Six Sigma: Analyze, Improve, Control

This module introduces essential methods for identifying and analyzing the root causes of process problems. Learners will explore Cause and Effect diagrams (Fishbone diagrams) and Pareto Charts to systematically identify contributing factors. The module covers statistical correlation techniques and regression analyses to establish relationships between variables and pinpoint underlying causes of quality issues.

This module focuses on inferential statistical techniques essential for Six Sigma analysis. Learners will master confidence intervals and hypothesis testing methods to draw valid conclusions about process performance from sample data. The module emphasizes practical application of these techniques to evaluate process centering and make data-driven decisions about process improvement.

This module teaches learners how to plan and execute designed experiments (DOE) to test process improvements systematically. Learners will understand how to identify factors, calculate main effects, and analyze interaction effects between variables. The module provides practical skills for optimizing processes through controlled experimentation and quantitative analysis.

This module covers Analysis of Variance (ANOVA) techniques for evaluating experimental results. Learners will perform one-way ANOVA to compare between-factor and within-factor variation for single factor experiments. The module also addresses two-way ANOVA for testing the significance of factor effects in 2x2 designed experiments, enabling rigorous statistical validation of process improvements.

This module covers critical quality management tools used in industry for problem-solving and risk management. Learners will master the 8 Disciplines (8D) methodology for systematic problem resolution and the 5 Whys technique for root cause analysis. The module also introduces the concept of Design for Six Sigma (DFSS), which focuses on incorporating quality principles into product and process design from the beginning.

This module provides a comprehensive integration of all course concepts through a complete Six Sigma scenario. Learners will step through all phases of the DMAIC (Define, Measure, Analyze, Improve, Control) process improvement cycle, applying the tools and techniques learned throughout the course. This capstone module reinforces understanding and demonstrates the practical application of Six Sigma methodology in real-world quality improvement situations.

This module introduces Statistical Process Control (SPC) and Control Chart Theory for ongoing process monitoring. Learners will distinguish between common cause variation and assignable cause variation in process data. The module covers construction of X-bar and R Charts, including calculation of upper and lower control limits and centerlines, providing essential tools for maintaining process stability over time.

This module expands learners' understanding of various control chart types beyond basic X-bar and R charts. Learners will work with p-charts and c-charts for attribute data, I/MR charts for individual measurements, and EWMA (Exponentially Weighted Moving Average) charts for detecting small process shifts. The module also reviews the Control and Response Plan framework for Six Sigma projects.