Green Belt Course

Lesson 1 • Core Philosophy and Key Concepts

Defines variation, defects, and process capability as central concerns. Links these concepts to customer satisfaction and financial performance.

Lesson 2 • The DMAIC Roadmap Overview

Introduces the five-phase DMAIC structure as the governing methodology. Shows how each phase produces specific deliverables that feed the next.

Lesson 3 • Project Selection and Business Alignment

Explains criteria for choosing high-impact improvement projects. Connects project selection to strategic goals and resource constraints.

Lesson 4 • History and Evolution of Six Sigma

Traces Six Sigma from manufacturing origins to cross-industry adoption. Provides context for why structured problem-solving outperforms intuition-based improvement.

Lesson 1 • Crafting the Project Charter

Covers all charter elements: problem statement, goal, scope, team, and timeline. A well-formed charter prevents scope creep and aligns stakeholders from the start.

Lesson 2 • Voice of the Customer Methods

Teaches data-collection techniques to capture customer needs accurately. Converts raw feedback into measurable Critical-to-Quality requirements.

Lesson 3 • Stakeholder Analysis and Communication

Identifies stakeholders by influence and interest to plan targeted engagement. Ensures project momentum through proactive communication strategies.

Lesson 1 • Baseline Performance Metrics

Calculates process capability indices and sigma levels from collected data. Establishes the quantified gap between current and target performance.

Lesson 2 • Descriptive Statistics and Data Visualization

Applies summary statistics and graphical tools to reveal data patterns. Builds the analytical vocabulary needed for the Analyze phase.

Lesson 3 • Measurement System Analysis

Evaluates whether the measurement system itself introduces unacceptable variation. Gage R&R studies confirm data trustworthiness before analysis begins.

Lesson 4 • Data Collection Planning

Structures a rigorous plan specifying what, where, when, and how to collect data. Prevents sampling bias and ensures sufficient statistical power.

Lesson 1 • Cause-and-Effect Analysis

Structures brainstorming with fishbone diagrams and the 5 Whys technique. Moves teams from symptom description to testable causal hypotheses.

Lesson 2 • Hypothesis Testing Fundamentals

Introduces null and alternative hypotheses, p-values, and Type I/II errors. Provides the statistical logic for confirming or rejecting proposed root causes.

Lesson 3 • Regression and Correlation Analysis

Quantifies relationships between input variables and process outputs. Simple and multiple regression models support root-cause verification.

Lesson 4 • Comparing Groups and Distributions

Applies t-tests, ANOVA, and chi-square tests to detect meaningful differences. Selects the correct test based on data type and group structure.

Lesson 1 • Lean Improvement Tools

Applies 5S, standard work, error-proofing, and flow principles to eliminate waste. Lean tools complement statistical solutions with physical process redesign.

Lesson 2 • Design of Experiments Principles

Introduces factorial experiments to test multiple factors simultaneously and efficiently. DOE reveals interaction effects that one-factor-at-a-time testing misses.

Lesson 3 • Piloting and Validating Solutions

Designs a controlled pilot to test solutions before full deployment. Pilot data confirms improvement magnitude and surfaces implementation risks.

Lesson 4 • Solution Generation Techniques

Applies brainstorming, benchmarking, and TRIZ-inspired thinking to generate solution options. Divergent ideation precedes structured evaluation to avoid premature narrowing.

Lesson 1 • Multi-Vari and Variance Components

Decomposes total process variation into positional, cyclical, and temporal components. Identifies the dominant variation family to focus improvement efforts.

Lesson 2 • Logistic Regression for Binary Outcomes

Models the probability of a binary outcome as a function of continuous or categorical inputs. Extends regression capability to pass/fail and yes/no response data.

Lesson 3 • Capability Analysis for Non-Normal Data

Addresses capability measurement when data violates normality assumptions. Transformation and non-parametric methods yield valid capability indices.

Lesson 1 • Standard Operating Procedures

Translates improved process steps into clear, auditable SOPs. SOPs ensure consistent execution regardless of personnel changes.

Lesson 2 • Training and Knowledge Transfer

Plans and delivers training to equip process operators with new methods. Effective transfer prevents regression caused by knowledge gaps.

Lesson 3 • Statistical Process Control Charts

Applies control charts to monitor process behavior and detect special-cause variation. Correct chart selection depends on data type and subgroup structure.

Lesson 4 • Project Closure and Benefits Realization

Formalizes project completion with financial validation and lessons-learned documentation. Closure ensures benefits are captured and credited to the improvement effort.

Lesson 1 • Facilitating Team Meetings Effectively

Applies structured facilitation techniques to keep teams focused and productive. Effective meetings accelerate decision-making and maintain team engagement.

Lesson 2 • Project Planning and Scheduling

Builds a realistic project schedule with milestones, dependencies, and resource assignments. Proactive planning reduces delays and keeps sponsors informed.

Lesson 3 • Replicating and Scaling Improvements

Identifies opportunities to apply proven solutions to similar processes or sites. Replication multiplies financial impact without repeating the full DMAIC cycle.

Lesson 4 • Presenting Results to Leadership

Structures data-driven presentations tailored to executive audiences. Clear storytelling with quantified outcomes secures continued sponsorship and resources.