Six Sigma Green Belt Training Course

1. Introduction to Six Sigma and Quality Management

1.1 Six Sigma Fundamentals

• Six Sigma philosophy and methodology including (customer focus, data-driven decisions, process improvement)

• Quality management evolution including (TQM, Lean, Six Sigma integration)

• Six Sigma roles and responsibilities including (Champion, Black Belt, Green Belt, Yellow Belt)

• Business benefits and ROI including (cost reduction, quality improvement, customer satisfaction)

• Introduction to ASQ Body of Knowledge and IASSC standards for Six Sigma practitioners

1.2 Statistical Thinking and Data Analysis

• Statistical concepts including (population, sample, variables, distributions)

• Data types and measurement scales including (nominal, ordinal, interval, ratio)

• Descriptive statistics including (central tendency, variability, shape)

• Probability distributions including (normal, binomial, Poisson)

• Statistical inference including (confidence intervals, hypothesis testing, p-values)

2. Define Phase

2.1 Project Selection and Definition

• Project identification criteria including (business impact, feasibility, resource requirements)

• Project charter development including (problem statement, scope, objectives, timeline)

• Stakeholder analysis including (customer identification, voice of customer, requirements)

• SIPOC methodology including (suppliers, inputs, process, outputs, customers)

• Critical to Quality (CTQ) identification including (customer requirements, quality characteristics, specifications)

2.2 Voice of Customer and Requirements

• Voice of Customer (VOC) collection including (surveys, interviews, focus groups, observation)

• Customer requirements analysis including (Kano model, QFD, customer journey mapping)

• Critical to Quality tree including (needs, drivers, requirements, specifications)

• Project scope definition including (boundaries, inclusions, exclusions, constraints)

• Team formation and roles including (project sponsor, team members, stakeholders)

3. Measure Phase

3.1 Data Collection and Measurement Systems

• Data collection planning including (sampling strategy, data sources, collection methods)

• Measurement systems analysis including (Gage R&R, bias, linearity, stability)

• Operational definitions including (clear definitions, measurement procedures, standardization)

• Process mapping including (value stream mapping, flowcharts, swim lane diagrams)

• Baseline measurement including (current state assessment, performance metrics, data validation)

3.2 Process Capability and Statistical Analysis

• Process capability studies including (Cp, Cpk, Pp, Ppk calculations)

• Statistical process control including (control charts, process stability, capability assessment)

• Normality testing including (Anderson-Darling, Shapiro-Wilk, normal probability plots)

• Descriptive statistics including (mean, median, mode, standard deviation, variance)

• Data visualization including (histograms, box plots, scatter plots, Pareto charts)

4. Analyze Phase

4.1 Root Cause Analysis and Problem Solving

• Root Cause Analysis (RCA) techniques including (fishbone diagram, 5 Whys, fault tree analysis)

• Statistical hypothesis testing including (t-tests, chi-square tests, ANOVA)

• Correlation and regression analysis including (linear regression, multiple regression, correlation coefficients)

• Process analysis including (value-added analysis, waste identification, bottleneck analysis)

• Cause and effect relationships including (statistical significance, practical significance, confidence levels)

4.2 Statistical Tools and Techniques

• Hypothesis testing methodology including (null hypothesis, alternative hypothesis, test statistics)

• Confidence intervals including (mean confidence intervals, proportion confidence intervals)

• Statistical power and sample size including (power calculations, alpha risk, beta risk)

• Multi-vari analysis including (positional, cyclical, temporal variation)

• Design of experiments fundamentals including (factorial designs, screening experiments, response surface methodology)

5. Improve Phase

5.1 Solution Development and Implementation

• Solution generation including (brainstorming, creative problem solving, benchmarking)

• Solution selection criteria including (impact assessment, feasibility analysis, cost-benefit analysis)

• Pilot testing including (pilot design, data collection, results analysis)

• Implementation planning including (project planning, resource allocation, timeline development)

• Change management including (communication strategy, training, resistance management)

5.2 Design of Experiments and Optimization

• Experimental design principles including (randomization, replication, blocking)

• Factorial experiments including (full factorial, fractional factorial, screening designs)

• Response surface methodology including (central composite design, optimization techniques)

• Statistical optimization including (response optimization, desirability functions)

• Robust design including (Taguchi methods, parameter design, tolerance design)

6. Control Phase

6.1 Statistical Process Control

• Control chart selection including (X-bar R charts, X-bar S charts, individual charts)

• Control chart implementation including (control limits, plotting rules, interpretation)

• Process monitoring including (SPC implementation, alarm systems, response protocols)

• Mistake-proofing including (poka-yoke, error prevention, detection systems)

• Process standardization including (standard operating procedures, work instructions, training)

6.2 Sustainability and Continuous Improvement

• Control plan development including (control methods, measurement systems, response plans)

