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Lean Six Sigma Black Belt

Lean Six Sigma Black Belt

The Lean Six Sigma Black Belt training is the most independent certification for the Lean Six Sigma quality management methodology. This Six Sigma Black Belt certification course is the final stage of the Master’s program that will position you as an expert in implementing Lean, Design for Six Sigma (DFSS), Six Sigma Define, Measure, Analyze, Improve and Control (DMAIC) and Total Productive Maintenance (TPM).

Join this exclusive worldwide group of 4000 certified Lean Six Sigma Black Belts today.


The Lean Six Sigma Black Belt Certification is designed to help you master the combined concepts of Lean and Six Sigma methodologies. Lean Six Sigma has emerged as a popular business management strategy applied to projects globally. It helps a company achieve process excellence initiatives, accelerate product delivery, improve product quality, and drive increased profits to an organization. Lean Six Sigma Black Belt certification demonstrates one’s ability to deploy these skills in enterprise-wide projects

Our Lean Six Sigma Black Belt training and certification program is modeled on the Body of Knowledge (BOK), with cutting-edge and real-life applications integrated with the training section.

Once the Lean Six Sigma Black Belt training is complete, participants will be able to implement the concepts of Lean, Design for Six Sigma (DFSS), Six Sigma Define, Measure, Analyze, Improve and Control (DMAIC) and Total Productive Maintenance (TPM) using statistical tools and analysis.


Exam Information

The IASSC Certified Lean Six Sigma Black Belt Exam™ is a 150 question, closed book, proctored exam with a 4 hour allotted time. The Exam contains approximately 30 multiple-choice and true/false questions from each major section of the IASSC Lean Six Sigma Black Belt Body of Knowledge and is administered online.


There are no prerequisites required in order to sit for the IASSC Certified Lean Six Sigma Black Belt Exam.


Module 1: Define Phase

1.1 The Basics of Six Sigma
1.1.1 Meanings of Six Sigma
1.1.2 General History of Six Sigma & Continuous Improvement
1.1.3 Deliverables of a Lean Six Sigma Project
1.1.4 The Problem Solving Strategy Y = f(x)
1.1.5 Voice of the Customer, Business, and Employee
1.1.6 Six Sigma Roles & Responsibilities

1.2 The Fundamentals of Six Sigma
1.2.1 Defining a Process
1.2.2 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
a. including DPU, DPMO, FTY, RTY Cycle Time; deriving these metrics

1.3 Selecting Lean Six Sigma Projects
1.3.1 Building a Business Case & Project Charter
1.3.2 Developing Project Metrics
1.3.3 Financial Evaluation & Benefits Capture

1.4 The Lean Enterprise
1.4.1 Understanding Lean
1.4.2 The History of Lean
1.4.3 Lean & Six Sigma
1.4.4 The Seven Elements of Waste
a. Overproduction, Correction, Inventory, Motion, Overprocessing, Conveyance, Waiting.
1.4.5 5S
a. Straighten, Shine, Standardize, Self-Discipline, Sort

Module 2: Measure Phase

2.1 Process Definition
2.1.1 Cause & Effect / Fishbone Diagrams
2.1.2 Process Mapping, SIPOC, Value Stream Map
2.1.3 X-Y Diagram
2.1.4 Failure Modes & Effects Analysis (FMEA)

2.2 Six Sigma Statistics
2.2.1 Basic Statistics
2.2.2 Descriptive Statistics
2.2.3 Normal Distributions & Normality
2.2.4 Graphical Analysis

2.3 Measurement System Analysis
2.3.1 Precision & Accuracy
2.3.2 Bias, Linearity & Stability
2.3.3 Gage Repeatability & Reproducibility
2.3.4 Variable & Attribute MSA

2.4 Process Capability
2.4.1 Capability Analysis
2.4.2 Concept of Stability
2.4.3 Attribute & Discrete Capability
2.4.4 Monitoring Techniques

Module 3: Analyze Phase

3.1 Patterns of Variation
3.1.1 Multi-Vari Analysis
3.1.2 Classes of Distributions

3.2 Inferential Statistics
3.2.1 Understanding Inference
3.2.2 Sampling Techniques & Uses
3.2.3 Central Limit Theorem

3.3 Hypothesis Testing
3.3.1 General Concepts & Goals of Hypothesis Testing
3.3.2 Significance; Practical vs. Statistical
3.3.3 Risk; Alpha & Beta
3.3.4 Types of Hypothesis Tests

3.4 Hypothesis Testing with Normal Data
3.4.1 1 & 2 sample t-tests
3.4.2 1 sample variance
3.4.3 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
3.5.1 Mann-Whitney
3.5.2 Kruskal-Wallis
3.5.3 Mood’s Median
3.5.4 Friedman
3.5.5 1 Sample Sign
3.5.6 1 Sample Wilcoxon
3.5.7 One and Two-Sample Proportion
3.5.8 Chi-Squared (Contingency Tables)
a. Including Tests of Equal Variance, Normality Testing, and Sample Size calculation, performing tests and interpreting results.

Module 5: Improve Phase

4.1 Simple Linear Regression
4.1.1 Correlation
4.1.2 Regression Equations
4.1.3 Residuals Analysis

4.2 Multiple Regression Analysis
4.2.1 Non- Linear Regression
4.2.2 Multiple Linear Regression
4.2.3 Confidence & Prediction Intervals
4.2.4 Residuals Analysis
4.2.5 Data Transformation, Box-Cox

4.3 Designed Experiments
4.3.1 Experiment Objectives
4.3.2 Experimental Methods
4.3.3 Experiment Design Considerations

4.4 Full Factorial Experiments
4.4.1 2k Full Factorial Designs
4.4.2 Linear & Quadratic Mathematical Models
4.4.3 Balanced & Orthogonal Designs
4.4.4 Fit, Diagnose Model and Center Points

4.5 Fractional Factorial Experiments
4.5.1 Designs
4.5.2 Confounding Effects
4.5.3 Experimental Resolution

Module 6: Control Phase

5.1 Lean Controls
5.1.1 Control Methods for 5S
5.1.2 Kanban
5.1.3 Poka-Yoke (Mistake Proofing)

5.2 Statistical Process Control (SPC)
5.2.1 Data Collection for SPC
5.2.2 I-MR Chart
5.2.3 Xbar-R Chart
5.2.4 U Chart
5.2.5 P Chart
5.2.6 NP Chart
5.2.7 Xbar-S Chart
5.2.8 CuSum Chart
5.2.9 EWMA Chart
5.2.10 Control Methods
5.2.11 Control Chart Anatomy
5.2.12 Subgroups, Impact of Variation, Frequency of Sampling
5.2.13 Center Line & Control Limit Calculations

5.3 Six Sigma Control Plans
5.3.1 Cost-Benefit Analysis
5.3.2 Elements of the Control Plan
5.3.3 Elements of the Response Plan

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