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SixSigma ASQ
The ASQ Certified Six Sigma Green Belt exam validates mastery of the Define, Measure, Analyze, and Improve phases, enabling professionals to lead data‑driven process improvement initiatives across organizations.
258
Minutes
110
Questions
550/750
Passing Score
$483
Exam Cost
Who Should Take This
Mid‑level engineers, analysts, or project managers with at least three years of experience in quality, operations, or manufacturing are ideal candidates. They seek formal recognition of their Six Sigma expertise to advance careers, lead cross‑functional teams, and drive measurable performance gains.
What's Covered
1
All domains in the ASQ Certified Six Sigma Green Belt (CSSGB) Body of Knowledge: Overview of Six Sigma and the Organization
2
, Define Phase
3
, Measure Phase
4
, Analyze Phase
5
, Improve Phase
6
, and 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
63 learning goals
1
Domain 1: Overview - Six Sigma and the Organization
3 topics
Six Sigma and Organizational Goals
- Explain the value of Six Sigma to organizations and trace its evolution from quality leaders including Shewhart, Deming, Juran, and the Motorola and GE deployments.
- Apply SMART goal criteria to establish linkages between Six Sigma projects and organizational strategic objectives, ensuring project outcomes drive measurable business results.
- Identify organizational drivers and metrics including profit margins, market share, customer satisfaction scores, and KPIs, and explain how Six Sigma projects align with these measures.
- Analyze the relationship between Six Sigma project outcomes and organizational KPIs to demonstrate the quantifiable business value of completed improvement projects.
Lean Principles in the Organization
- Explain Lean concepts including theory of constraints, value chain analysis, flow optimization, takt time calculation, just-in-time principles, and Gemba walk methodology.
- Apply value stream mapping techniques to identify value-added and non-value-added activities, quantify lead times, and propose future-state process improvements.
- Analyze a process value stream to identify the system constraint (bottleneck) and recommend throughput improvement strategies based on theory of constraints principles.
Design for Six Sigma (DfSS)
- Describe DMADV and IDOV roadmaps for Design for Six Sigma and explain when DfSS is preferred over DMAIC for product and process design initiatives.
- Differentiate between Design FMEA (DFMEA) and Process FMEA (PFMEA), identifying the purpose, timing, and application context for each type within product development lifecycles.
2
Domain 2: Define Phase
3 topics
Project Identification and VOC
- Apply project selection criteria and methodology to evaluate candidate Six Sigma projects based on strategic alignment, financial impact, and resource requirements.
- Explain benchmarking types (competitive, collaborative, best practices) and apply benchmarking methodology to establish performance baselines and improvement targets.
- Identify internal and external customers and apply VOC data collection methods (surveys, focus groups, interviews, complaint analysis) to capture customer requirements.
- Apply Quality Function Deployment (QFD), CTQ trees, and the Kano model to translate voice of the customer data into measurable critical-to-quality characteristics.
- Construct a SIPOC diagram to define process elements and boundaries, and identify process owners and key stakeholders for project scope definition.
- Analyze Kano model results to classify customer requirements as must-be, one-dimensional, or attractive, and prioritize CTQ characteristics based on their impact on customer satisfaction.
Project Management and Charter
- Develop a project charter with a well-defined problem statement, project scope, SMART goals, primary and consequential metrics, and team member assignments.
- Apply project planning tools including WBS, Gantt charts, critical path method (CPM), and PERT charts to schedule and manage Six Sigma project activities.
- Perform risk analysis for Six Sigma projects by identifying potential risks, assessing probability and impact, and developing mitigation strategies.
- Describe project closure activities including lessons learned documentation, results validation, and handoff procedures to process owners.
Management Tools and Team Dynamics
- Apply management and planning tools including affinity diagrams, interrelationship digraphs, tree diagrams, prioritization matrices, matrix diagrams, PDPC, and activity network diagrams to complex project scenarios.
- Conduct a SWOT analysis to evaluate project feasibility and identify strategic factors that influence project planning and execution.
- Explain team evolution stages and apply RACI matrices to define roles, responsibilities, and accountability structures for Six Sigma project teams.
