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SixSigma IASSC
The IASSC Certified Lean Six Sigma Green Belt (ICGB) exam equips candidates with practical competence across all five DMAIC phases, enabling them to lead data‑driven process improvement initiatives.
180
Minutes
100
Questions
70/100
Passing Score
$350
Exam Cost
Who Should Take This
It is ideal for emerging analysts, junior engineers, and entry‑level project managers who have limited formal Six Sigma training but seek to validate their ability to apply DMAIC tools. These professionals aim to enhance their problem‑solving credibility and position themselves for roles that require measurable process optimization expertise.
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
Six Sigma Basics and Fundamentals
- Describe the meanings and history of Six Sigma as a philosophy, metric, and methodology, and explain how continuous improvement principles drive organizational performance.
- Apply the Y=f(x) problem-solving strategy to identify critical input variables and their relationship to output quality characteristics in process improvement projects.
- Explain CTQ characteristics, COPQ analysis, and Pareto analysis principles, and calculate basic Six Sigma metrics (DPU, DPMO, FTY, RTY, cycle time).
- Differentiate between VOC, VOB, and VOE perspectives and apply each to identify improvement opportunities that balance customer, business, and employee needs.
- Describe roles and responsibilities across the Six Sigma organization and explain how role assignments affect project scope, authority, and resource allocation.
- Explain the DMAIC methodology phases and describe the purpose, key activities, tollgate deliverables, and success criteria for each phase in a Green Belt project.
- Analyze process performance data to assess current sigma level and determine the gap between current and target performance for project goal setting.
- Apply team dynamics principles including forming-storming-norming-performing stages, RACI assignment, and conflict resolution techniques to manage Green Belt project teams effectively.
Project Selection and Lean Enterprise
- Develop business cases and project charters with clear problem statements, scope definitions, goals, metrics, and financial evaluations for Lean Six Sigma projects.
- Apply financial evaluation and benefits capture methods to estimate, track, and report the monetary impact of Lean Six Sigma improvement projects.
- Analyze the seven elements of waste using Lean principles and apply 5S methodology to identify, quantify, and eliminate waste in production and service processes.
- Explain how Lean and Six Sigma methodologies complement each other and determine when each approach is most effective for a given improvement opportunity.
- Apply stakeholder analysis techniques to identify project supporters, resistors, and influencers and develop engagement strategies for each stakeholder group.
- Analyze the integration of Lean waste elimination and Six Sigma variation reduction to determine the optimal improvement approach for different process problem types.
2
Measure Phase
2 topics
Process Definition and Statistics
- Construct cause-and-effect diagrams, process maps, SIPOC diagrams, and value stream maps to document processes and identify measurement points for data collection.
- Apply X-Y diagrams and FMEA to prioritize process inputs, assess failure mode risks, and focus improvement efforts on the most critical process variables.
- Calculate and interpret descriptive statistics (mean, median, mode, range, variance, standard deviation) and construct graphical displays (histograms, box plots, scatter diagrams).
- Describe properties of normal distributions, apply normality assessment tests, and explain when data transformations are needed for valid statistical analysis.
- Apply sampling methods (random, stratified, systematic) and calculate required sample sizes to ensure statistically representative data collection from process populations.
- Differentiate between continuous and discrete data types and between nominal, ordinal, interval, and ratio measurement scales for proper statistical method selection.
- Analyze graphical displays to identify data distribution characteristics, detect patterns and anomalies, and validate data quality before proceeding with statistical analysis.
MSA and Process Capability
- Conduct gauge R&R studies for variable and attribute measurement systems and interpret results to assess precision, accuracy, bias, linearity, and stability.
- Perform process capability analysis, calculate capability indices, and interpret results to determine whether a process meets specification requirements.
- Analyze the relationship between process stability and capability and explain why a process must be in statistical control before capability analysis is valid.
- Apply process monitoring techniques to track ongoing capability and detect shifts, trends, or degradation in process performance over time.
- Calculate process sigma levels from DPMO values and explain the 1.5-sigma shift convention used to convert between short-term and long-term capability estimates.
3
Analyze Phase
2 topics
Variation and Inferential Statistics
- Conduct multi-vari analysis to identify and separate positional, cyclical, and temporal patterns of variation in process data.
- Classify distributions and apply sampling techniques to obtain representative process data, explaining the central limit theorem and its implications for inferential statistics.
- Explain the foundational concepts of hypothesis testing including null and alternative hypotheses, significance levels, and the distinction between statistical and practical significance.
- Differentiate between alpha and beta risks (Type I and Type II errors) and explain how sample size, effect size, and significance level affect statistical power.
- Explain the central limit theorem and describe how it enables the use of normal-distribution-based methods even when the underlying process data is non-normal.
- Apply inferential reasoning to draw conclusions about process populations from sample data, distinguishing between point estimates and interval estimates.
Hypothesis Testing Methods
- Apply one-sample and two-sample t-tests to compare process means against targets or between groups and interpret results for process improvement decisions.
- Apply one-way ANOVA to compare means across multiple groups, verify assumptions (equal variance, normality), and determine appropriate sample sizes for valid comparisons.
