Lean Six Sigma Green Belt: Analyze and Improve Course

Lesson 1 • Roles, Belts, and Project Governance

Defines Green Belt responsibilities versus Black Belt and Champion roles. Establishes accountability structures students will operate within on real projects.

Lesson 2 • Voice of the Customer and CTQ Translation

Translates customer needs into measurable Critical-to-Quality characteristics. Anchors all subsequent analysis to customer-defined performance standards.

Lesson 3 • DMAIC Roadmap Overview

Maps all five DMAIC phases and their deliverables. Clarifies how Analyze and Improve phases depend on Define and Measure outputs.

Lesson 1 • Measurement System Analysis

Evaluates gauge repeatability and reproducibility to confirm data reliability. Unreliable measurement systems invalidate all downstream analysis.

Lesson 2 • Baseline Process Capability

Calculates Cp, Cpk, Pp, Ppk, and sigma level from baseline data. These metrics define the improvement gap that Analyze and Improve phases must close.

Lesson 3 • Process Mapping Techniques

Introduces SIPOC, swimlane, and value-stream maps as complementary tools. Each map type reveals different process dimensions needed for thorough analysis.

Lesson 1 • Central Tendency and Dispersion Measures

Covers mean, median, mode, range, variance, and standard deviation. These measures quantify process location and spread for comparison and monitoring.

Lesson 2 • Graphical Tools for Continuous Data

Teaches histograms, box plots, and run charts for continuous measurements. Visual patterns reveal distribution shape, outliers, and time-based trends.

Lesson 3 • Multi-Vari and Stratification Analysis

Uses multi-vari charts to separate positional, cyclical, and temporal variation families. Stratification narrows the search space before formal hypothesis testing.

Lesson 1 • Hypothesis Test Framework

Establishes null and alternative hypotheses, alpha risk, beta risk, and p-values. A consistent framework prevents misinterpretation of test results.

Lesson 2 • Tests for Means and Medians

Applies one-sample t-test, two-sample t-test, and Mann-Whitney U test to compare location parameters. Correct test selection depends on normality and sample size.

Lesson 3 • Probability and Sampling Distributions

Covers probability rules, normal distribution properties, and the central limit theorem. These concepts underpin every hypothesis test used in the Analyze phase.

Lesson 4 • Tests for Variance and Proportions

Uses F-test, Levene's test, and proportion z-test to compare spread and defect rates. Variance tests are critical when variation reduction is the improvement goal.

Lesson 1 • Regression Diagnostics and Validation

Evaluates residual plots, leverage, and influence statistics to confirm model validity. Invalid models produce misleading predictions and flawed improvement decisions.

Lesson 2 • Multiple Linear Regression Fundamentals

Extends simple regression to multiple predictors, covering model building and coefficient interpretation. Multivariate models reflect real process complexity better than single-variable models.

Lesson 3 • Regression-Based Process Optimization

Uses regression equations to identify optimal input settings that achieve target output values. Connects statistical modeling directly to the Improve phase solution design.

Lesson 1 • Cause-and-Effect Diagrams

Constructs Ishikawa fishbone diagrams using the 6M framework to brainstorm potential causes. Systematic categorization prevents overlooking major cause families.

Lesson 2 • Failure Mode and Effects Analysis

Scores failure modes by severity, occurrence, and detection to calculate Risk Priority Numbers. FMEA links root-cause identification directly to improvement prioritization.

Lesson 3 • Five Whys and Fault Tree Analysis

Drills from symptom to root cause using iterative questioning and Boolean logic trees. Both tools complement fishbone diagrams by deepening causal chains.

Lesson 4 • Waste and Value-Stream Root Cause Analysis

Applies the eight Lean wastes and value-stream analysis to identify non-value-added activities as root causes. Bridges qualitative Lean observation with quantitative Six Sigma verification.

Lesson 1 • DOE Results Interpretation and Action

Translates significant effects and optimal settings into actionable process changes. Bridges experimental findings to the solution implementation steps of the Improve phase.

Lesson 2 • Fractional Factorial Designs

Reduces run count by confounding higher-order interactions in fractional factorial designs. Enables screening of many factors efficiently when resources are limited.

Lesson 3 • Response Surface Methodology

Uses central composite and Box-Behnken designs to model curvature and locate process optima. RSM refines factor settings after screening experiments identify key variables.

Lesson 4 • DOE Fundamentals and Terminology

Defines factors, levels, responses, runs, and replication in experimental design. Correct terminology and planning prevent costly experimental errors.

Lesson 1 • Solution Selection and Prioritization

Uses impact-effort matrices, Pugh concept selection, and weighted scoring to choose solutions. Rigorous selection prevents investing resources in low-impact or high-risk changes.

Lesson 2 • Lean Improvement Tools

Applies 5S, standard work, visual management, and mistake-proofing to eliminate waste and error. Lean tools deliver rapid, low-cost improvements that complement statistical solutions.

Lesson 3 • Solution Generation Techniques

Applies brainstorming, TRIZ, and benchmarking to generate a broad solution set. Divergent thinking before convergent selection increases the probability of breakthrough solutions.

Lesson 4 • Pilot Planning and Execution

Designs a controlled pilot to validate solution effectiveness before full deployment. Pilot data confirms predicted gains and reveals implementation risks at low cost.