• Process capability monitoring including (ongoing capability studies, trend analysis)

• Continuous improvement culture including (kaizen, suggestion systems, employee engagement)

• Project closure including (documentation, lessons learned, benefits realization)

• Knowledge transfer including (training development, best practice sharing, mentoring)

7. Lean Six Sigma Integration

7.1 Lean Principles and Tools

• Lean thinking including (value identification, waste elimination, flow optimization)

• Value stream mapping including (current state, future state, improvement opportunities)

• Waste identification including (muda, mura, muri, eight wastes)

• Lean tools including (5S, kanban, standard work, visual management)

• Lean Six Sigma synergy including (speed and quality, waste reduction, variation reduction)

7.2 Process Flow and Efficiency

• Process flow analysis including (cycle time, lead time, throughput)

• Bottleneck identification including (theory of constraints, capacity analysis)

• Takt time and demand analysis including (customer demand, production rate, capacity planning)

• Single piece flow including (batch size reduction, setup time reduction)

• Pull systems including (kanban systems, demand-driven production, inventory reduction)

8. Team Leadership and Project Management

8.1 Team Dynamics and Leadership

• Team formation including (team selection, role definition, team charter)

• Team leadership including (motivation, communication, conflict resolution)

• Meeting facilitation including (agenda setting, participation, decision making)

• Stakeholder management including (communication planning, expectation management)

• Change leadership including (change models, resistance management, adoption strategies)

8.2 Project Management Fundamentals

• Project planning including (work breakdown structure, scheduling, resource planning)

• Risk management including (risk identification, assessment, mitigation strategies)

• Communication management including (stakeholder communication, reporting, documentation)

• Quality management including (quality planning, quality assurance, quality control)

• Project monitoring including (progress tracking, milestone management, corrective actions)

9. Advanced Statistical Methods

9.1 Regression Analysis and Modeling

• Simple linear regression including (correlation analysis, regression equation, significance testing)

• Multiple regression including (model building, variable selection, model validation)

• Logistic regression including (binary outcomes, odds ratios, model interpretation)

• Polynomial regression including (curved relationships, model fitting, prediction)

• Regression diagnostics including (residual analysis, outlier detection, assumption checking)

9.2 Analysis of Variance and Comparative Studies

• One-way ANOVA including (between-group variation, within-group variation, F-test)

• Two-way ANOVA including (interaction effects, main effects, factor analysis)

• Non-parametric tests including (Mann-Whitney, Kruskal-Wallis, chi-square tests)

• Comparative studies including (before-after comparisons, control group analysis)

• Multi-variate analysis including (principal component analysis, cluster analysis)

10. Quality Tools and Problem-Solving Techniques

10.1 Quality Management Tools

• Seven basic quality tools including (check sheets, histograms, Pareto charts, cause-and-effect diagrams)

• Seven management tools including (affinity diagrams, tree diagrams, matrix diagrams)

• Statistical quality control including (acceptance sampling, quality audits, supplier quality)

• Quality function deployment including (house of quality, customer requirements, design specifications)

• Failure mode and effects analysis including (FMEA process, risk priority numbers, prevention strategies)

10.2 Problem-Solving Methodologies

• Systematic problem solving including (problem definition, analysis, solution development)

• Creative problem solving including (brainstorming techniques, lateral thinking, innovation)

• Decision-making tools including (decision matrices, cost-benefit analysis, risk assessment)

• Implementation strategies including (pilot testing, full implementation, monitoring)

• Verification and validation including (solution effectiveness, sustainability, improvement)

11. HSE in Quality Management

• Process safety management including (hazard identification, risk assessment, safety protocols)

• Environmental considerations including (environmental impact, sustainability, resource conservation)

• Occupational health and safety including (workplace safety, ergonomics, health protection)

• Regulatory compliance including (quality standards, safety regulations, environmental requirements)

• Risk management integration including (operational risk, safety risk, environmental risk)

12. Quality Assurance and Standards

• ISO 9001 quality management system including (quality policy, process approach, continuous improvement)

• Quality assurance principles including (prevention, detection, correction, improvement)

• Quality auditing including (internal audits, external audits, audit methodology)

• Supplier quality management including (supplier selection, evaluation, development)

• Quality documentation including (quality manual, procedures, work instructions, records)

13. Case Studies & Group Discussions

• Regional improvement projects from Middle East operations including (manufacturing processes, service delivery, administrative functions)

• Complex Six Sigma projects including (multi-departmental initiatives, cross-functional teams, organizational change)

• Statistical analysis exercises including (hypothesis testing scenarios, capability studies, control chart interpretation)

• Problem-solving simulations including (real-world challenges, team-based solutions, implementation planning)

• The importance of proper training in developing competent Six Sigma practitioners and achieving sustainable improvements