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Lean Six Sigma Black Belt


Excellence • 2026 Session

Lean Six Sigma Black Belt

Process complex data, lead multidisciplinary teams, and become a master of the DMAIC methodology. This advanced program prepares you to drive major changes and cost reductions within your organization.
Goal

Course Objective

Through interactive presentations, practical exercises, group activities, simulations, and concrete examples, this course aims for an in-depth and correct understanding of the fundamental elements and basic principles of the Lean Six Sigma methodology. The main objectives are:

  • Discovering and understanding the principles, objectives, and main tools of the Lean Six Sigma methodology.
  • Demonstrating the efficiency of teamwork in identifying the root causes of a problem, finding, and implementing viable and long-lasting solutions.
  • Mastering the use of the MINITAB advanced statistics application.
  • Preparation for obtaining international certification.
Target Audience

Who is this course for?

Candidates for this course must have an inclination towards analysis and be well-regarded by their peers. They will be actively involved in driving changes within the organization and in its development process. Trainees can come from a wide range of disciplines and do not require formal studies in statistics or engineering.

However, because they will master a wide variety of technical tools in a short period of time, it is advisable that they are capable of applying college-level mathematics. They must also be able to assimilate and apply relatively advanced statistical concepts.

A Six Sigma Black Belt will extract useful data from the organization's databases. Successful candidates must understand one or more operating systems, spreadsheets, databases, presentation applications, and word processors. As part of the training, they will use MINITAB advanced statistical software efficiently and confidently.

Prerequisites: prior completion of the Lean Six Sigma Green Belt course.

Format

Course Duration

The course will have a duration of 5 days grouped into 2 sessions.

  • Prior participation in the Six Sigma Green Belt training session is required.
  • Additionally, the study program duration can be adjusted if you have previously attended the free monthly workshops organized on the topic of Six Sigma.
  • To ensure efficient information transfer, the maximum number of participants will be 8.
Curriculum

Course Content

The course aims to transfer competencies for acquiring initial basic and general theoretical knowledge, as well as general and specific competencies regarding:

  • Identifying the principles of the DMAIC approach and the Lean Six Sigma methodology, as well as its main tools, in accordance with ISO 13053-2/2011 and 13053-1/2011 standards, and respecting the curriculum recommended by the International Association for Six Sigma Certification (IASSC).
  • Teamwork to identify and quantify improvement opportunities.
  • Organizing multidisciplinary teams (process organization) where necessary.
  • Leading improvement projects or facilitating their leadership by Green Belts, using the DMAIC methodology.
  • Training, supervising, and coordinating Green Belt specialists in the DMAIC methodology and associated process optimization techniques.
  • Participating in all analysis meetings, through presentations of the work progress, emphasizing up-to-date achievements.

Detailed Content (DMAIC Cycle)

This module includes the chapters contained in IASSC’s Universally Accepted Lean Six Sigma Body of Knowledge.

[Image of Lean Six Sigma DMAIC methodology diagram]

1) Define

a) Six Sigma Initiation
  • i) Meaning of Six Sigma
  • ii) Six Sigma History & Continuous Improvement
  • iii) Deliverables of a Six Sigma Project
  • iv) Problem Solving Strategy Y = f(x)
  • v) Voice of the Customer, Business, and Employee
  • vi) Six Sigma Roles and Responsibilities
b) Six Sigma Fundamentals
  • i) Defining a Process
  • ii) Critical to Quality Characteristics (CTQ)
  • iii) Cost of Poor Quality (COPQ)
  • iv) Pareto Analysis (80:20 rule)
  • v) Basic Six Sigma Metrics, including DPU, DPMO, FTY, RTY, Cycle Time
c) Selecting Six Sigma Projects
  • i) Building the Business Case and Project Charter
  • ii) Identifying Project Metrics
  • iii) Financial Evaluation and Benefits Capture
d) The Lean Organization
  • i) Understanding Lean
  • ii) Lean History
  • iii) Lean & Six Sigma
  • iv) The Seven Elements of Waste: Overproduction, Correction, Inventory, Motion, Overprocessing, Conveyance, Waiting.
  • v) 5S: Sort, Set in order, Shine, Standardize, Sustain