- Calculate and interpret process performance metrics (DPU, RTY, COPQ, DPMO, sigma levels) to establish project baselines and measure improvement impact.
3
Domain 3: Measure Phase
3 topics
Process Analysis and Probability
- Create detailed process maps, procedures documentation, and flowcharts to document current-state processes and identify measurement points for data collection.
- Apply probability concepts including independent events, mutually exclusive events, multiplication rules, and addition rules to calculate process outcome probabilities.
- Explain the central limit theorem and its significance for sampling distributions, confidence interval construction, and hypothesis testing in process analysis.
- Describe descriptive versus inferential statistics and explain the role of each in process characterization, prediction, and decision-making in Six Sigma projects.
Statistical Distributions and Data Collection
- Identify and describe properties of key statistical distributions (normal, binomial, Poisson, chi-square, Student's t, and F) and explain when each distribution models process data.
- Differentiate between continuous (variables) and discrete (attributes) data and between nominal, ordinal, interval, and ratio measurement scales for proper statistical method selection.
- Design sampling plans specifying sampling methods (random, stratified, systematic, cluster), sample sizes, and data collection procedures that ensure representative and unbiased process data.
- Construct and interpret graphical displays including scatter diagrams, histograms, box-and-whisker plots, and normal probability plots to assess data distribution characteristics.
- Calculate and interpret descriptive statistics including measures of central tendency and dispersion for both continuous and discrete process data sets.
MSA and Process Capability
- Conduct gauge repeatability and reproducibility (GR&R) studies and interpret results including bias, linearity, and precision-to-tolerance (P/T) ratios to assess measurement system adequacy.
- Calculate and interpret process capability indices (Cp, Cpk) and process performance indices (Pp, Ppk) to assess whether a process meets specification requirements.
- Differentiate between short-term capability and long-term performance, explain the 1.5-sigma shift concept, and calculate process sigma levels from DPMO values.
- Explain the relationship between natural process limits and specification limits and assess whether a process is both capable and centered.
4
Domain 4: Analyze Phase
3 topics
Exploratory Data Analysis
- Conduct multi-vari studies to identify positional, cyclical, and temporal patterns of variation within process data and determine dominant sources of variability.
- Perform simple linear regression analysis, calculate the correlation coefficient, interpret p-values for significance, and distinguish correlation from causation in process variable relationships.
Hypothesis Testing
- Explain the distinction between statistical significance and practical significance and determine appropriate sample sizes for hypothesis tests based on desired power and effect size.
- Apply one-sample and two-sample t-tests, F-tests, and one-way ANOVA to compare process means and variances and interpret the results in the context of process improvement.
- Apply chi-square tests for independence and goodness-of-fit to analyze categorical process data and assess whether observed frequencies differ significantly from expected values.
- Analyze hypothesis test outputs to evaluate Type I and Type II error risks and make informed decisions about process changes based on statistical evidence.
- Determine the appropriate hypothesis test (t-test, F-test, ANOVA, chi-square) based on data type, number of samples, and the specific comparison being made.
Root Cause and Gap Analysis
- Perform gap analysis to compare current process performance against benchmarks or targets and quantify the performance gap requiring improvement.
- Apply root cause analysis techniques including fishbone diagrams, 5 Whys, and fault tree analysis to systematically identify the fundamental causes of process defects.
- Evaluate multiple root causes to prioritize which causes have the greatest impact on process performance and warrant focused improvement efforts.
5
Domain 5: Improve Phase
2 topics
Design of Experiments
- Define DOE terminology including factors, levels, responses, treatments, replication, and randomization, and explain how designed experiments isolate the effects of process variables.
- Interpret main effects plots and interaction plots from factorial experiments to identify which factors and factor combinations have the greatest influence on process responses.
- Plan a full factorial experiment specifying factors, levels, randomization strategy, and replication plan appropriate to the process improvement objective.
Implementation and Lean Tools
- Design pilot tests, simulations, and proof-of-concept trials to validate proposed improvements before full-scale implementation.
