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Building on the concepts from the first course in the Six Sigma Program, Define and Measure, in this course, you will learn how to statistically analyze data with the Six Sigma methodology using inferential statistical techniques to determine confidence intervals and to test hypotheses based on sample data. You will also review cause and effect techniques for root cause analysis.
You will learn how to perform correlation and regression analyses in order to confirm the root cause and understand how to improve your process and plan designed experiments.
You will learn how to implement statistical process control using control charts and quality management tools, including the 8 Disciplines and the 5 Whys to reduce risk and manage process deviations.
To complement the lectures, learners are provided with interactive exercises, which allow learners to see the statistics "in action." Learners then master statistical concepts by completing practice problems. These are then reinforced using interactive case studies, which illustrate the application of the statistics in quality improvement situations.
Upon successful completion of this program, learners will earn the TUM Lean and Six Sigma Yellow Belt certification, confirming mastery of Lean Six Sigma fundamentals to a Green Belt level. The material is based on the American Society for Quality (www.asq.org) Body of Knowledge up to a Green Belt Level. The Professional Certificate is designed as preparation for a Lean Six Sigma Green Belt exam.
- To identify process problems and perform a root cause analysis using cause and effect diagrams and regression analysis.
- To analyze data using inferential statistical techniques, including confidence intervals and hypothesis testing.
- To test and quantitatively assess the impact of different improvement options using the design of an experiment.
- To test for the significance of effects using an Analysis of Variance.
- To implement control mechanisms for long-term monitoring using control charts for both quantitative and qualitative measurements.
- To apply the Six Sigma methodology for the Analyze, Improve and Control phases in your work or research.
Prerequisite
This course reviews basic statistics used in Six Sigma and quality management, applying them to quality problems and methods. Therefore, basic math skills are necessary.
Syllabus
Week 1: ANALYZE - Root Cause Analysis
Introduction to methods for root cause analysis, including Cause and Effect (Fishbone diagrams) and Pareto Charts. We learn how to perform statistical correlations and regression analyses.
Week 2: ANALYZE - Inferential Statistics
Learn the inferential statistics techniques of confidence intervals and hypothesis testing in order to use sample data and draw conclusions about or process centering.
Week 3: IMPROVE - Design of Experiments
Plan designed experiments and calculate the main and interaction effects.
Week 4: MEASURE - Analysis of Variance
Review how to perform a one-way Analysis of Variance (ANOVA) for comparing the between-factor variation to the within-factor variation for a single factor experiment.
Use a two-way ANOVA for testing the significance of the factor effects for a 2x2 DOE.
Week 5: CONTROL - SPC and Control Charts
Implement Statistical Process Control (SPC) & Control Chart Theory for monitoring process data and distinguishing between common cause variation and assignable cause variation. Construct X-bar and R Charts by calculating the upper and lower control limits and the centerline.
Week 6: CONTROL - Other Control Charts
Understand other control charts, including p-and c-charts and I/MR, and EWMA Charts and review of the Control and Response Plan for Six Sigma projects.
Week 7: Quality Tools: FMEA, 8D, 5 Whys
Use several important tools used in quality management, including the 8 Disciplines (8D) and 5 Whys, and learn the concept behind Design for Six Sigma (DFSS).
Week 8: Six Sigma Scenario and Course Summary
Step through a full Six Sigma scenario, covering all phases of the DMAIC process improvement cycle.
Instructors
Martin Grunow
Professor of Production and Supply Chain Management
Technical University of Munich
Holly Ott
Senior Lecturer in Operations Managementat Technical University of Munich and Professor Production Management at Rosenheim University
Technical University of Munich
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