Six Sigma Green Belt

Course name

Six Sigma Green Belt

Course ID


Course duration

8 days








Six Sigma is the latest methodology sweeping across the business landscape and being adopted by major corporations and consulting firms. It is a comprehensive, flexible system for achieving, sustaining and maximizing business success.

Green Belt program gives you an opportunity to master problem-solving technology using Six Sigma tools. You will be able to apply a sophisticated data-driven methodology toward any process within your organization.

Requirement: Laptop Computers (windows 98, 200, Me, XP, NT 4; 32MB RAM; Pentium Processor 133 MHz or higher; CD-ROM Drive; 32MB HD space) and Minitab Statistical Software will be required for use in the workshops. Participants must furnish their own laptop computers.

Course objectives

Upon completion of this course, participants will understand the Six Sigma methodology, Six Sigma metrics, and analytical skills for solving many Six Sigma projects. Participants will be awarded Green Belt Certification and should be capable of executing a complete Six Sigma project or a high impact project sub-task.

Who should attend?

Anyone interested in making a significant and lasting  positive contribution toward the effectiveness, efficiency, and profitability of their workplace.

Also for those individuals who wish to upgrade their skills to become more marketable for employment.

Course outline


  • What is Six Sigma?

  • Defects Per Million Opportunities Metric (DPMO)

  • Success Stories

  • SIPOC Model

  • DMAIC Process

  • Process Mapping

  • Six Sigma Organizational Structure

  • Role of the Black Belt


  • Customer Satisfaction & Kano Model

  • Critical to Quality Characteristics (CTQC's)

  • Operational Definition

  • Process Mapping

  • Process Flow Chart

  • Project Selection and Planning

  • Project Charter


  • Variable and Attribute Data

  • Data Collection

  • Measurement System Analysis

  • Baseline DPMO & Sigma Conversion

  • Rolled Throughput Yield


  • Distributions: Mean, Standard Deviation, Histograms

  • Statistical Process Control Charts (SPC)

  • Capability Analysis

  • Confidence Intervals and Hypothesis Testing (Supplemental )

  • Comparison of Two Treatments, F-Test, t-test (Supplemental)

  • Comparison of Multiple Treatments ANOVA (Supplemental)

  • Comparison of Variances - Chi-Square Test (Supplemental)

  • Cause and Effect Diagrams

  • FMEA

  • Regression and Correlation Analysis

  • Introduction to Design of Experiments


  • Error-proofing

  • Corrective Action Matrix

  • Introduction to DOE


  • Poka-Yoke

  • SPC

  • Variable and Attribute Data  Charts

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