Lean Six Sigma Black Belt Course

Lesson 1 • DMAIC Roadmap Overview

Presents the Define-Measure-Analyze-Improve-Control phases as a structured problem-solving cycle. Clarifies phase gates, deliverables, and tollgate review criteria used throughout the course.

Lesson 2 • Six Sigma Quality Concepts

Introduces defect rates, sigma levels, process capability, and the cost of poor quality. Establishes the statistical rationale for targeting 3.4 defects per million opportunities.

Lesson 3 • Core Principles of Lean Thinking

Defines the five Lean principles—value, value stream, flow, pull, and perfection—and applies them to real processes. Connects Lean thinking to waste elimination and customer-centric design.

Lesson 4 • History and Evolution of Lean Six Sigma

Traces the origins of Toyota Production System, Motorola's Six Sigma, and their merger into Lean Six Sigma. Provides context for why the integrated approach outperforms either methodology alone.

Lesson 1 • Building a Robust Project Charter

Constructs each charter element: problem statement, goal statement, scope, timeline, and team. Teaches how a well-written charter prevents mid-project redirection and secures sponsor commitment.

Lesson 2 • SIPOC and Process Scoping

Builds Supplier-Input-Process-Output-Customer diagrams to define process boundaries at a high level. Prevents scope creep by anchoring the team to agreed start and end points.

Lesson 3 • Stakeholder Analysis and Communication Planning

Maps stakeholder influence and interest to craft targeted engagement strategies. Builds a communication plan that maintains momentum and reduces resistance throughout the project.

Lesson 4 • Voice of the Customer Analysis

Collects and translates customer needs into measurable Critical-to-Quality characteristics using interviews, surveys, and Kano analysis. Links customer requirements directly to project metrics.

Lesson 1 • Data Types and Collection Planning

Distinguishes continuous, discrete, and attribute data and selects appropriate collection strategies for each. Designs sampling plans that balance statistical confidence with collection cost.

Lesson 2 • Process Mapping and Value Stream Mapping

Creates detailed current-state process maps and value stream maps to expose waste, delays, and handoff failures. Provides the visual foundation for identifying root causes in the Analyze phase.

Lesson 3 • Baseline Capability Analysis

Calculates Cp, Cpk, Pp, Ppk, DPMO, and sigma level to quantify current process performance. Establishes the numeric baseline against which improvement will be measured.

Lesson 4 • Statistical Distributions and Descriptive Statistics

Reviews normal, binomial, Poisson, and other distributions relevant to process data. Builds statistical literacy needed for hypothesis testing and capability analysis in later phases.

Lesson 1 • Root Cause Analysis Techniques

Applies fishbone diagrams, 5 Whys, fault tree analysis, and cause-and-effect matrices to structure root cause investigation. Prioritizes verified causes using data before moving to solutions.

Lesson 2 • Comparing Means and Variances

Applies t-tests, ANOVA, F-tests, and Levene's test to determine whether group differences are statistically significant. Connects test results to specific process inputs suspected of causing variation.

Lesson 3 • Attribute and Non-Parametric Tests

Covers chi-square, Mann-Whitney, Kruskal-Wallis, and Mood's median tests for non-normal or attribute data. Expands the analyst's toolkit beyond normality assumptions.

Lesson 4 • Hypothesis Testing Principles

Establishes null and alternative hypotheses, alpha and beta risks, and p-value interpretation for process comparisons. Provides the decision framework used in all inferential statistical tests.

Lesson 1 • Advanced DOE and Response Surface Methods

Extends DOE to response surface methodology and central composite designs for optimizing continuous responses. Enables Black Belts to find optimal operating conditions beyond simple factor screening.

Lesson 2 • Solution Generation and Selection

Uses brainstorming, TRIZ, benchmarking, and weighted decision matrices to generate and rank candidate solutions. Ensures the chosen solution addresses root causes rather than symptoms.

Lesson 3 • Design of Experiments Fundamentals

Introduces full factorial, fractional factorial, and screening designs to efficiently identify factor effects and interactions. DOE replaces one-factor-at-a-time testing with statistically rigorous experimentation.

Lesson 4 • Lean Improvement Tools

Applies 5S, standard work, visual management, error-proofing, and quick changeover to eliminate waste and stabilize processes. These tools create the operational foundation for sustaining statistical gains.

Lesson 1 • Project Closure and Knowledge Transfer

Formalizes financial benefit validation, lessons-learned documentation, and replication opportunities across the organization. Ensures institutional knowledge is captured and the project is officially closed.

Lesson 2 • Control Plan Development

Builds a comprehensive control plan linking CTQs, control methods, measurement frequency, and response actions. The control plan is the primary handoff document to the process owner.

Lesson 3 • Selecting and Applying Control Charts

Matches the correct chart type—Xbar-R, Xbar-S, I-MR, p, np, c, u—to data type and subgroup structure. Correct chart selection ensures valid control limits and meaningful signals.

Lesson 4 • Mistake-Proofing and Visual Controls

Embeds poka-yoke devices and visual standards into the process to prevent defects without relying on operator vigilance. Reduces control burden and sustains gains even during workforce changes.

Lesson 1 • Logistic Regression and Binary Outcomes

Models the probability of binary outcomes such as pass/fail or defect/no-defect as a function of continuous or categorical inputs. Extends regression capability to attribute response variables.

Lesson 2 • Multivariate Analysis Techniques

Applies principal component analysis, cluster analysis, and discriminant analysis to high-dimensional process data. Reduces complexity and reveals hidden structure in large datasets.

Lesson 3 • Reliability Analysis and Life Data

Uses Weibull analysis, failure mode and effects analysis, and accelerated life testing to model product and process reliability. Connects reliability metrics to customer satisfaction and warranty cost.

Lesson 4 • Monte Carlo Simulation and Risk Modeling

Builds probabilistic models to simulate process output distributions under uncertainty and quantify risk. Supports robust design decisions when physical experimentation is costly or impractical.

Lesson 1 • Building a Continuous Improvement Culture

Designs recognition systems, idea management processes, and leadership behaviors that embed continuous improvement into daily operations. Shifts the organization from project-based to culture-based improvement.

Lesson 2 • Change Management and Resistance Handling

Applies structured change management models to plan, communicate, and sustain organizational change driven by improvement projects. Addresses resistance with evidence-based influence strategies.

Lesson 3 • Strategic Deployment Planning

Aligns the Lean Six Sigma program with organizational strategy using hoshin kanri and balanced scorecard linkages. Ensures project portfolios address the most critical business gaps.

Lesson 4 • Financial Analysis for Black Belts

Quantifies hard and soft savings, calculates net present value and return on investment, and validates benefits with finance. Builds credibility with executives by speaking the language of financial impact.