QCLSSC Lean Six Sigma Green Belt Certification

Module 1: What Is Six Sigma?

• Understand the fundamental purpose and goals of Six Sigma in organizations.
• Recognize how Six Sigma improves quality, reduces defects, and enhances efficiency.
• Identify the roles and responsibilities of Six Sigma team members.
• Appreciate the strategic importance of Six Sigma for organizational success.
• Understand the link between Six Sigma and data-driven decision-making.

Module 2: Six Sigma History and Application

• Trace the evolution of Six Sigma from its origins to modern applications.
• Understand contributions from pioneers like Motorola and GE in quality management.
• Recognize industries and sectors where Six Sigma is applied successfully.
• Identify key milestones and development of Six Sigma methodology.
• Connect historical insights to current process improvement practices.

Module 3: Other Process Improvement and Quality Methods

• Compare Six Sigma with Lean, Kaizen, Total Quality Management (TQM), and other frameworks.
• Understand when to use alternative methods for process improvement.
• Identify complementary tools to enhance efficiency and quality.
• Recognize strengths and limitations of each approach.
• Integrate multiple methodologies to achieve continuous improvement.

Module 4: Lean Concepts

• Understand the principles of Lean and the importance of eliminating waste.
• Identify the eight types of waste (TIMWOOD) in processes.
• Distinguish between value-added and non-value-added activities.
• Apply Lean tools like 5S, Kaizen, and Kanban.
• Learn how Lean improves workflow efficiency and reduces cycle time.

Module 5: Basic Six Sigma Concepts

• Understand Six Sigma terminology including defects, variation, and sigma levels.
• Learn key metrics like DPMO (Defects Per Million Opportunities).
• Recognize the importance of data in process improvement.
• Understand Six Sigma hierarchy and roles: Champion, Black Belt, Green Belt, Yellow Belt.
• Apply basic concepts to identify improvement opportunities.

Module 6: Approaching the Problem

• Apply structured problem-solving approaches in process improvement.
• Use tools like Fishbone diagrams and 5 Whys to identify root causes.
• Develop clear problem statements for projects.
• Apply Plan-Do-Check-Act (PDCA) methodology for solutions.
• Prioritize problems based on impact and feasibility.

Module 7: What is a Process?

• Define processes and their components including inputs, outputs, and stakeholders.
• Understand the role of processes in achieving organizational goals.
• Learn techniques for process mapping and documentation.
• Recognize process performance metrics and variation.
• Identify opportunities for optimization and efficiency improvements.

Module 8: Quality

• Understand quality from both customer and organizational perspectives.
• Recognize the cost of poor quality (COPQ) and its impact on business.
• Learn about quality dimensions in products and services.
• Apply quality principles to enhance operational efficiency.
• Understand the connection between quality improvements and customer satisfaction.

Module 9: Selecting the Right Projects

• Identify criteria for high-impact Six Sigma projects.
• Align project selection with organizational strategy.
• Assess project feasibility, resource needs, and potential ROI.
• Create project charters to define scope and objectives.
• Ensure stakeholder alignment for successful project execution.

Module 10: Basic Six Sigma Team Management

• Understand team structures and roles in Six Sigma projects.
• Develop leadership and communication skills for team management.
• Assign responsibilities effectively for project success.
• Promote collaboration and resolve team conflicts.
• Monitor team performance and maintain accountability.

Module 11: Introduction to DMAIC and DMADV

• Understand the purpose of DMAIC (Define-Measure-Analyze-Improve-Control) and DMADV (Define-Measure-Analyze-Design-Verify).
• Learn key steps and deliverables in each methodology.
• Understand when to use DMAIC versus DMADV for process improvement.
• Apply structured approaches to solve business problems.
• Recognize the role of DMAIC and DMADV in achieving measurable outcomes.

Module 12: Define

• Develop precise problem statements and project objectives.
• Identify customers, stakeholders, and their requirements.
• Define project scope, boundaries, and constraints.
• Create project charters to guide improvement initiatives.
• Establish measurable goals and performance indicators.

Module 13: Measure

• Collect accurate and relevant data to evaluate process performance.
• Develop measurement systems for consistency and reliability.
• Calculate baseline metrics and process capability.
• Understand types of data and ensure data quality.
• Prepare measurement plans to support projects.

Module 14: Analyze

• Apply statistical tools to identify root causes of process inefficiencies.
• Use Pareto charts, cause-and-effect diagrams, and process analysis techniques.
• Understand correlation, causation, and trend patterns in data.
• Develop hypotheses for testing improvement opportunities.
• Prioritize factors that impact process performance.

Module 15: Improve

• Design and implement solutions to address root causes.
• Apply Lean tools to eliminate waste and improve efficiency.
• Optimize workflows to reduce variation and enhance quality.
• Conduct pilot testing and validation of solutions.
• Align improvements with organizational objectives and customer requirements.

Module 16: Control

• Develop control plans to sustain process improvements.
• Monitor key performance indicators to maintain consistency.
• Implement corrective actions when deviations occur.
• Use statistical process control (SPC) charts to track stability.
• Foster a culture of continuous improvement for long-term success.

Module 17: Intermediate Graphical Analysis

• Learn to visualize data using histograms, scatter plots, and box plots.
• Identify trends, outliers, and patterns in process data.
• Interpret graphical results for informed decision-making.
• Communicate insights effectively using visual data representation.
• Apply graphical analysis to support process improvement projects.

Module 18: Normal Probability Distributions

• Understand the characteristics of normal distributions.
• Calculate probabilities, Z-scores, and areas under the curve.
• Apply normality assumptions to process evaluation.
• Analyze process variation using normal distribution principles.
• Interpret results for data-driven decisions in improvement projects.

Module 19: Correlation and Regression

• Identify relationships between variables using correlation analysis.
• Apply linear regression to predict process outcomes.
• Interpret regression coefficients and R-squared values.
• Use correlation and regression to support root cause analysis.
• Integrate statistical modeling into process improvement strategies.

Module 20: Non-Normal Probability Distributions

• Recognize non-normal distributions such as Poisson, Binomial, and Exponential.
• Apply probability calculations for non-normal process data.
• Adapt Six Sigma tools for non-normal datasets.
• Analyze real-world data that does not follow normal distribution.
• Use non-normal distribution results to support improvement decisions.

Module 21: Hypothesis Testing

• Formulate null and alternative hypotheses for process evaluation.
• Select appropriate statistical tests for given data sets.
• Interpret p-values, confidence intervals, and test outcomes.
• Apply hypothesis testing to validate improvement initiatives.
• Make informed, data-driven business decisions.

Module 22: Sample Size

• Calculate sample sizes to ensure accurate data representation.
• Understand the effect of sample size on result reliability.
• Apply formulas for determining minimum sample requirements.
• Avoid under-sampling and over-sampling errors.
• Ensure data collected supports valid Six Sigma conclusions.

Module 23: Advanced Control Charts

• Monitor process stability using advanced control chart techniques.
• Identify trends, shifts, and variations in process data.
• Choose the appropriate control chart based on data type.
• Analyze deviations to maintain process consistency.
• Apply advanced control charts to sustain process improvements.

Module 24: Applying Statistics to Business Applications Through Six Sigma

• Integrate statistical analysis into decision-making for business processes.
• Identify improvement opportunities using process data.
• Measure the impact of changes using Six Sigma metrics.
• Interpret statistical results to guide actionable solutions.
• Apply statistical insights to optimize overall organizational performance.