Learn how to statistically analyse process data to determine the root cause for process problems, to propose solutions, and to implement quality management tools, such as 8D and the 5 Whys, as well as the concept of Design for Six Sigma (DFSS).
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.
In Six Sigma: Analyze, Improve, Control edX course, 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.
What you will learn
- To identify process problems and perform a root cause analysis using cause and effect diagrams and then performing a regression analysis.
- To analyse data using inferential statistical techniques, including confidence intervals and hypothesis testing.
- To test and quantitatively assess the impact of different improvement options using a design of experiment.
- To test for the significance of effects using an analysis of variacne.
- 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 Analyse, Improve and Control phases in your work or research.
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 one-way Analysis of Variance (ANOVA) for comparing the between-factor variaion to the within-factor variaion for a single factor experiment.
Use a two-way ANOVA for testing the significance of the factor effects for a 2×2 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 center line.
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 Reponse 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.
- Martin Grunow
- Holly Ott