- Apply Lean waste elimination tools including pull systems, kanban, 5S, standard work, and poka-yoke to reduce non-value-added activities in process flows.
- Apply cycle-time reduction techniques including SMED (Single-Minute Exchange of Dies) and continuous flow principles to reduce process lead times and improve throughput.
- Apply Kaizen and Kaizen Blitz methodologies to execute rapid, focused improvement events that deliver measurable results within compressed timeframes.
- Evaluate proposed improvement solutions using selection criteria including effectiveness, feasibility, implementation cost, and sustainability to choose the optimal solution.
6
Domain 6: Control Phase
3 topics
Statistical Process Control
- Explain SPC theory and objectives including monitoring process stability, tracking trends, reducing variation, and distinguishing common cause from special cause variation.
- Apply rational subgrouping principles to select appropriate subgroup sizes and sampling frequencies for control chart construction.
- Select and construct appropriate control charts (X-bar-R, X-bar-s, ImR for variables; p, np, c, u for attributes) based on data type, subgroup size, and monitoring objectives.
- Analyze control chart patterns to detect out-of-control conditions, shifts, trends, and runs, and determine appropriate corrective actions for each pattern type.
Sustaining Improvements
- Develop comprehensive control plans specifying critical process parameters, monitoring methods, reaction plans, and ownership to sustain process improvements.
- Explain document control procedures, training plan development, and audit types (first-party, second-party, third-party) used to sustain standardized process improvements.
- Apply PDCA methodology in the control phase to continuously monitor, adjust, and improve sustained processes based on ongoing performance data.
Lean Process Controls
- Explain total productive maintenance (TPM) principles and describe how preventive maintenance programs reduce equipment-related process variation and downtime.
- Apply visual factory elements including Andon signals, Jidoka automation, and visual management boards to enable real-time process monitoring and rapid response to abnormalities.
Scope
Included Topics
- All domains in the ASQ Certified Six Sigma Green Belt (CSSGB) Body of Knowledge: Overview of Six Sigma and the Organization (10%), Define Phase (18%), Measure Phase (18%), Analyze Phase (16%), Improve Phase (15%), and Control Phase (14%).
- Organizational alignment including Six Sigma linkage to organizational goals, SMART goals, KPIs, Lean principles (theory of constraints, value chain, flow, takt time, JIT, Gemba), value stream mapping, and Design for Six Sigma methodologies (DMADV, IDOV).
- Define phase competencies including project identification and selection, benchmarking, VOC collection and analysis, QFD, CTQ trees, Kano model, project charter and scope definition, project management tools (WBS, Gantt, CPM, PERT), risk analysis, management and planning tools, business results metrics (DPU, RTY, COPQ, DPMO, sigma levels), and team dynamics.
- Measure phase skills covering process mapping, probability and statistics, statistical distributions (normal, binomial, Poisson, chi-square, t, F), data collection methods, descriptive statistics, graphical analysis, measurement system analysis (GR&R), and process capability indices (Cp, Cpk, Pp, Ppk).
- Analyze phase skills in multi-vari studies, correlation and regression, hypothesis testing (t-tests, F-tests, ANOVA, chi-square), root cause analysis, and gap analysis. Improve phase competencies in Design of Experiments (DoE) fundamentals, Lean tools (pull systems, kanban, 5S, poka-yoke, SMED, Kaizen). Control phase skills in SPC theory, control charts (X-bar-R, X-bar-s, ImR, p, np, c, u), sustaining improvements, and Lean process controls (TPM, visual factory).
Not Covered
- Advanced DOE topics including fractional factorial designs, response surface methodology, and Taguchi methods covered in Black Belt certification.
- Advanced multivariate analysis, discriminant analysis, MANOVA, and logistic regression covered in Black Belt and Master Black Belt certifications.
- Enterprise-wide Six Sigma deployment planning, organizational change management, and strategic program design covered in Black Belt and Master Black Belt.
- Software-specific statistical tool implementation beyond conceptual understanding of statistical methods.
- Industry-specific regulatory frameworks, ISO auditing procedures, and sector-specific quality management system implementation.
Official Exam Page
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