- Apply non-parametric tests (Mann-Whitney, Kruskal-Wallis, Mood's Median, Friedman, Wilcoxon) when normality assumptions are violated and explain when each test is appropriate.
- Apply one-sample and two-sample proportion tests and chi-squared contingency table analysis to test hypotheses about categorical process data.
- Analyze hypothesis test results to select the appropriate test based on data type, sample size, and comparison requirements, and determine actionable conclusions.
- Apply single-factor variance tests (F-test, Levene's test, Bartlett's test) to compare process variability across groups and assess variance homogeneity assumptions.
- Analyze the practical significance of statistically significant results by evaluating effect sizes, confidence intervals, and the business impact of detected differences.
4
Improve Phase
1 topic
Regression Analysis
- Perform simple linear regression analysis, calculate the correlation coefficient, and interpret the strength and direction of the linear relationship between two process variables.
- Develop regression equations, calculate prediction and confidence intervals, and perform residuals analysis to validate regression model assumptions.
- Apply multiple linear regression to model relationships between multiple predictor variables and a response, interpreting coefficients and assessing model significance.
- Apply non-linear regression approaches and Box-Cox data transformations when linear models are inadequate, and evaluate transformation effectiveness.
- Analyze regression model outputs to evaluate model adequacy, identify influential data points, detect multicollinearity, and determine whether the model provides reliable predictions.
- Differentiate between correlation and causation when interpreting regression results and explain why designed experiments may be needed to establish causal relationships.
- Apply regression models to predict process outcomes under different operating conditions and calculate prediction intervals to quantify prediction uncertainty.
5
Control Phase
2 topics
Lean Controls and SPC
- Apply 5S control methods, kanban systems, and poka-yoke techniques to sustain Lean improvements and prevent regression to pre-improvement process states.
- Apply SPC data collection methods and construct appropriate control charts (I-MR, Xbar-R, Xbar-S, P, NP, U charts) based on data type and subgroup characteristics.
- Construct and interpret CuSum and EWMA charts for detecting small sustained process shifts that traditional Shewhart charts may miss.
- Analyze control chart patterns to detect out-of-control signals, interpret run rules, and distinguish process shifts from random variation for corrective action decisions.
- Explain rational subgrouping principles for control chart construction and describe how subgroup selection affects chart sensitivity to process shifts.
- Differentiate between common cause and special cause variation on control charts and explain the management response appropriate for each type of variation.
Control Plans and Sustainment
- Perform cost-benefit analysis for control system implementation to justify monitoring investments and quantify the cost of control versus the cost of process failure.
- Develop comprehensive control plans specifying control methods, measurement procedures, sampling plans, reaction plans, and responsible personnel.
- Develop response plans that define escalation procedures, containment actions, and corrective action triggers when control charts signal process abnormalities.
- Analyze control system effectiveness by evaluating detection capability, false alarm rates, and response time to determine whether the control plan adequately protects against process degradation.
- Apply documentation and training procedures to transfer improved processes to process owners with adequate standard operating procedures and job aids.
- Explain how PDCA methodology applies to ongoing process monitoring and control to continuously adjust and improve sustained process performance.
- Analyze process audit results to verify that implemented controls remain effective and that process performance continues to meet established targets.
- Apply visual management techniques (Andon boards, status displays, performance dashboards) to provide real-time process visibility for operators and management.
Scope
Included Topics
- All five DMAIC phases in the IASSC Lean Six Sigma Green Belt Body of Knowledge: Define, Measure, Analyze, Improve, and Control.
- Define phase: Six Sigma basics and fundamentals, CTQ characteristics, COPQ, Pareto analysis, basic metrics (DPU, DPMO, FTY, RTY, cycle time), project selection and charter development, financial evaluation, and Lean enterprise principles including seven wastes and 5S.
- Measure phase: process definition tools (fishbone diagrams, process mapping, SIPOC, value stream mapping, X-Y diagrams, FMEA), descriptive and inferential statistics, normal distributions, graphical analysis, MSA (precision, accuracy, GR&R), and process capability analysis.
- Analyze phase: patterns of variation (multi-vari analysis, distribution classification), inferential statistics (sampling, central limit theorem), hypothesis testing (normal and non-normal data tests including t-tests, ANOVA, Mann-Whitney, Kruskal-Wallis, chi-square), and proportion testing.
- Improve phase: simple and multiple linear regression, correlation analysis, residuals analysis, data transformation (Box-Cox), confidence and prediction intervals. Control phase: Lean controls (5S, kanban, poka-yoke), SPC charts (I-MR, Xbar-R, U, P, NP, Xbar-S, CuSum, EWMA), control and response plans.
Not Covered
- Advanced DOE including full and fractional factorial designs covered in Black Belt certification.
- Multivariate analysis, MANOVA, discriminant analysis, and logistic regression covered in Black Belt certification.
- Enterprise-wide deployment, strategic planning, and portfolio management covered in Black Belt and Master Black Belt.
- Advanced robust design, Taguchi methods, and response surface methodology.
Official Exam Page
Learn more at International Association for Six Sigma Certification
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