Lean Six Sigma Black Belt (LSSBB) Certification


The Lean Six Sigma Black Belt program is intended for those who are employed as Black Belt within organizations, spending all of their time on process improvement teams and operates in support of a Lean Six Sigma Master Black Belt, they possesses exceptional expertise and knowledge of current industry practice, they have outstanding leadership ability, strategic vision and innovation.

They are experts in identifying problems, data capturing and analysis, build solutions to lower operational costs and raise profitability. A Certified Lean Six Sigma Black Belt successfully applies process improvement to businesses and organizations across all industries.

Lean Six Sigma Black Belt training, written and facilitated by the author of the latest Lean Six Sigma Handbooks, provides detailed instruction in the application of the Six Sigma and Lean techniques necessary for actively leading Lean Six Sigma project teams and includes:

  • Twelve months access to on line materials including training slides, online study guide, online certification exam and additional Knowledge Center references.
  • Green Belt XL software (Twelve month Student Version). Minitab examples also included!
  • Upon successful course completion: Electronic certificate indicating Lean Six Sigma Black Belt Course Completion
  • Internationally-recognized IQF Lean Six Sigma Study Guide and IQF Lean Six Sigma Certification Exam software. Upon successful certification exam completion, student can receive an electronic certificate indicating either Lean Six Sigma Black Belt Certification without Projects or (if two acceptable projects submitted) Lean Six Sigma Black Belt Certification with Projects.


Candidates should have a general understanding of business functions, have a college degree, very good level of English language, proficient in using MS office and general statistical software and experienced problem solvers. A Green Belt certification is not required.


By completing the Lean Six Sigma Black Belt Certification course, students will be able to:

  • Participate in the development of a successful Lean Six Sigma program.
    Contribute to the definition of project selection criteria and develop project proposals to meet those criteria.
  • Lead a Lean Six Sigma project team, using the DMAIC problem solving methodology and team building skills.
  • Apply and interpret basic and advanced Lean Six Sigma tools, as necessary, for project definition, process baseline analysis, process improvement, and process control.
  • Demonstrate sufficient expertise of the Black Belt Body of Knowledge on an appropriate certification exam.


Approximately 200 hours of instruction, equivalent to (20 CEU)

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1. Introduction to Lean Six Sigma

• Definition of Lean Six Sigma & its relation to Cost and Efficiency.
• Popularity and Application of Lean Six Sigma
• Comparisons between TQM and Lean Six Sigma Programs.

2. How to Deploy Lean Six Sigma

• Leadership responsibilities
• Resource allocation
• Data driven decision making
• Organizational metrics and dashboards

3. Lean Six Sigma Projects

• Project Focus
• Selecting Projects
• Estimating Project Benefits
• Overview of DMAIC methodology
• Project Reporting

4. DEFINE Stage Tools & Objectives

• Define Stage Objectives
• Project Charter: Use and Development

5. DEFINE: Metrics & Deliverables

• Critical to Quality (CTQ) Metrics
• Critical to Schedule (CTS) Metrics
• Relation of CTQ and CTS Metrics to Critical to Cost (CTC) Metrics

6. DEFINE: Project Financials

• Cost of Poor Quality
• Quantifying Project Benefits
• CTC Calculations.

7. DEFINE: Project Scheduling

• Scheduling Project Activities & Events
• Gantt Chart
• Critical Path Analysis / Activity Network Diagram.
• PERT Analysis

8. DEFINE: Change Management and Lean Six Sigma Teams

• Problems with Change
• Achieving Buy-In
• Team Formation, Rules & Responsibilities
• Consensus Building

9. Measure Stage Tools and Objectives

• Measure Stage Objectives
• Process Definition (Flowcharts, Process Maps, SIPOC)
• Metric Definition
• Establishing Process Baseline

10. MEASURE: Probability & Distributions

• General Probability Rules.
• Distribution Use & Interpretation in MS Excel and Minitab

11. MEASURE: X-Bar Charts

• Assumptions
• Construction & Calculations.
• Rational Subgroups
• Sampling Considerations.
• Interpretation

