Lean Six Sigma Green Belt Practitioners - Part 2 (inc exam)


The Lean Six Sigma Green Belt Practitioner training complements the soft skills gained from the Green Belt Manager course, with advanced tools for quantitative process improvement. To successfully complete a DMAIC project, Green Belts need to possess not only Leadership and Project Management skills, but also understand the principles of data driven decision making.

The course is aligned with the work performed on projects so that, after week 1, delegates are able to start working on the Define and Measure phase. They attend Week 2 when they are close to the Analyse phase. At this stage, more advanced techniques are required to help them analyse process data, interpret the results and apply inferential techniques to measure the risk associated with change and improvement decisions. Upon completion of the LSSPGB2 delegates are fully equipped to tackle more complex business problems and complement qualitative analysis (process modelling, VOC analysis, Value analysis, Root cause analysis) with formal methods (estimation, hypothesis testing, correlation analysis).

Delegates also have an opportunity to learn how to use the main tools for Statistical Analysis in a project environment.
Theoretical concepts are alternated with hands on exercises on real projects data using a variety of software applications (Excel, MINITAB, Crystal Ball, Fathom).

On the last day of the training, delegates sit on the Green Belt certification exam.

After successfully completing the Green Belt exam, delegates have 12 months to demonstrate application of Lean Six Sigma methods to real projects in their own organisation, in order to obtain a full Green Belt certification.

If you have any queries about your projects following the course or wish to submit your project for accreditation then please contact the Lean Six Sigma group via the email [email protected]


At the end of this course you will be able to understand:

  • Quantitative methods for process improvement
  • Application of statistical tools to support root Cause Analysis
  • Descriptive and Inferential Statistics techniques
  • Data Analysis (graphical and formal)
  • DMAIC Projects - Financial Indicators
  • Basics of Probability theory (and practical applications to estimation problems)


Delegates are advised that they must ensure that they bring along their course materials from the Lean Six Sigma Green Belt Practitioners Part 1 course.


Introduction and Learning objectives for the week

Reminder from GB1 (exercises)

The Define Phase

  • The Business Case - Project financial indicators (ROI, NPV)
  • The Business Case - Probabilistic Models (Crystal Ball exercises)
  • Probability - introduction
  • Probability models (and their use for process improvement and design)

The Measure phase

  • Reminder (measurement framework and metrics identification)
  • Descriptive statistics
  • MINITAB Exercises (Creating a process baseline) - graphical tools
  • Z transformation (Appendix)

The Analyse Phase

  • Root Cause Analysis (reminder)
  • Estimation (point estimation and confidence intervals)
  • The Central Limit Theorem (CLT)
  • Hypothesis Testing - Introduction
  • Hypothesis Testing Examples and MINITAB Exercises
  • Hypothesis Testing on Continuous Normal Data (Z and T tests, tests for variances..)
  • ANOVA - Analysis of Variance
  • Non Parametric Tests
  • Tests for discrete variables (proportions, Chi-Square)
  • Correlation Analysis and Correlation indexes (Pearson, Spearman)
  • Regression Analysis overview and exercises
  • Measurement System Analysis (MSA / Gage R&R)

The Improve Phase

  • Reminder
  • Improvement Qualification
  • Change Management in the Improve phase - The role of Green Belts

The Control Phase

  • Statistical Process Control
  • SPC applicability and interpretation

Control Charts

  • X-bar and R charts
  • I-MR charts
  • U Charts

Green Belt training Conclusions and Next Steps

Training provider

Teaching mode: Classroom - Instructor Led
Duration: 5 days
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