JK Michaels IASSC-Lean Six Sigma Green Belt
Lean Six Sigma is a methodology and toolkit that can drastically improve your business processes.
Whether you are in manufacturing or services, Lean Six Sigma can help you to eliminate waste, reduce defects, increase quality and improve your profitability by focusing on process areas that add value.
This hands-on Lean Six Sigma Green Belt training course provides a through working understanding of the Define Measure Analyse Improve and Control (DMAIC) model which is the foundation for Six Sigma projects.
Delegates leave our Lean Six Sigma Green Belt training course with the knowledge required to identify, scope and run effective Six Sigma projects that drive continuous improvement for your organisation.
Our Lean Six Sigma Green Belt Training curriculum is accredited and aligns with International Association of Six Sigma Certification Universal Body Of Knowledge standard of certification for Green Belts. As a Six Sigma Green Belt training graduate, you will not only improve your career, but change all aspects of your life. Enroll today and be on track to get your Six Sigma Green Belt certification in no time.
DEFINE
1.1 The Basics of Six Sigma
1.1.1 Meanings of Six Sigma
1.1.2 General History of Six Sigma & Continuous Improvement
1.1.3 Deliverables of a Lean Six Sigma Project
1.1.4 The Problem Solving Strategy Y = f(x)
1.1.5 Voice of the Customer, Business and Employee
1.1.6 Six Sigma Roles & Responsibilities
1.2 The Fundamentals of Six Sigma
1.2.1 Defining a Process
1.2.2 Critical to Quality Characteristics (CTQ’s)
1.2.3 Cost of Poor Quality (COPQ)
1.2.4 Pareto Analysis (80:20 rule)
1.2.5 Basic Six Sigma Metrics
1.3 Selecting Lean Six Sigma Projects
1.3.1 Building a Business Case & Project Charter
1.3.2 Developing Project Metrics
1.3.3 Financial Evaluation & Benefits Capture
1.4 The Lean Enterprise
1.4.1 Basics of Lean
1.4.2 History of Lean
1.4.3 Lean & Six Sigma Integration
1.4.4 The Seven Elements of Waste
1.4.5 5S - Straighten, Shine, Standardize, Self-Discipline, Sort
MEASURE
2.1 Process Definition
2.1.1 Cause & Effect / Fishbone Diagrams
2.1.2 Process Mapping, SIPOC, Value Stream Map
2.1.3 X-Y Diagram
2.1.4 Failure Modes & Effects Analysis (FMEA)
2.2 Lean Six Sigma Statistics
2.2.1 Basic Applied Statistics
2.2.2 Descriptive Statistics
2.2.3 Distributions
2.2.4 Graphical Analysis
2.3 Measurement System Analysis
2.3.1 Precision & Accuracy
2.3.2 Bias, Linearity & Stability
2.3.3 Gage Repeatability & Reproducibility
2.3.4 Variable & Attribute MSA
2.4 Process Capability
2.4.1 Capability Analysis
2.4.2 Concept of Stability
2.4.3 Attribute & Discrete Capability
2.4.4 Monitoring Techniques
ANALYZE
3.1 Patterns of Variation
3.1.1 Multi-Vari Analysis
3.1.2 Classes of Distributions
3.2 Inferential Statistics
3.2.1 Understanding Inference
3.2.2 Sampling Techniques & Uses
3.2.3 Central Limit Theorem
3.3 Hypothesis Testing
3.3.1 General Concepts & Goals of Hypothesis Testing
3.3.2 Significance; Practical vs. Statistical
3.3.3 Risk; Alpha & Beta
3.4 Hypothesis Testing with Normal Data
3.4.1 1-sample & 2 sample t-tests
3.4.2 One-Way ANOVA
3.4.3 Two-Way ANOVA
3.5 Hypothesis Testing with Non-Normal Data
3.5.1 Mann-Whitney
3.5.2 Kruskal-Wallis
3.5.3 Mood’s Median
3.5.4 Friedman
3.5.5 1 Sample Sign
3.5.6 1 Sample Wilcoxon
3.5.7 One and Two Sample Proportion
3.5.8 Chi-Squared (Contingency Tables)
IMPROVE
4.1 Simple Linear Regression
4.1.1 Correlation
4.1.2 Regression Equations
4.1.3 Residuals Diagnostics Analysis
4.2 Multiple Regression Analysis
4.2.1 Non-Linear Regression
4.2.2 Multiple Linear Regression
4.2.3 Confidence & Prediction Intervals
4.2.4 Residuals Diagnostics Analysis
4.2.5 Data Transformation, Box Cox Technique
4.3 Designed Experiments
4.3.1 Experimental Objectives
4.3.2 Experimental Methods
4.3.3 Experiment Design Considerations
4.4 Full Factorial Experiments
4.4.1 2k Full Factorial Designs
4.4.2 Linear & Quadratic Mathematical Models
4.4.3 Balanced & Orthogonal Designs
4.4.4 Fit, Diagnose Model and Center Points
4.5 Fractional Factorial Experiments
4.5.1 Designs
4.5.2 Confounding Effects
4.5.3 Experimental Resolution
4.6 Advanced Experiments
4.6.1 Steepest Ascent Analysis
CONTROL
5.1 Lean Controls
5.3.1 Control Methods for 5S
5.3.2 Kanban (Pull Systems)
5.3.3 Poka-Yoke (Mistake Proofing)
5.2 Statistical Process Control (SPC)
5.4.1 Data Collection for SPC
5.4.2 I-MR Chart
5.4.3 Xbar-R Chart
5.4.4 U Chart
5.4.5 P Chart
5.4.6 NP Chart
5.4.7 Xbar-S Chart
5.4.8 CumSum Chart
5.4.9 EWMA Chart
5.4.10 Control Methods
5.3 Six Sigma Control Plans
5.6.1 Cost Benefit Analysis
5.6.2 Elements of the Control Plan
5.6.3 Elements of the Response Plan
