Six Sigma: Black Belt Course

Lesson 1 • Core Philosophy and Key Concepts

Defines variation, defects, and process capability as the philosophical pillars of Six Sigma. Connects these concepts to customer value and business outcomes.

Lesson 2 • History and Evolution of Six Sigma

Traces Six Sigma from Motorola's origins through modern enterprise adoption. Provides context for why the methodology exists and how it drives competitive advantage.

Lesson 3 • Project Charter and Scope Definition

Teaches how to draft a project charter that aligns stakeholders on problem, goal, scope, and timeline. A well-scoped charter prevents project drift and sets measurable targets.

Lesson 4 • Roles, Belts, and Governance

Clarifies the responsibilities of Champions, Master Black Belts, Black Belts, and Green Belts. Explains how governance structures sustain Six Sigma programs over time.

Lesson 1 • Sampling Strategies and Data Collection Plans

Teaches rational subgrouping, sampling frequency, and structured data collection plan design. Proper sampling ensures data represent true process behavior without bias.

Lesson 2 • Conducting Gauge R&R Studies

Provides step-by-step guidance for designing and executing crossed and nested Gauge R&R studies. Results are interpreted to accept, improve, or replace measurement systems.

Lesson 3 • Attribute Agreement Analysis

Covers Kappa statistics and attribute Gauge R&R for pass/fail and categorical measurement systems. Connects attribute measurement reliability to defect classification accuracy.

Lesson 4 • Measurement System Analysis Fundamentals

Introduces Gauge R&R concepts including repeatability, reproducibility, bias, linearity, and stability. Establishes why measurement error must be quantified before process data are trusted.

Lesson 1 • Parametric and Non-Parametric Tests

Covers t-tests, ANOVA, Mann-Whitney, Kruskal-Wallis, and chi-square tests with selection criteria. Correct test selection depends on data type, distribution, and sample structure.

Lesson 2 • Confidence Intervals and Hypothesis Testing

Explains confidence interval construction and the logic of null and alternative hypotheses. These tools form the statistical backbone of the Analyze and Improve phases.

Lesson 3 • Descriptive Statistics and Graphical Analysis

Covers measures of central tendency, spread, and shape alongside histograms, box plots, and run charts. Graphical analysis reveals patterns that numerical summaries alone can miss.

Lesson 4 • Probability Distributions in Six Sigma

Introduces normal, binomial, Poisson, and Weibull distributions with practical process examples. Selecting the correct distribution is prerequisite to accurate capability and reliability analysis.

Lesson 1 • Specification Limits and Customer Requirements

Defines USL, LSL, and target values derived from Voice of the Customer and engineering tolerances. Specification limits are the reference points for all capability calculations.

Lesson 2 • Process Capability Indices

Calculates and interprets Cp, Cpk, Cpm, Pp, and Ppk with emphasis on centering and spread. Distinguishes short-term potential capability from long-term actual performance.

Lesson 3 • Non-Normal Process Capability

Addresses capability analysis when data violate normality using transformations and non-parametric methods. Misapplying normal-based indices to non-normal data produces misleading results.

Lesson 4 • Capability Studies in Practice

Guides students through planning, executing, and reporting a full capability study. Practical considerations include subgroup strategy, data volume, and stakeholder communication.

Lesson 1 • Statistical Verification of Root Causes

Uses hypothesis tests, regression, and logistic regression to confirm that suspected causes are statistically significant. Verification prevents teams from solving the wrong problem.

Lesson 2 • Cause-and-Effect Analysis Tools

Covers fishbone diagrams, 5 Whys, and affinity diagrams for structured brainstorming of potential causes. These tools organize team knowledge before statistical verification.

Lesson 3 • Failure Mode and Effects Analysis

Introduces FMEA as a proactive risk tool for identifying and prioritizing potential failure modes. RPN scoring guides resource allocation toward the highest-risk process steps.

Lesson 4 • Graphical Root Cause Tools

Applies Pareto charts, multi-vari charts, and scatter plots to identify dominant sources of variation. Visual tools accelerate cause identification and communicate findings clearly.

Lesson 1 • DOE Analysis and Validation

Covers ANOVA for DOE, residual diagnostics, model adequacy checks, and confirmation runs. Validation confirms that predicted improvements hold under real process conditions.

Lesson 2 • Full Factorial Experiments

Covers 2k full factorial designs, main effects, interaction effects, and effect estimation. Full factorials provide complete information on all factor combinations within the design space.

Lesson 3 • Fundamentals of Experimental Design

Establishes DOE vocabulary including factors, levels, responses, replication, and randomization. A strong conceptual foundation prevents common experimental design errors.

Lesson 4 • Fractional Factorial and Screening Designs

Introduces half-fraction and higher-fraction designs, confounding, and resolution levels. Screening designs efficiently identify vital few factors from a large candidate set.

Lesson 1 • Control Plans and Sustaining Improvements

Builds comprehensive control plans that link process parameters, measurement methods, and response actions. A robust control plan ensures improvements survive leadership and personnel changes.

Lesson 2 • Variable Control Charts

Covers Xbar-R, Xbar-S, and Individuals-Moving Range charts with construction and interpretation. Variable charts monitor process mean and spread simultaneously for continuous data.

Lesson 3 • Control Chart Theory and Selection

Explains common cause vs. special cause variation and the statistical basis for control chart limits. Correct chart selection depends on data type, subgroup size, and process structure.

Lesson 4 • Advanced SPC Techniques

Introduces CUSUM, EWMA, and multivariate control charts for detecting small shifts and correlated variables. Advanced charts complement traditional Shewhart charts in high-stakes processes.

Lesson 1 • Six Sigma Project Financial Analysis

Quantifies project benefits using hard and soft savings, NPV, and return on investment frameworks. Financial rigor ensures Six Sigma projects are prioritized and credited accurately.

Lesson 2 • Multivariate Analysis Methods

Covers principal component analysis, cluster analysis, and discriminant analysis for high-dimensional data. Multivariate methods reveal patterns invisible to univariate tools in complex processes.

Lesson 3 • Enterprise Deployment and Strategy Alignment

Connects Six Sigma deployment to strategic planning, balanced scorecards, and portfolio management. Black Belts who align projects to strategy maximize organizational impact and program longevity.

Lesson 4 • Reliability Analysis and Life Data

Applies Weibull analysis, failure rate modeling, and accelerated life testing to product reliability. Reliability methods extend Six Sigma from process quality to product durability.