1.1 What is Six Sigma
Six Sigma is a structured, data-driven methodology used to improve process performance by reducing variation and eliminating defects. A defect is defined as any outcome that does not meet customer requirements. Six Sigma aims for a performance level of no more than 3.4 defects per million opportunities (DPMO).
1.2 History and Evolution
- Originated at Motorola in the 1980s
- Popularized by General Electric under Jack Welch
- Adopted across manufacturing, IT, healthcare, banking, and service industries
1.3 Benefits of Six Sigma
- Improved customer satisfaction
- Reduced cost of poor quality (COPQ)
- Improved process consistency
- Data-based decision making
- Cultural shift toward continuous improvement
1.4 Six Sigma Roles and Responsibilities
- Champion: Provides direction, removes roadblocks
- Master Black Belt: Expert coach and trainer
- Black Belt: Leads complex projects full time
- Green Belt: Leads small-to-medium projects part time
- Team Member: Supports project execution
1.5 Lean vs Six Sigma
- Lean: Focus on waste elimination and speed
- Six Sigma: Focus on variation reduction
- Lean Six Sigma: Combines both approaches
1.6 DMAIC Overview
- Define
- Measure
- Analyze
- Improve
- Control
2.1 Objective of Define Phase
To clearly define the problem, project scope, goals, and customer requirements.
2.2 Project Selection
- Aligned with business objectives
- Measurable impact (cost, quality, delivery)
- Manageable scope (3–6 months)
2.3 Project Charter
Key Elements:
- Business Case
- Problem Statement
- Goal Statement (SMART)
- Project Scope
- Timeline and Milestones
- Team Members
Example (Manufacturing):
- Problem: 8% rejection in shaft diameter exceeding tolerance
- Goal: Reduce rejection to <2% within 4 months
- Business Impact: ₹18 lakhs annual COPQ reduction
2.4 Voice of the Customer (VOC)
VOC represents customer needs and expectations.
VOC Collection Methods:
- Surveys
- Interviews
- Complaints data
- Warranty claims
Example (Service): Customer complaints about long turnaround time (TAT)
2.5 CTQ (Critical to Quality)
CTQs translate customer needs into measurable requirements.
VOC → CTQ Example:
- VOC: "Delivery is too slow."
- CTQ: Order processing time ≤ 24 hours
2.6 SIPOC Diagram
- Suppliers
- Inputs
- Process
- Outputs
- Customers
When to use: Early Define phase
Common mistakes: Too much detail; unclear process boundaries
2.7 High-Level Process Mapping
Provides an overview of the current process flow.
2.8 Stakeholder Analysis
Identifies key stakeholders and their influence on the project.
Deliverables of Define Phase:
- Approved Project Charter
- SIPOC Diagram
- CTQ Tree
3.1 Objective of Measure Phase
To understand current process performance and establish a baseline.
3.2 Types of Data
- Continuous data (e.g., time, weight)
- Discrete data (e.g., defect count)
3.3 Data Collection Plan
Includes what data to collect, how, when, and by whom.
3.4 Operational Definitions
Ensures consistency and clarity in measurement.
3.5 Sampling Methods
- Random sampling
- Stratified sampling
- Systematic sampling
3.6 Measurement System Analysis (MSA)
Evaluates accuracy and precision of measurement systems.
Key Concepts:
- Bias
- Linearity
- Stability
- Gage R&R (Variable and Attribute)
3.7 Basic Statistics
- Mean, Median, Mode
- Range
- Variance
- Standard Deviation
3.8 Process Capability
- Cp, Cpk (short-term)
- Pp, Ppk (long-term)
3.9 Graphical Analysis
- Histogram
- Pareto Chart
- Run Chart
- Box Plot
4.1 Objective of Analyze Phase
To identify and validate root causes of defects and variation.
4.2 Process Mapping (Detailed)
Helps identify non-value-added steps.
4.3 Root Cause Analysis Tools
- Cause-and-Effect Diagram
- 5 Why Analysis
- Brainstorming
4.4 Failure Mode and Effects Analysis (FMEA)
- Severity
- Occurrence
- Detection
- Risk Priority Number (RPN)
4.5 Hypothesis Testing Basics
- Null Hypothesis (H0)
- Alternate Hypothesis (H1)
- Type I and Type II Errors
- Confidence Level and P-value
4.6 Common Hypothesis Tests
- 1-Sample t-Test
- 2-Sample t-Test
- Paired t-Test
- Chi-Square Test
- One-Way ANOVA (overview)
4.7 Correlation and Regression
Used to understand relationships between variables.
5.1 Objective of Improve Phase
To develop, test, and implement solutions that address root causes.
5.2 Solution Generation Techniques
- Brainstorming
- Benchmarking
5.3 Solution Selection Tools
- Pugh Matrix
- Cost-Benefit Analysis
5.4 Lean Tools for Green Belts
- 5S
- Kaizen
- Waste Identification (DOWNTIME)
5.5 Pilot Implementation
Testing solutions on a small scale.
5.6 Risk Analysis
Identifying and mitigating risks before full implementation.
5.7 Introduction to DOE
- Factors and Levels
- Full and Fractional Factorial (conceptual)
6.1 Objective of Control Phase
To sustain improvements and prevent regression.
6.2 Control Plan
Defines how the process will be monitored and controlled.
6.3 Standardization
- SOPs
- Work Instructions
6.4 Visual Controls
Make process status visible and clear.
6.5 Control Charts
- X̄-R and X̄-S Charts
- Individuals and Moving Range (I-MR)
- Attribute Charts (p, np, c, u)
6.6 Reaction Plan
Actions to be taken when a process goes out of control.
6.7 Project Closure
- Benefits validation
- Lessons learned
- Handover to process owner
Module 7: Minitab for Six Sigma (Overview)
7.1 Introduction to Minitab
7.2 Data Entry and Management
7.3 Statistical Analysis Using Minitab
- Descriptive statistics
- Capability analysis
- Hypothesis testing
- Control charts
7.4 Interpreting Minitab Output
8.1 Project Requirements
- One completed DMAIC project
- Financial and operational impact
8.2 Tollgate Reviews
8.3 Project Presentation and Certification
Appendix
- Key Six Sigma formulas
- Glossary of terms
- Sample templates (Charter, SIPOC, Control Plan)
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