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.
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.
Black Belt Program Overview
Week 1: July 15-19, 2019
- DMAIC Problem Solving Process and DEFINE Phase
- Sampling, Descriptive Statistics, and Basic Graphical Tools (Run Chart, Histogram, Box Plot)
- Introduction to Minitab (Tutorial)
- 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)
- 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
- 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
- Minitab Tutorial – Hypothesis Testing
Week 2: August 19-23, 2019
- 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
Program Faculty
- Patrick Hammett
- Director of Academic Programs and Learning Systems, Integrative Systems + Design
- Lead Faculty, Six Sigma Programs
- Lecturer, Integrative Systems + Design
- More about...
Certification
Participants pursuing their University of Michigan Lean Six Sigma Black Belt Professional Certification are required to meet the following:
- 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 Black Belt Project (reviewed by U-M faculty)
Prerequisites
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
Software Requirements
The assignment exercises and case studies involve the extensive use of Minitab 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 fine though some menus may appear different than those shown in lecture.
