Become a Change Expert. Full Online Black Belt Certification
For the Black Belt Course, the Book of Knowledge links directly with IASSC to support external exam prep
•1.1 Basics of Six Sigma
•Understanding the definitions and historical context of Six Sigma and continuous improvement.
•Identifying deliverables of Lean Six Sigma projects.
•Applying the problem-solving strategy Y = f(x).
•Recognizing the roles and responsibilities within Six Sigma initiatives.
•1.2 Fundamentals of Six Sigma
•Defining processes and identifying Critical to Quality Characteristics (CTQs).
•Calculating the Cost of Poor Quality (COPQ).
•Utilizing Pareto Analysis (80:20 rule).
•Deriving basic Six Sigma metrics, including DPU, DPMO, FTY, RTY, and Cycle Time.
•1.3 Selecting Lean Six Sigma Projects
•Developing business cases and project charters.
•Establishing project metrics.
•Conducting financial evaluations and capturing benefits.
•1.4 The Lean Enterprise
•Comprehending Lean principles and their history.
•Integrating Lean with Six Sigma methodologies.
•Identifying the seven elements of waste: Overproduction, Correction, Inventory, Motion, Overprocessing, Conveyance, and Waiting.
•Implementing 5S: Sort, Straighten, Shine, Standardize, and Sustain.
•2.1 Process Definition
•Creating Cause & Effect (Fishbone) Diagrams.
•Developing Process Maps, SIPOC diagrams, and Value Stream Maps.
•Constructing X-Y Diagrams.
•Performing Failure Modes & Effects Analysis (FMEA).
•2.2 Six Sigma Statistics
•Applying basic and descriptive statistics.
•Understanding normal distributions and assessing normality.
•Conducting graphical analyses.
•2.3 Measurement System Analysis
•Evaluating precision, accuracy, bias, linearity, and stability.
•Conducting Gage Repeatability & Reproducibility studies for both variable and attribute data.
•2.4 Process Capability
•Performing capability analysis and understanding the concept of stability.
•Assessing attribute and discrete capability.
•Implementing monitoring techniques.
•3.1 Patterns of Variation
•Conducting Multi-Vari Analysis.
•Recognizing classes of distributions.
•3.2 Inferential Statistics
•Grasping the principles of inference.
•Utilizing sampling techniques and understanding the Central Limit Theorem.
•3.3 Hypothesis Testing
•Comprehending the concepts and goals of hypothesis testing.
•Differentiating between practical and statistical significance.
•Understanding risks, including Alpha & Beta errors.
•Identifying types of hypothesis tests.
•3.4 Hypothesis Testing with Normal Data
•Performing 1 & 2 sample t-tests, 1 sample variance tests, and One-Way ANOVA.
•Conducting tests of equal variance, normality testing, and sample size calculations.
•3.5 Hypothesis Testing with Non-Normal Data
•Applying tests such as Mann-Whitney, Kruskal-Wallis, Mood’s Median, Friedman, 1 Sample Sign, 1 Sample Wilcoxon, One and Two Sample Proportion, and Chi-Squared (Contingency Tables).
•4.1 Simple Linear Regression
•Analyzing correlation and developing regression equations.
•Conducting residuals analysis.
•4.2 Multiple Regression Analysis
•Implementing non-linear and multiple linear regression.
•Calculating confidence and prediction intervals.
•Performing data transformation, including Box-Cox transformation.
•4.3 Designed Experiments
•Setting experiment objectives and selecting appropriate methods.
•Considering experiment design factors.
•4.4 Full Factorial Experiments
•Designing 2k full factorial experiments.
•Developing linear and quadratic mathematical models.
•Ensuring balanced and orthogonal designs.
•Fitting and diagnosing models, including the use of center points.
•4.5 Fractional Factorial Experiments
•Designing fractional factorial experiments.
•Identifying and managing confounding effects.
•Determining experimental resolution.
5.1 Lean Controls
•Implementing control methods for 5S.
•Utilizing Kanban systems.
•Applying Poka-Yoke (Mistake Proofing) techniques.
•5.2 Statistical Process Control (SPC)
•Collecting data for SPC.
•Developing various control charts, including I-MR, Xbar-R, U, P, NP, X-S, CumSum, and EWMA charts.
•Understanding control chart anatomy, subgroups, impact of variation, and frequency of sampling.
•Calculating center lines and control limits.
•5.3 Six Sigma Control Plans
•Conducting cost-benefit analysis.
•Defining elements of control and response plans.
