Six Sigma Training

Course Aim

This course equips delegates with a working knowledge of Six Sigma tools and techniques and the critical thinking skills required to apply them.

The duration of the Green Belt course is 10 days; the programme ultimately allows Green Belt candidates to increase process and system knowledge through the correct application of problem solving and statistical methods. The programme enhances critical thinking and technical expertise.

The training makes extensive use of hands-on exercises; company specific projects and data sets ensure this to be of the greatest benefit. The two-week training programme is structured to follow the five main phases of Six Sigma DMAIC – Define, Measurement, Analyse, Improve and Control.

We offer short, focused, strategic, high-value interventions

Course Objectives

By the end of this course, delegates will be able to:

  • Explain Six Sigma methodology – DMAIC
  • Apply statistical methods to specific company projects in a disciplined approach in order to capture business opportunities and improve performance
  • Develop critical thinking skills to allow them to work at the highest level of efficiency in the future

Course Content

Course content includes:

Six Sigma Introduction
Why six Sigma?
Six Sigma project definition
Project selection
Scoping projects
Six Sigma deployment
Process mapping
Input prioritization tools
Failure mode effect analysis
Minitab 14.1 introduction
Measurement systems
Capability analysis
Quality Function Deployment (QFD)
Data collection
Sampling principles
Statistical process control
Process control plan
Project plan using MS Project and deliverable
Project reviews
Homework

There is now a gap of one month , during which time delegates start working on an in-house project, using the tools learned so far.

  • Previous course review in class project
  • Design of experiments (DOE)
  • Design for Six Sigma (DFSS) tools
  • Piloting
  • Voice of the customer (VOC)
  • Critical to quality (CTQs, CTBs, CTXs)
  • Advanced graphical analysis
  • Multi-variate planning
  • Variation trees and funnelling
  • Hypothesis testing
  • Central limit theorem
  • Statistical analysis roadmap
  • Test for mean with t-test
  • One way ANOVA
  • Non-manufacturing applications
  • Correlation and regression
  • Multi-variate analysis (MANOVA)
  • Developing control plans
  • Control charts
  • Impact of process instability on capability process
  • Confidence intervals ( vs Hypothesis tests)
  • Implications of the central limit theorem
  • General linear models
  • Simulation
  • DOE ( focus on two-level factorials, screening designs)
  • Piloting improvements
  • Mistake proofing
  • More statistical process control
  • Presentation
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