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A propos de la formation Lean Six Sigma Green Belt certification

The Lean Six Sigma Green Belt certification training aims to strengthen participants' understanding of Lean Six Sigma methodologies. This course equips individuals with the knowledge and skills to lead improvement projects independently. Additionally, it prepares participants to successfully pass the Lean Six Sigma Green Belt certification exam offered by IASSC.

Détails
Objectifs pédagogiques de la formation Lean Six Sigma Green Belt certification
  • Analyze data-extract variables-and perform hypothesis testing
  • Design simple experimental plans to collect data effectively
  • Master process modeling techniques and identify correlations between variables
  • Successfully achieve Lean Six Sigma Green Belt certification.

Qui devrait suivre cette formation Lean Six Sigma Green Belt certification ?

Public visé par la formation Lean Six Sigma Green Belt certification

This training is suitable for professionals seeking to enhance their knowledge of Lean Six Sigma and take on roles in improvement projects based on Lean Six Sigma principles. It is particularly beneficial for individuals aspiring to become key contributors in Lean Six Sigma-driven initiatives.  

Prérequis de la formation Lean Six Sigma Green Belt certification

There are no specific prerequisites for this training. However, prior certification as a Yellow Belt is not required. Participants are expected to have a basic understanding of Lean Six Sigma concepts to fully benefit from the course content.

Formations Similaires

Déroulé de la formation Lean Six Sigma Green Belt certification


Module 1: Introduction and Overview


Setting the objectives of the Green Belt certification training.

Overview of the certification process.

Reviewing the DMAIC methodology fundamentals.

Recap of Six Sigma principles and Lean principles.

Understanding different types of waste.

Recap of "Define" and "Measure" phases from the Yellow Belt scope.


Module 2: Analyze Phase - Analyzing Data


Exploring models of variation: analysis and multi-variance graphs, practical applications, data interpretation.

Statistical inference: central limit theorem, standard error, etc.

Introduction to hypothesis testing: objectives, central tendency concept, types of hypothesis tests.

Hypothesis testing with normal data: sample sizes, various hypothesis tests on means, analysis, etc.

Hypothesis testing with non-normal data: equal variance data, medians, proportion tests, contingency tests.


Module 3: Improve Phase - Process Modeling


Process modeling through regression techniques.

Advanced process modeling.

Linear and non-linear regression. Multiple linear regression (MLR).

Introduction to Design of Experiment (DOE).

Explaining the differences between physical model and DOE.

Understanding One Factor at a Time (OFAT) experimentation and its limitations.


Module 4: Control Phase - Process Control


Recap: Lean tools and control, 5S, Kanban, Poke-Yoke, etc.

Overview of Six Sigma control plan, cost-benefit analysis.

Advanced experimentation: utilizing DOE results for process improvement assessment.

Capacity analysis: process capability, method selection, interpretation.

Defect control: prevention methods, tools, and techniques.

Introduction to Statistical Process Control (SPC).

Describing SPC chart elements and objectives.

Explaining the 9 steps of implementing a control chart methodology.


Module 5: Certification Exam Preparation


Case study implementation of DMAIC phases.

General review sessions for exam preparation.

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