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SixSigma IASSC
The IASSC Certified Lean Six Sigma Black Belt (ICBB) exam validates mastery of Define, Measure, Analyze, Improve, and Control phases, enabling professionals to lead data‑driven process improvement initiatives.
240
Minutes
150
Questions
70/100
Passing Score
$450
Exam Cost
Who Should Take This
Mid‑level managers, quality engineers, and consultants who have practical Six Sigma exposure and aim to spearhead enterprise‑wide improvement projects should pursue this certification. It equips them with the analytical and leadership skills needed to design, implement, and sustain high‑impact solutions.
What's Covered
1
Define Phase
2
Measure Phase
3
Analyze Phase
4
Improve Phase
5
Control Phase
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
60 learning goals
1
Define Phase
2 topics
Advanced Six Sigma and Lean Foundations
- Design comprehensive Lean Six Sigma project frameworks that integrate Six Sigma variation reduction with Lean waste elimination for maximum process improvement impact.
- Apply advanced Y=f(x) analysis to decompose complex process systems into manageable input-output relationships and prioritize critical transfer functions.
- Apply advanced CTQ flowdown analysis to translate high-level customer requirements into specific, measurable process parameters across multiple process layers.
- Analyze the financial impact of quality using advanced COPQ models, opportunity cost analysis, and Pareto prioritization across enterprise-level process portfolios.
- Design VOC/VOB/VOE data collection strategies that capture diverse stakeholder perspectives and translate them into actionable improvement priorities.
- Apply advanced sigma level analysis to benchmark process maturity and establish stretch improvement targets that drive breakthrough performance gains.
Advanced Project Selection and Charter
- Design project selection frameworks that evaluate strategic alignment, financial ROI, resource requirements, and cross-functional impact to build prioritized project pipelines.
- Develop comprehensive project charters with detailed financial evaluations, risk assessments, and benefits capture plans for complex multi-phase improvement initiatives.
- Apply advanced waste analysis to identify the seven wastes across complex service and manufacturing value streams and quantify waste elimination opportunities.
- Analyze project scope to identify potential scope creep risks, stakeholder conflicts, and resource constraints that may impact project success.
- Design 5S implementation plans with sustainment audits, visual standards, and performance metrics for complex multi-area workplace organization initiatives.
- Apply advanced value stream mapping to identify system-level improvement opportunities including lead time reduction, inventory optimization, and flow balancing.
2
Measure Phase
2 topics
Advanced Process Definition and Statistics
- Construct advanced process maps, value stream maps, and SIPOC diagrams for complex multi-department processes to identify all measurement points and data collection requirements.
- Apply advanced FMEA methodology with detailed severity, occurrence, and detection rating scales to prioritize failure modes across complex process systems.
- Apply comprehensive descriptive and inferential statistics to characterize process populations, construct confidence intervals, and perform parameter estimation.
- Analyze graphical displays to assess distributional fit, identify outliers and influential observations, and validate statistical assumptions for advanced analysis methods.
- Design comprehensive data collection plans specifying operational definitions, sampling strategies, measurement scales, and data validation procedures for complex processes.
- Apply advanced X-Y matrix analysis to prioritize process variables and create focused measurement plans that capture the most influential input-output relationships.
Advanced MSA and Capability
- Design and conduct comprehensive MSA studies including advanced GR&R analysis, attribute agreement analysis, and linearity and bias assessments for complex measurement systems.
- Perform advanced process capability analysis for both normal and non-normal data, applying transformations when needed and interpreting Cp, Cpk, Pp, and Ppk indices.
- Analyze process stability requirements for capability studies and evaluate the impact of measurement system variation on capability index estimates.
- Design measurement system improvement plans when GR&R studies reveal inadequate measurement capability and verify improvement effectiveness through follow-up studies.
- Apply advanced capability analysis for attribute data (defects per unit, proportion defective) and interpret results for processes where variables measurement is not feasible.
3
Analyze Phase
2 topics
Advanced Variation Analysis and Inference
- Conduct advanced multi-vari studies to decompose total process variation into positional, cyclical, and temporal components and identify dominant variation sources.
- Apply advanced sampling strategies and sample size calculations to ensure adequate statistical power for detecting specified effect sizes in process comparisons.
- Analyze the relationship between sample size, significance level, effect size, and statistical power to design efficient hypothesis testing plans for process improvement decisions.
- Design sampling strategies that balance statistical power requirements with practical constraints including cost, time, and process disruption considerations.
- Analyze the impact of sample size on confidence interval width, hypothesis test power, and the ability to detect economically meaningful process differences.
Comprehensive Hypothesis Testing
- Apply one-sample and two-sample t-tests with appropriate assumptions checking (normality, equal variance) and interpret results for process mean comparisons.
- Apply one-way ANOVA with post-hoc comparisons, verify assumptions, and determine appropriate sample sizes for multi-group process mean comparisons.
- Apply non-parametric hypothesis tests (Mann-Whitney, Kruskal-Wallis, Mood's Median, Friedman, sign/Wilcoxon tests) for data that violates normality assumptions.
- Apply proportion tests and chi-squared contingency table analysis to evaluate categorical data hypotheses about process defect rates and attribute characteristics.
- Analyze hypothesis test selection criteria to determine the most appropriate test based on data type, sample size, number of groups, and distributional assumptions.
