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Master Black Belt Lean Six Sigma

Enterprise Deployment & Performance Excellence
Develop the expertise to achieve the highest levels of operational performance using Lean, LSSx.0, DMAIC, and expert-level statistical analysis, while leading large-scale Lean Six Sigma deployments.
13
Days
or
97.5
Hours

Pricing

7450 €
excl. VAT
Open Enrollment Remote
On Request
Request A Quote
In-Company
Small group sessions (maximum 12 participants).
Interactive learning approach with exercises, case studies, and serious games.
Complete training kit included (manuals and materials).
Session recordings available for remote participants for review and reinforcement.
Official LSSx.0 reference books included.
Recognized LSSx.0™ Lean Six Sigma Master Black Belt certification.
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Target Audience

All sectors — industry and services
This Master Black Belt training course is designed for:
Experienced Black Belts seeking to deepen their statistical expertise and lead large-scale improvement programs
Continuous Improvement leaders and Operational Excellence managers
Process improvement experts and transformation leaders
Consultants specializing in Lean Six Sigma deployment
Quality and performance excellence leaders
Prerequisites: Black Belt certification and demonstrated project leadership.

Learning Objectives

01
Understand the philosophy, tools, and principles of Lean Management
02
Assess operational performance, capacity, and resource losses
03
Create and analyze process maps based on field observations
04
Improve operational processes using Lean problem-solving methods
05
Develop performance management routines and lead improvement workshops
01
Understand the foundations of Lean Six Sigma and the DMAIC framework
02
Identify the type of operational problem using the LSSx.0 typology and select the appropriate improvement trajectory
03
Define relevant quality criteria based on customer expectations
04
Identify root causes and evaluate potential improvement solutions
05
Lead a DMAIC project addressing a simple operational problem
01
Define project metrics and structure robust data collection plans
02
Validate measurement systems to ensure reliable data
03
Identify and quantify root causes through statistical analysis
04
Develop statistical process monitoring systems
05
Lead a DMAIC project addressing complex operational problems
01
Validate and improve the precision and accuracy of measurement systems to ensure high-quality data
02
Assess data distribution and select appropriate inferential statistical methods
03
Apply hypothesis testing and linear regression to identify and quantify non-quality factors
04
Design and conduct factorial Design of Experiments (DoE) to evaluate process parameter impact
05
Optimize process performance by analyzing experimental results and defining optimal operating conditions
01
Conduct probabilistic capability studies and validate target performance using advanced hypothesis testing
02
Identify and quantify all influencing factors through advanced statistical analysis and sampling methods
03
Define optimal sampling strategies to maximize control chart effectiveness
04
Design a Lean Six Sigma organizational model aligned with company context and constraints
05
Develop and structure enterprise-level deployment plans to achieve sustainable operational excellence

Training Program

Lean Foundations

Principles and positioning of Lean Management
Continuous improvement and Kaizen approach

Process and Flow Analysis

Process and flow analysis (cycle time, takt time, bottlenecks)
Work-in-progress, lead time, and Little’s Law
Value Stream Mapping (VSM) and rhythm diagrams

Operational Improvement

Waste elimination and value-added analysis
Flow optimization and line balancing
Pull systems and production control (kanban, continuous flow)

Lean Practices and Performance

Visual management and Lean practices (5S, poka-yoke, SMED)
Operational performance and efficiency indicators (OPE, FPY, RTY)

Lean Six Sigma Foundations

Principles and positioning of Lean Six Sigma
LSSx.0 problem typology and selection of the appropriate improvement trajectory

Problem Structuring

Project definition and SIPOC process mapping
Voice of the Customer and Critical-to-Satisfaction

Problem Analysis

Problem metrics and measurement planning
Root cause analysis using Pareto and Five Whys

Solution & Performance Management

Solution identification and evaluation
Performance monitoring and response plan

Statistical Problem Solving

Application of the MAIC method to solve complex statistical problems
Project metrics definition and structured data collection

