IASSC Universally Accepted Lean Six Sigma Body of Knowledge for Black Belts1.0 Define Phase1.1 The Basics of Six Sigma1.1.1 Meanings of Six Sigma1.1.2 General History of Six Sigma & Continuous Improvement1.1.3 Deliverables of a Lean Six Sigma Project1.1.4 The Problem Solving Strategy Y = f(x)1.1.5 Voice of the Customer, Business and Employee1.1.6 Six Sigma Roles & Responsibilities1.2 The Fundamentals of Six Sigma1.2.1 Defining a Process1.2.2 Critical to Quality Characteristics (CTQ’s)1.2.3 Cost of Poor Quality (COPQ)1.2.4 Pareto Analysis (80:20 rule)1.2.5 Basic Six Sigma Metrics (including DPU, DPMO, FTY, RTY Cycle Time, deriving these metrics)1.3 Selecting Lean Six Sigma Projects1.3.1 Building a Business Case & Project Charter1.3.2 Developing Project Metrics1.3.3 Financial Evaluation & Benefits Capture1.4 The Lean Enterprise1.4.1 Understanding Lean1.4.2 The History of Lean1.4.3 Lean & Six Sigma1.4.4 The Seven Elements of Waste (Overproduction, Correction, Inventory, Motion, Overprocessing, Conveyance, Waiting)1.4.5 5S (Straighten, Shine, Standardize, Self-Discipline, Sort)2.0 Measure Phase2.1 Process Definition2.1.1 Cause & Effect / Fishbone Diagrams2.1.2 Process Mapping, SIPOC, Value Stream Map2.1.3 X-Y Diagram2.1.4 Failure Modes & Effects Analysis (FMEA)2.2 Six Sigma Statistics2.2.1 Basic Statistics2.2.2 Descriptive Statistics2.2.3 Normal Distributions & Normality2.2.4 Graphical Analysis2.3 Measurement System Analysis2.3.1 Precision & Accuracy2.3.2 Bias, Linearity & Stability2.3.3 Gage Repeatability & Reproducibility2.3.4 Variable & Attribute MSA2.4 Process Capability2.4.1 Capability Analysis2.4.2 Concept of Stability2.4.3 Attribute & Discrete Capability2.4.4 Monitoring Techniques3.0 Analyze Phase3.1 Patterns of Variation3.1.1 Multi-Vari Analysis3.1.2 Classes of Distributions3.2 Inferential Statistics3.2.1 Understanding Inference3.2.2 Sampling Techniques & Uses3.2.3 Central Limit Theorem3.3 Hypothesis Testing3.3.1 General Concepts & Goals of Hypothesis Testing3.3.2 Significance; Practical vs. Statistical3.3.3 Risk; Alpha & Beta3.3.4 Types of Hypothesis Test3.4 Hypothesis Testing with Normal Data3.4.1 1 & 2 sample t-tests3.4.2 1 sample variance3.4.3 One Way ANOVA (Including Tests of Equal Variance, Normality Testing and Sample Size calculation, performing tests and interpreting results)3.5 Hypothesis Testing with Non-Normal Data3.5.1 Mann-Whitney3.5.2 Kruskal-Wallis3.5.3 Mood’s Median3.5.4 Friedman3.5.5 1 Sample Sign3.5.6 1 Sample Wilcoxon3.5.7 One and Two Sample Proportion3.5.8 Chi-Squared (Contingency Tables - Including Tests of Equal Variance, Normality Testing and Sample Size calculation, performing tests and interpreting results)4.0 Improve Phase4.1 Simple Linear Regression4.1.1 Correlation4.1.2 Regression Equations4.1.3 Residuals Analysis4.2 Multiple Regression Analysis4.2.1 Non- Linear Regression4.2.2 Multiple Linear Regression4.2.3 Confidence & Prediction Intervals4.2.4 Residuals Analysis4.2.5 Data Transformation, Box Cox4.3 Designed Experiments4.3.1 Experiment Objectives4.3.2 Experimental Methods4.3.3 Experiment Design Considerations4.4 Full Factorial Experiments4.4.1 2k Full Factorial Designs4.4.2 Linear & Quadratic Mathematical Models4.4.3 Balanced & Orthogonal Designs4.4.4 Fit, Diagnose Model and Center Points4.5 Fractional Factorial Experiments4.5.1 Designs4.5.2 Confounding Effects4.5.3 Experimental Resolution5.0 Control Phase5.1 Lean Controls5.1.1 Control Methods for 5S5.1.2 Kanban5.1.3 Poka-Yoke (Mistake Proofing)5.2 Statistical Process Control (SPC)5.2.1 Data Collection for SPC5.2.2 I-MR Chart5.2.3 Xbar-R Chart5.2.4 U Chart5.2.5 P Chart5.2.6 NP Chart5.2.7 Xbar-S Chart5.2.8 CumSum Chart5.2.9 EWMA Chart5.2.10 Control Methods5.2.11 Control Chart Anatomy5.2.12 Subgroups, Impact of Variation, Frequency of Sampling5.2.13 Center Line & Control Limit Calculations5.3 Six Sigma Control Plans5.3.1 Cost Benefit Analysis5.3.2 Elements of the Control Plan5.3.3 Elements of the Response Plan