- Apply variance component analysis to partition total process variation into within-group and between-group components for targeted variation reduction strategies.
- Analyze the assumptions underlying each hypothesis test method and apply diagnostic checks (normality tests, equal variance tests) to validate test appropriateness.
4
Improve Phase
2 topics
Advanced Regression Analysis
- Perform advanced regression analysis including simple linear, multiple linear, and non-linear regression with comprehensive residuals analysis and model validation.
- Apply Box-Cox and other data transformations to achieve linearity and normality assumptions, and evaluate transformation effectiveness using residual diagnostics.
- Construct and interpret confidence intervals and prediction intervals for regression predictions, explaining how interval width varies with distance from the mean.
- Analyze regression model diagnostics to detect multicollinearity, heteroscedasticity, autocorrelation, and influential observations that may invalidate model predictions.
- Design confirmatory regression studies that validate predictive models under operating conditions and verify that model predictions translate to actual process improvement.
Design of Experiments
- Design two-level full factorial (2^k) experiments specifying factors, levels, randomization, replication, and blocking strategies to isolate main effects and interactions.
- Design fractional factorial experiments for screening large numbers of factors, understanding confounding patterns, resolution levels, and alias structures.
- Analyze DOE results to identify statistically significant main effects and interactions, interpret effect magnitude, and determine optimal factor settings for process optimization.
- Evaluate experimental design efficiency by assessing confounding, resolution, and power, and recommend when higher-resolution or additional experiments are needed.
- Design blocking and randomization strategies for experiments conducted in environments with known nuisance variables to isolate treatment effects from noise.
- Apply DOE results to determine optimal process settings, predict response values at optimum conditions, and validate predictions through confirmation runs.
- Analyze the trade-offs between experimental resolution, run count, and information content when selecting between full factorial and fractional factorial designs.
5
Control Phase
2 topics
Advanced SPC and Lean Controls
- Apply advanced Lean control mechanisms including 5S sustainment audits, kanban system design, and poka-yoke device selection for different error types and process contexts.
- Select and construct the appropriate SPC chart type (I-MR, Xbar-R, Xbar-S, P, NP, U, C, CuSum, EWMA) based on data characteristics, subgroup size, and detection requirements.
- Analyze advanced control chart patterns using Western Electric rules, Nelson rules, and zone tests to detect subtle process changes and determine appropriate corrective responses.
- Evaluate the detection capability of different control chart types for various shift magnitudes and select charts that provide the best balance of sensitivity and false alarm rate.
- Design rational subgrouping strategies that maximize control chart sensitivity to process shifts while maintaining practical sampling feasibility.
- Apply advanced SPC theory to determine control limit calculations, understand average run length (ARL), and evaluate the operating characteristic of control chart schemes.
Advanced Control and Response Plans
- Design comprehensive control plans integrating SPC monitoring, Lean controls, response procedures, and escalation protocols for sustained process improvement.
- Develop advanced response plans with tiered escalation procedures, containment protocols, root cause investigation triggers, and corrective action verification methods.
- Design process handoff and transition plans that transfer ownership to process owners with adequate training, documentation, and ongoing support mechanisms.
- Analyze the long-term sustainability of implemented controls by evaluating adherence rates, process owner engagement, and control system degradation indicators.
- Design training and communication programs for process operators and managers that ensure understanding and compliance with implemented control systems.
- Apply lessons learned documentation processes to capture knowledge from completed projects and build organizational improvement capability for future initiatives.
- Analyze process control system maturity to evaluate whether monitoring, response, and sustainment mechanisms are adequate for long-term process stability maintenance.
Scope
Included Topics
- All five DMAIC phases in the IASSC Lean Six Sigma Black Belt Body of Knowledge at advanced depth: Define, Measure, Analyze, Improve, and Control.
- Define phase: comprehensive Six Sigma and Lean foundations, advanced CTQ and COPQ analysis, project selection with financial evaluation, advanced waste analysis and 5S implementation.
- Measure phase: advanced process definition tools (fishbone, process mapping, SIPOC, value stream mapping, X-Y diagrams, FMEA), descriptive and inferential statistics, distributions and normality, graphical analysis, comprehensive MSA (precision, accuracy, bias, linearity, stability, GR&R), advanced process capability analysis and monitoring.
- Analyze phase: advanced multi-vari analysis, comprehensive inferential statistics, full hypothesis testing suite (normal and non-normal data tests, proportion tests, chi-squared analysis, ANOVA with assumptions checking).
- Improve phase: advanced regression (simple, multiple, non-linear), correlation analysis, residuals analysis, confidence and prediction intervals, designed experiments (2^k factorial designs, fractional factorials, confounding), Box-Cox transformations. Control phase: advanced SPC (I-MR, Xbar-R, U, P, NP, Xbar-S, CuSum, EWMA), Lean controls, comprehensive control and response plans.
Not Covered
- Enterprise-wide deployment strategy, organizational change management, and portfolio management covered in MBB-level certifications.
- Response surface methodology, Taguchi designs, split-plot designs, and Monte Carlo simulation covered in advanced specialist certifications.
- Multivariate analysis methods (MANOVA, discriminant analysis, logistic regression) beyond the Black Belt scope.
- Training design and delivery, coaching and mentoring responsibilities.
Official Exam Page
Learn more at International Association for Six Sigma Certification
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