This course is designed to develop Black Belt competencies which are required to address critical & chronic problems originated from business operations. This course concentrates on application of Six Sigma DMAIC methodology and related intermediate & advanced Quality/Statistical tools to ensure defined problems are resolved and improvement project goals are sustainable. The ultimate goal of this course is to develop Black Belts who serve as effective Project Leaders in accomplishing improvement projects.
Learning Outcome
- Master the skills of intermediate-advanced Quality/Statistical tools application
- Lead complex Six Sigma projects independently
- Expertly manage all phases of the DMAIC methodology
- Obtain in-depth knowledge in quality/statistical tools to mentor and coach Green Belts and Yellow Belts in problem solving/process improvement
- Facilitate change management within the organisation
- Align Six Sigma project with organisational strategies and goals and drive process excellence
- Enhance leadership and project management skills
Methodology
The methods used here are the combination of lectures, interactive discussion, demo and exercises (individual/group) that mainly focus on a real project case study.
Pre-requisite
A sound technical background and minimum 5 years of working experience in any business operation discipline or Six Sigma Green Belt certified.
Duration
10 Days
Target Group (who should attend)
- Executives, Engineers and Managers from any organisations including Core Operation/Support/Transactional group either in manufacturing or service industries
Day 1: Define
- Introduction
- Six Sigma Overview
- What is Six Sigma?
- How Six Sigma DMAIC Work
- Focus Area of Improvement
- Voice of Customer (VoC) Analysis
- Project Charter
- Elements of Project Charter
- Guidelines for Drafting Effective Project Charter
- Process Mapping
- Process Characteristics
- Process Mapping Techniques & Steps
Day 2-4: Measure
- Determine Variables to Measure
- Input, Process & Output Variable Identification
- Operational Definition
- Data Collection Plan
- Basic Data Analysis
- Introduction to Process Variation Management
- Basic Numerical & Graphical Analysis Tools
- Measurement System Analysis (MSA)
- Overview of MSA
- Measurement System Variation & Analysis
- MSA for Variable Data
- Gage Repeatability & Reproducibility (GR&R)
- Crossed
- Expanded
- Bias & Linearity Study
- Stability Study
- MSA for Attribute Data
- Attribute Agreement Analysis (AAA)
- Attribute Agreement Analysis
- Acceptance Criteria, Steps & Analysis
- Baseline Performance Establishment
- Data Distribution & Process Stability
- Process Capability Analysis for Attribute and Variable Data
- Definition, Interpretation & Acceptance
- Process Variation & Strategies for Improvement
- Dealing with Non-Normal data & Countermeasure
Day 5-8: Analyse
- Potential Root Causes Identification
- Basic Quality Tools Analysis
- Source of Variation (SoV) Analysis
- Root Causes Validation
- Central Limit Theorem
- Confidence Interval
- Hypothesis Testing Concept
- Hypothesis Testing Tools, Steps, Analysis & Conclusion
- Parametric Tests
- Non-Parametric Tests
- Correlation & Simple Linear Regression Analysis
- Multiple Regression Analysis
- Optimisation
- Design of Experiment (DoE) Introduction
- Terminology and Key Basics
- 2 Levels Fractional Factorial Design
- 2 Levels Full Factorial Design
- Response Surface Methodology
Day 9: Improve
- Solutions Generation & Selection
- Solution Generation Techniques & Prioritisation
- Pilot Run Plan & Change Management
- Pilot Run Process & Evaluation
- Potential Change Management
- Risk Management
- Overview of Failure Mode & Effect Analysis (FMEA)
- Guidelines for FMEA Preparation & Close-loop
Day 10: Control
- Control Plan
- Elements of Control Plan
- Guidelines for Drafting a Control Plan
- Control Methods
- Strategy for Control
- Selection of Effective Control Methods
- Project Closure & Summary
- Project Performance Review
- Standardisation & Replication
- Project Closure & Recognition