Measurement System & Process Analysis

Measurement system validation (Gauge R&R, linearity, bias)
Process capability analysis and Voice of the Process

Statistical Analysis

Process analysis using Y = f(X) logic and Ishikawa diagram
Graphical data analysis, correlation, and confidence intervals

Improvement Validation & Control

Statistical validation of improvements
Statistical Process Control (SPC) and control charts

Measurement System Analysis

Measurement system analysis (MSA)
Concordance analysis (Kappa, Kendall)
Gauge R&R using the ANOVA method
Measurement system linearity and bias studies

Statistical Inference

Introduction to hypothesis testing
Normality testing
Equality of variances tests
Influence on central tendency (ANOVA, Mood’s median test, Kruskal–Wallis test)
Chi-square association test

Correlation and Regression

Pearson correlation analysis
Simple and multiple linear regression

Design of Experiments (DoE)

Design of Experiments fundamentals
Two-level factorial designs
Experiment resolution and number of repetitions
Experiment execution

Optimization

Analysis of main effects and interaction effects
Pareto of effects
Response surface methodology
Response optimizer

Advanced Capability and Data Modelling

Probabilistic capability analysis
Expected defect rates for normal, non-normal, Poisson, and binomial distributions
Data transformation methods (Box-Cox, Johnson)

Advanced Statistical Inference

Hypothesis testing and statistical inference
Test power and sample size determination
Parametric and non-parametric tests (t-tests, Mann–Whitney, Friedman)
Variance comparison tests
Proportion and Poisson tests

Advanced Regression and Modelling

Logistic regression
Multiple and general regression models

Advanced Statistical Process Control

Statistical process control
Control chart effectiveness analysis
Time-weighted control charts

Deployment Module

Lean Six Sigma governance
Process management system
Transformation roadmap and ambition setting
Lean Six Sigma organizational structure
Roles and responsibilities (Champion, Sponsor, Process Owner, Belts)
Project selection and portfolio monitoring
Expected performance benefits

Learning Experience

Experiential & Inductive Pedagogy

Kaizen-based role-playing simulation of a cross-functional business process (order reception, production, invoicing, shipping)
DMAIC case study used as a structured guiding thread throughout the training
Statistical problem-solving applied to complex and “discernible” improvement cases
In-class Design of Experiments (DoE) simulations to optimize system parameters (paper flying machines case)
Process capability analysis applied to real production cases (screw manufacturing and printing processes)

Interactive Learning Environment

Small groups (maximum 12 participants) to foster interactivity, idea-sharing, and networking
Active participation through practical exercises and collaborative problem solving
Assessment
Self-assessment and practice tests at the end of each module

Tools & Materials

Statistical software: Statoscopex and Minitab
Excel-based statistical calculators and analytical models
Training materials provided in PDF format
Reference books :
LSSx.0 – 1. Flow Problems and Lean Management
LSSx.0 – 2. Discernible Problems and Methodological Foundation
LSSx.0 – 3. The Statoscope: Resolving Indiscernible and Related Statistical Problems

Certification

The Lean Six Sigma Master Black Belt certification (LSSx.0™) is awarded upon successful completion of two separate examinations: Lean Practitioner Examination and Six Sigma Master Black Belt Examination.
Both exams validate knowledge aligned with the LSSx.0™ body of knowledge, ensuring participants can design enterprise-level Lean Six Sigma architectures and lead large-scale performance transformation initiatives.
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Examination Format

Online, closed-book exams with remote video monitoring
Lean Practitioner Examination : 25 multiple-choice questions – 50 minutes
Six Sigma Master Black Belt Examination : 55 multiple-choice questions – 110 minutes
Available in French or English
Exams must be completed within six months of training

Passing Criteria

Minimum score: 60% per exam
One retake per exam included in case of failure
Additional attempts available upon purchase of a new exam
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