Week 1: Fundamentals of Six Sigma and DMAIC Methodology
Day 1: Introduction to Six Sigma and Statistical Foundations
What is Six Sigma?
- Overview of Six Sigma methodology
- The goal of Six Sigma: Process improvement and defect reduction
- The philosophy of Six Sigma: DMAIC (Define, Measure, Analyse, Improve, Control)
The Normal Distribution
- Key properties of the Normal distribution
- Understanding the significance of standard deviation and mean
- Z-scores and probabilities
- The role of the Normal distribution in process improvement
Central Limit Theorem (CLT)
- Explanation of the CLT and its significance in process analysis
- Sampling distribution and its implications for statistical testing
The t Distribution
- When and why to use the t-distribution
- Differences between the t and normal distributions
Day 2: Define Phase – Project Initiation and SIPOC Mapping
Define Stage in DMAIC
- Project scope and goals: Defining what constitutes success in the project
- Project Charter: How to develop a clear project charter that outlines objectives, team roles, and goals
Creating a SIPOC Diagram
- Understanding SIPOC (Suppliers, Inputs, Process, Outputs, Customers)
- How to map a process at a high level to identify critical inputs and outputs
Critical to Quality Measure (CTQ)
- Understanding CTQ and how it aligns with customer requirements
- Techniques to translate customer needs into measurable outputs
Dimensions of Service and Product Quality
- The 5 dimensions of service quality: Tangibles, Reliability, Responsiveness, Assurance, Empathy
- The 4 dimensions of product quality: Performance, Features, Reliability, Durability
Quality Function Deployment (QFD)
- Introduction to QFD and the House of Quality
- Translating customer needs into product specifications
Day 3: Measure Phase – Data Collection, Variation, and Process Capability
Key Measures for Process Performance
- Key performance indicators (KPIs) and how to define them for specific projects
- Understanding process metrics: Cycle time, throughput, defect rates, etc.
Data Collection Planning and Execution
- How to plan data collection to ensure it’s systematic and unbiased
- Techniques to ensure data accuracy and consistency
Displaying Process Variation
- Tools for visualizing variation: Histograms, box plots, scatter plots
Measurement System Analysis (Gage R&R)
- How to assess the accuracy and reliability of measurement systems
- Gage R&R study: Precision vs. accuracy
Process Capability Analysis (Advanced)
- Calculating process capability indices (Cp, Cpk)
- Understanding process performance using sigma levels
Sigma Performance and Calculating Sigma Levels
- Calculating sigma level of a process
- The importance of achieving higher sigma levels for improved quality
Day 4: Analyse Phase – Identifying Root Causes and Analysing Data
Stratification Techniques
- Breaking down data by categories to identify patterns and trends
Tree Diagrams
- Using tree diagrams for breaking down complex problems into manageable components
Correlation and Simple Linear Regression
- Understanding correlation vs causation
- Using linear regression to model relationships between variables
Statistical Process Control (SPC)
- The role of SPC in monitoring process behaviour
- Types of control charts: X-bar, R-chart, p-chart, etc.
T-test
- Performing t-tests to compare sample means
- Understanding p-values and interpreting statistical significance
One-way and Two-way ANOVA
- Performing ANOVA to analyse variance across multiple groups or factors
5 Whys and Fishbone Diagram (Ishikawa)
- Root cause analysis using the 5 Whys technique
- Fishbone diagrams for visualizing potential causes of problems
Cause and Effect Analysis
- Techniques to identify root causes of process issues
Failure Mode and Effects Analysis (FMEA)
- Identifying and prioritizing potential failure modes in processes
- Using FMEA to assess risks and prioritize actions
Day 5: Operational Analysis and Lean Tools
Standard Operating Procedures (SOPs)
- Importance of SOPs in maintaining process consistency and quality
Capacity Utilization
- Analysing and improving process capacity
- How to calculate and optimize capacity utilization
Lean Accounting
- Introduction to lean accounting principles
- Cost structures, value stream costing, and financial benefits of Lean
Stakeholder Analysis
- Identifying and managing key stakeholders in Six Sigma projects
- Techniques to ensure stakeholder engagement and buy-in
End of Week 1 Exam (3 hours)
- Written test covering all topics from Week 1
- Multiple-choice, short answer, and scenario-based questions
Week 2: Advanced Analysis, Improvement Techniques, and Control Tools
Day 8: Advanced Data and Process Analysis
Data and Process Analysis Techniques
- Advanced techniques for deeper process analysis
Root Cause Analysis
- Methods for analysing and identifying root causes in complex problems
Quantifying the Gap/Opportunity
- How to quantify performance gaps and assess opportunities for improvement
Hypothesis Testing and Power of Tests
- Conducting hypothesis tests to validate assumptions
- Understanding statistical power and sample size determination
Descriptive Tests and Multiple Regression
- Using descriptive statistics to summarize data
- Multiple regression analysis for complex models
Design of Experiments (DOE)
- Planning and conducting experiments to optimize process variables
- Introduction to factorial designs and response surface methodology
Taguchi Methods
- Robust design techniques to minimize variation in product and process performance
Day 9: Improve Phase – Solution Generation and Testing
Generating and Testing Solutions
- Brainstorming solutions to process issues
- Using creativity and structured methods (e.g., TRIZ) to generate innovative solutions
Selecting the Best Solutions
- Criteria for evaluating and selecting optimal solutions based on cost, feasibility, and impact
Designing Implementation Plans
- Developing detailed plans for implementing improvement solutions
Time Series Analysis
- Analysing trends and patterns over time to make data-driven decisions
Cross-Correlation
- Analysing relationships between time-series data
Binary Logistic Regression
- Predicting categorical outcomes (yes/no) using logistic regression
Chi-Square Test
- Testing relationships between categorical variables using chi-square tests
Day 10: Control Phase – Ensuring Sustainability
Monitoring Plans
- Developing ongoing monitoring and control plans to track improvements
Process Standardization
- Standardizing processes to ensure consistent results
Response Plans
- Creating response plans to address any issues or deviations from expected outcomes
Transfer of Ownership
- Ensuring successful transition of process improvements to operational teams
Reliability Engineering
- Techniques for ensuring process reliability and minimizing failure rates
Total Productive Maintenance (TPM)
- TPM principles for improving machine reliability and uptime
Poka Yoke
- Designing error-proofing mechanisms to prevent defects
Cellular Manufacturing
- Implementing cellular manufacturing layouts to optimize flow and reduce waste
Characteristics of Lean
- The key principles of Lean: Value, value stream mapping, pull systems, flow, and continuous improvement
End of Week 2 Exam (3 hours)
- Written test covering all topics from Week 2
- Multiple-choice, short answer, and case-based questions
