Six Sigma: Analyse, Improve, Control
Learn how to analyse 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 Failure Modes and Effects Analysis 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 the 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 Technical University of Munich Lean Six Sigma Yellow Belt Certification, confirming mastery of the fundamentals of Lean Six Sigma to a Yellow Belt level, based on the American Society of Quality’s Body of Knowledge for the Certified Six Sigma Yellow Belt.
Week 1: ANALZYE – Inferential Statistics
Review of the Six Sigma Methodology and the DMAIC process improvement cycle and 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 2: ANALYZE – Regression and Correlation
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 3: IMPROVE – Design of Experiments
We plan designed experiments and calculate the main and interaction effects.
Week 4: MEASURE – Analysis of Variance
Test the significance of experimental results using an analysis of variance, for both measurement and attribute data.
Week 5: CONTROL – SPC and Control Charts
Cover Statistical Process Control & Control Chart Theory, including X-bar and R Charts. and
Week 6: CONTROL – Control Charts Introduction
Other control charts, including p-and C- charts and I/MR, and EWMA Charts, are introduced, and review of the Control and Reponse Plan for Six Sigma projects.
Week 7: Quality Tools: FMEA, 8D, 5 Whys
Introduce several important tools used in quality management, including Failure Modes and Effects Analysis, 8 Disciplines and 5 Whys.
Week 8: Course Summary and Review.
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