12. MEASURE: Individuals Data

• Uses, comparison with X-Bar chart
• Construction, calculations, assumptions, sampling considerations and interpretation:

13. MEASURE: Process Capability

• Histograms Use and Misuse
• Probability Plots
• Goodness of Fit Tests
• Capability & Performance Indices

14. MEASURE: Attribute Charts

• Selection (P, U, Np, C Charts)
• Construction & Calculations
• Sampling Considerations
• Interpretation

15. MEASURE: Short Run SPC

• Uses
• Calculations

16. MEASURE: Measurement Systems Analysis

• Stability Studies
• Linearity Analysis
• R&R Analysis

17. Analyze Stage Tools & Objectives

• Analyze Stage Objectives
• Definition of Waste
• Analyzing Process for NVA
• Cycle Efficiencies
• Lead Time and Velocity
• Takt Time
• Methods to Increase Velocity
• Standardization
• Optimization
• Spaghetti Diagrams
• Level Loading
• When are Batches More Efficient?
• Setup Reductions

18. ANALYZE: Sources of Variation

• Multi-vari Plots.
• Confidence Intervals on Means & Percents.
• Hypothesis Testing Method, Assumptions and Uses.
• Hypothesis Tests on Mean, Two Sample Means, Paired Samples.
• Hypothesis Tests on Variance, Two Sample Variances.
• Contingency tables.
• Power & Sample Size Considerations.
• Non-parametric Tests.


• Assumptions & Bartlett’s Equality of Variance Test.
• One-way ANOVA in Excel & Minitab
• Two-way ANOVA in Excel & Minitab
• Multi-factor ANOVA in Excel & Minitab
• Tukey’s HSD Test

20. ANALYZE: Regression Analysis

• Cause & Effect Diagrams.
• Scatter Diagrams.
• Correlation, Stratification, Extrapolation
• Linear Regression Model
• Interpreting the ANOVA Table
• Confidence & Prediction Limits.
• Residuals Analysis.

21. ANALYZE: Serial Correlation

• Applications
• Estimating Autocorrelation
• Interpreting Autocorrelation
• Batch Control Charts

22. ANALYZE: Multiple Regression

• Multivariate Models.
• Interaction Plots.
• Interpreting ANOVA Tables.
• Model Considerations.
• Stepwise Regression.
• Residuals Analysis.

23. ANALYZE: DOE Introduction

• Terminology
• DOE vs. Traditional Experiments
• DOE vs. Historical Data
• Design Planning
• Complete Factorials
• Fractional Factorials

24. ANALYZE: DOE Analysis Fundamentals

• Estimating Effects and Coefficients
• Estimating Error & Lack of Fit
• Extending Designs and Power of Design

25. ANALYZE: Design Selection

• Desirable Designs
• Performance: Balance, Orthogonality, Resolution
• Other Design Models

26. ANALYZE: Transformations

• Need for Transformations
• Non-Constant Variance
• Box-Cox Transforms
• Calculated Parameters
• Taguchi Signal to Noise Ratios

27. Improve Stage Tools and Objectives

• Improve Stage Objectives
• Tools to Prioritize Improvement Opportunities
• Defining New Process Flow
• Lean Tools to reduce NVA and Achieve Flow
• 5S
• Level Loading
• Tools to Define & Mitigate Failure Modes
• Preventing Failures

28. IMPROVE: Response Surface Analysis

• Objectives
• Applications
• Sequential Technique
• Steepest Ascent

29. IMPROVE: Ridge Analysis

• Graphical Method
• Analytical Method
• Overlaid Contours
• Desirability Function

30. IMPROVE: Simulations

• Applications
• Examples
• Applying Probabilistic Estimates

31. IMPROVE: Evolutionary Operation

• Methodology
• Example
• Risks & Advantages

32. Control Stage Tools & Objectives

• Control Stage Objectives
• Methods of Control
• Control Plans
• Training
• Measuring Improvement

33. Final Exam