Universally Accepted Lean Six Sigma Body of Knowledge for Green Belts

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1 Universally Accepted Lean Six Sigma Body of Knowledge for Green Belts The IASSC Certified Green Belt Exam was developed and constructed based on the topics within the body of knowledge listed here. Questions may test up to the complexity level of Apply, as opposed to Analyze for the ICBB, as defined by Levels of Cognition based on Bloom s Taxonomy Revised (2001). 1.0 Define Phase 1.1 The Basics of Six Sigma Meanings of Six Sigma General History of Six Sigma & Continuous Improvement Deliverables of a Lean Six Sigma Project The Problem Solving Strategy Y = f(x) Voice of the Customer, Business and Employee Six Sigma Roles & Responsibilities 1.2 The Fundamentals of Six Sigma Defining a Process Critical to Quality Characteristics (CTQ s) Cost of Poor Quality (COPQ) Pareto Analysis (80:20 rule) Basic Six Sigma Metrics a. including DPU, DPMO, FTY, RTY Cycle Time, deriving these metrics and these metrics 1.3 Selecting Lean Six Sigma Projects Building a Business Case & Project Charter Developing Project Metrics Financial Evaluation & Benefits Capture 1.4 The Lean Enterprise Understanding Lean The History of Lean Lean & Six Sigma The Seven Elements of Waste a. Overproduction, Correction, Inventory, Motion, Overprocessing, Conveyance, Waiting S a. Straighten, Shine, Standardize, Self-Discipline, Sort

2 2.0 Measure Phase 2.1 Process Definition Cause & Effect / Fishbone Diagrams Process Mapping, SIPOC, Value Stream Map X-Y Diagram Failure Modes & Effects Analysis (FMEA) 2.2 Six Sigma Statistics Basic Statistics Descriptive Statistics Normal Distributions & Normality Graphical Analysis 2.3 Measurement System Analysis Precision & Accuracy Bias, Linearity & Stability Gage Repeatability & Reproducibility Variable & Attribute MSA 2.4 Process Capability Capability Analysis Concept of Stability Attribute & Discrete Capability Monitoring Techniques 3.0 Analyze Phase 3.1 Patterns of Variation Multi-Vari Analysis Classes of Distributions 3.2 Inferential Statistics Understanding Inference Sampling Techniques & Uses Central Limit Theorem 3.3 Hypothesis Testing General Concepts & Goals of Hypothesis Testing Significance; Practical vs. Statistical Risk; Alpha & Beta Types of Hypothesis Test

3 3.4 Hypothesis Testing with Normal Data & 2 sample t-tests sample variance One Way ANOVA a. Including Tests of Equal Variance, Normality Testing and Sample Size calculation, performing tests and interpreting results. 3.5 Hypothesis Testing with Non-Normal Data Mann-Whitney Kruskal-Wallis Mood s Median Friedman Sample Sign Sample Wilcoxon One and Two Sample Proportion Chi-Squared (Contingency Tables) a. Including Tests of Equal Variance, Normality Testing and Sample Size calculation, performing tests and interpreting results. 4.0 Improve Phase 4.1 Simple Linear Regression Correlation Regression Equations Residuals Analysis 4.2 Multiple Regression Analysis Non- Linear Regression Multiple Linear Regression Confidence & Prediction Intervals Residuals Analysis Data Transformation, Box Cox

4 5.0 Control Phase 5.1 Lean Controls Control Methods for 5S Kanban Poka-Yoke (Mistake Proofing) 5.2 Statistical Process Control (SPC) Data Collection for SPC I-MR Chart Xbar-R Chart U Chart P Chart NP Chart X-S chart CumSum Chart EWMA Chart Control Chart Anatomy 5.3 Six Sigma Control Plans Cost Benefit Analysis Elements of the Control Plan Elements of the Response Plan

5 Levels of Cognition based on Bloom s Taxonomy Revised (2001) These levels are from Levels of Cognition (from Bloom s Taxonomy Revised, 2001). They are listed in order from the least complex to the most complex. Remember: Recall or recognize terms, definitions, facts, ideas, materials, patterns, sequences, methods, principles, etc. Understand: Read and understand descriptions, communications, reports, tables, diagrams, directions, regulations, etc. Apply: Know when and how to use ideas, procedures, methods, formulas, principles, theories, etc. Analyze: Break down information into its constituent parts and recognize their relationship to one another and how they are organized; identify sublevel factors or salient data from a complex scenario. Evaluate: Make judgments about the value of proposed ideas, solutions, etc., by comparing the proposal to specific criteria or standards. Create: Put parts or elements together in such a way as to reveal a pattern or structure not clearly there before; identify which data or information from a complex set is appropriate to examine further or from which supported conclusions can be drawn.

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