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.