I-CERT Lean Six Sigma Green Belt Level 2 Certification

Learning Objectives

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

1. What Is Six Sigma?

• Understand the fundamentals and purpose of Six Sigma
• Recognize its role in process improvement and quality management
• Identify key concepts and terminology used in Six Sigma
• Explain the benefits of Six Sigma in business operations

2. Six Sigma History and Application

• Understand the historical development of Six Sigma
• Explore real-world applications across industries
• Recognize the impact of Six Sigma on operational efficiency
• Analyze case studies of successful Six Sigma implementation

3. Other Process Improvement and Quality Methods

• Gain awareness of alternative quality and improvement methodologies
• Compare Lean, Kaizen, Total Quality Management (TQM), and other approaches
• Understand their integration with Six Sigma initiatives
• Evaluate when to apply specific methodologies

4. Lean Concepts

• Understand core Lean principles and waste elimination
• Apply Lean tools to improve process efficiency
• Integrate Lean with Six Sigma for continuous improvement
• Identify opportunities to optimize workflows

5. Basic Six Sigma Concepts

• Understand key Six Sigma metrics and terminology
• Learn about process variation and performance measurement
• Apply Six Sigma principles to simple process problems
• Develop a foundation for advanced statistical analysis

6. Approaching the Problem

• Learn structured problem-solving techniques
• Define problems clearly using data and evidence
• Identify root causes systematically
• Develop actionable improvement strategies

7. What is a Process?

• Understand the components and flow of business processes
• Map and analyze processes effectively
• Identify inefficiencies and bottlenecks
• Recognize process inputs and outputs

8. Quality

• Understand quality concepts and standards
• Recognize customer requirements and expectations
• Apply quality measurement tools
• Integrate quality principles into process improvement

9. Selecting the Right Projects

• Learn criteria for identifying high-impact projects
• Evaluate project feasibility and benefits
• Align projects with organizational goals
• Prioritize initiatives for maximum value

10. Basic Six Sigma Team Management

• Understand team roles and responsibilities
• Apply effective team management practices
• Facilitate collaboration in process improvement projects
• Support leadership in Six Sigma initiatives

11. Introduction to DMAIC and DMADV

• Understand DMAIC and DMADV methodologies
• Identify when to use each approach
• Apply frameworks to structure process improvement projects
• Develop project charters and objectives

12. Define

• Clearly define project goals and scope
• Identify key stakeholders and requirements
• Develop process maps and project plans
• Set measurable objectives

13. Measure

• Collect and analyze process data
• Establish baseline performance metrics
• Identify sources of variation
• Use measurement tools to quantify process performance

14. Analyze

• Perform root cause analysis using data-driven methods
• Apply statistical techniques to identify process issues
• Use graphical and analytical tools for decision-making
• Prioritize improvement opportunities

15. Improve

• Design and implement process improvements
• Test solutions and validate results
• Apply Lean tools to optimize workflows
• Ensure improvements align with business objectives

16. Control

• Establish control plans to sustain improvements
• Use control charts and monitoring tools
• Ensure process stability and consistency
• Develop documentation for continuous improvement

17. Intermediate Graphical Analysis

• Apply charts and graphs to interpret process data
• Identify trends, patterns, and anomalies
• Make data-driven decisions
• Communicate findings effectively

18. Normal Probability Distributions

• Understand normal distribution and its applications
• Apply probability concepts to process performance
• Analyze data using statistical tools
• Make informed process improvement decisions

19. Correlation and Regression

• Understand relationships between variables
• Apply regression analysis for predictive insights
• Use correlation to identify process dependencies
• Support data-driven decision-making

20. Non-Normal Probability Distributions

• Identify non-normal data patterns
• Apply statistical methods for non-normal distributions
• Analyze and interpret non-standard process data
• Make reliable improvement recommendations

21. Hypothesis Testing

• Formulate and test hypotheses using statistical methods
• Make decisions based on data analysis
• Evaluate process changes and improvements
• Reduce uncertainty in decision-making

22. Sample Size

• Determine appropriate sample sizes for analysis
• Ensure statistical validity and accuracy
• Apply sampling techniques in process studies
• Support effective measurement and control

23. Advanced Control Charts

• Use advanced control charts for process monitoring
• Detect variation and trends in processes
• Ensure consistent quality and performance
• Support long-term process stability

24. Applying Statistics to Business Applications Through Six Sigma

• Apply statistical methods to solve real-world business problems
• Use data to drive process improvement initiatives
• Translate analysis into actionable business insights
• Demonstrate competency in Lean Six Sigma methodology