2) Measure

a) Process Definition
  • i) Cause & Effect / Fishbone Diagram
  • ii) Process Mapping, SIPOC, Value Stream Map
  • iii) X-Y Diagram
  • iv) Failure Modes and Effects Analysis (FMEA)
b) Six Sigma Statistics
  • i) Basic Statistics
  • ii) Descriptive Statistics
  • iii) Normal Distributions and Normality
  • iv) Graphical Analysis
c) Measurement System Analysis (MSA)
  • i) Precision and Accuracy
  • ii) Bias, Linearity & Stability
  • iii) Gage Repeatability & Reproducibility
  • iv) Variable and Attribute MSA
d) Process Capability
  • i) Capability Analysis
  • ii) Concept of Stability
  • iii) Attribute and Discrete Capability
  • iv) Monitoring Techniques

3) Analyze

a) Patterns of Variation
  • i) Multi-Vari Analysis
  • ii) Classes of Distributions
b) Inferential Statistics
  • i) Understanding Inference
  • ii) Sampling Techniques
  • iii) Central Limit Theorem
c) Hypothesis Testing
  • i) General Concepts & Goals of Hypothesis Testing
  • ii) Significance: Practical vs. Statistical
  • iii) Risk; Alpha & Beta
  • iv) Types of Hypothesis Tests
d) Hypothesis Testing with Normal Data
  • i) 1 & 2 sample t-tests
  • ii) 1 sample variance
  • iii) One Way ANOVA: Including Test of Equal Variance, Normality Testing, Sample Size calculation, performing tests, and interpreting results
e) Hypothesis Testing with Non-Normal Data
  • i) Mann-Whitney
  • ii) Kruskal-Wallis
  • iii) Mood’s Median
  • iv) Friedman
  • v) 1 Sample Sign
  • vi) 1 Sample Wilcoxon
  • vii) One and Two Sample Proportion
  • viii) Chi-Squared (Contingency Tables): Including Test of Equal Variance, Normality Testing, Sample Size calculation, performing tests, and interpreting results

4) Improve

a) Simple Linear Regression
  • i) Correlation
  • ii) Regression Equations
  • iii) Residuals Analysis
b) Multiple Linear Regression
  • i) Non-Linear Regression
  • ii) Multiple Linear Regression
  • iii) Confidence & Prediction Intervals
  • iv) Residuals Analysis
  • v) Data Transformation, Box Cox
c) Design of Experiments
  • i) Experiment Objectives
  • ii) Experimental Methods
  • iii) Experiment Design Considerations
d) Full Factorial Experiments
  • i) 2k Full Factorial Designs
  • ii) Linear and Quadratic Mathematical Models
  • iii) Balanced and Orthogonal Designs
  • iv) Fit, Diagnose Model, and Center Points
e) Fractional Factorial Experiments
  • i) Design
  • ii) Confounding Effects
  • iii) Experimental Resolution

5) Control

a) Lean Controls
  • i) Control Methods for 5S
  • ii) Kanban
  • iii) Poka-Yoke (Mistake Proofing)
b) Statistical Process Control (SPC)
  • i) Data Collection for SPC
  • ii) I-MR Chart
  • iii) Xbar-R Chart
  • iv) U Chart
  • v) P Chart
  • vi) NP Chart
  • vii) X-S chart
  • viii) CumSum Chart
  • ix) EWMA Chart
  • x) Control Methods
  • xi) Control Chart Anatomy
  • xii) Subgroups, Impact of Variation, Frequency of Sampling
  • xiii) Center Line & Control Limit Calculations
c) Six Sigma Control Plans
  • i) Cost/Benefit Analysis
  • ii) Elements of the Control Plan
  • iii) Elements of the Response Plan
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Lean Six Sigma Black Belt Certification

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