA CERTIFICATIONS
Lean Six Sigma Black Belt - Blended
Time Commitment2 weeks, in-person8:00 am - 4:30 pm
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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. Using a blend of case studies drawn from different industries, this course provides advanced continuous improvement and quality engineering analysis skills used in Lean Six Sigma problem solving. Our blended delivery format combines in-person class time with additional work outside of class.
This course focuses on applications drawn from a variety of industries, including manufacturing, transactional/service, and healthcare environments.
- 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 Black Belt skill level
- Interpret test results and draw conclusions based on data
- Develop recommendations and control plans to improve processes
- Demonstrate understanding of the DMAIC methodology through the completion of a process improvement project outside of class
This course will be delivered in a blended format, including modules to complete at your own pace as well as in-person class time in Ann Arbor. Designed to reinforce learning, these classroom sessions offer the opportunity engage with U-M faculty, ask questions, and network with industry professionals.
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)
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)
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
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
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
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)
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
- Two Variable Analysis – Simple Linear Regression/Correlation
- Multiple Regression/Stepwise Regression/Best Subset
- Binary Logistic Regression Analysis
- Minitab Tutorial – Regression Analysis
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
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
Estimated: 120 self-paced hours, plus eight 2-hour remote live check-ins:
- 90 hours (approximately) for lecture recordings and exercises
- Eight 2-hour remote live check-in sessions (April – August)
- 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:
- Participate in all course training days and successfully complete all in-class and online exercises and case studies
- Complete all testing exercises and case studies and obtain 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 a 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.
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.
To view technical requirements, click here.
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.
ONLINE COURSE ACCESS
You will receive access to both the online Green Belt and Black Belt courses to review for project work and exam preparation. Supplemental modules are available for topics including:
- Measurement Systems Analysis: Gage R&R Study
- Introduction to Sample Size Planning (Single Statistics, Margin of Error, CV)
- Complex Regression and Data Transforms
- DOE Fractional Factorial Designs, 3k Factorial, 2k w/ Center Points
- Pugh Concept Selection Process
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 or equivalent software (SPSS, STAT SOFT, SAS).
If your organization does not offer Minitab please email email@example.com and Nexus will help provide access for this course.
Pat Hammett, PhD
- Lead Faculty, Six Sigma Programs
- Associate Chair of Masters Education, Integrative Systems + Design
- Lecturer, College of Engineering
Don Lynch, PhD
- Instructor, Nexus
- Vice President of Quality, Strategy, and Transformation, SKF USA Inc.
- Instructor, Nexus
Luis Guzman, PhD
- Instructor, Nexus
- Lecturer, Industrial & Operations Engineering
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