Lean Six Sigma training

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The Lean Six Sigma Yellow Bel is a professional who is well versed in the foundational elements of the Lean Six Sigma Methodology, who leads limited improvement projects and / or serves as a team member as a part of more complex improvement projects lead by a Certified Green Belt or Certified Black Belt, typically in a part-time role. 

A Lean Six Sigma Yellow Belt possesses a thorough understanding of the elementary aspects of the Lean Six Sigma Method including competence in the subject matters contained within the phases of Define, Measure, and Control (DMC) as defined by the IASSC Lean Six Sigma Yellow Belt Body of Knowledge™. 

A Lean Six Sigma Yellow Belt understands how to implement, perform, interpret and apply Lean Six Sigma in a skilled yet limited and / or supportive context. 

Course Outline – Yellow Belt 

Day 1 – Define Phase

• Understanding Six Sigma
• Six Sigma Fundamentals
• Selecting Projects suitable for LSS
• Elements of Waste and how to remove them

Day 2 – Measure Phase

• Process Discovery (Process Mapping)
• Six Sigma Statistics (understanding data)
• Measurement System Analysis (gaining assurance in the quality of your data)
• Process Capability (how capable is your process, and how much improvement is required)

Day 3 – Control Phase

• Lean Controls (sustaining your solutions using lean techniques)
• Defect Controls (preventing defects from occuring, or managing their impact)
• Six Sigma Control Plans (sustaining your solution using change management principles)

The Lean Six Sigma Green Belt is a professional who is well versed in the core to advanced elements of Lean Six Sigma Methodology, who leads improvement projects and / or serves as a team member as a part of more complex improvement projects lead by a Certified Black Belt, typically in a part-time role. 

A Lean Six Sigma Green Belt possesses a thorough understanding of all aspects of the Lean Six Sigma Method including competence in subject matters contained within the phases of Define, Measure, Analyze, Improve and Control (DMAIC) as defined by the IASSC Lean Six Sigma Green Belt Body of Knowledge™. 

A Lean Six Sigma Green Belt understands how to implement, perform, interpret and apply Lean Six Sigma at a high level of proficiency. 

Course Outline – Green Belt

Day 1 – Define Phase

• Understanding Six Sigma
• Six Sigma Fundamentals
• Selecting Projects suitable for LSS
• Elements of Waste and how to remove them

Day 2 – Measure Phase

• Process Discovery (Process Mapping)
• Six Sigma Statistics (understanding data)
• Measurement System Analysis (gaining assurance in the quality of your data)
• Process Capability (how capable is your process, and how much improvement is required)

Day 3 – Analyse Phase

• “X” Sifting (how to determine which inputs are having the greatest impact on your process)
• Inferential Statistics (understanding what sample data is telling you about the population)
• Introduction to Hypothesis Testing (proving or disproving the impact of a process input)
• Hypothesis Testing in detail for different data types

Day 4 – Improve Phase

• Process Modeling Regression (creating a simple mathematical relationship between process inputs and outputs)
• Advanced Process Modeling (creating complex mathematical relationships between process inputs and outputs)
• Designing Experiments (how to effectively design an experiment to test your potential solutions)

Day 5 – Control Phase

• Advanced experiments
• Advanced Capability Analysis
• Lean Controls (sustaining your solutions using lean techniques)
• Defect Controls (preventing defects from occurring, or managing their impact)
• Statistical process control (monitoring your solution)
• Six Sigma Control Plans (sustaining your solution using change management principles

 The  Lean Six Sigma Black Belt™  is a professional who is well versed in the Lean Six Sigma Methodology, who leads complex improvement projects, typically in a full-time capacity. 

A Lean Six Sigma Black Belt possesses a thorough understanding of all aspects of the Lean Six Sigma Method including a high-level of competence in the subject matters contained within the phases of Define, Measure, Analyse, Improve and Control (DMAIC) as defined by the IASSC Lean Six Sigma Black Belt Body of Knowledge™. 

A Lean Six Sigma Black Belt understands how to implement, perform, interpret and apply Lean Six Sigma at an advanced level of proficiency. 

IASSC Universally Accepted Lean Six Sigma Body of Knowledge for Black Belts

1.0 Define Phase

• 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
  a. including DPU, DPMO, FTY, RTY Cycle Time; deriving these 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 Understanding Lean
  1.4.2 The History of Lean
  1.4.3 Lean & Six Sigma
  1.4.4 The Seven Elements of Waste
  a. Overproduction, Correction, Inventory, Motion, Over-processing, Conveyance, Waiting.
  1.4.5 5S
  a. Straighten, Shine, Standardize, Self-Discipline, Sort

2.0 Measure Phase

• 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 Six Sigma Statistics
  2.2.1 Basic Statistics
  2.2.2 Descriptive Statistics
  2.2.3 Normal Distributions & Normality
  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

3.0 Analyze Phase

• 3.1 Patterns of Variation
  3.1.1 Multi-Variance 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
  3.3.4 Types of Hypothesis Test
• 3.4 Hypothesis Testing with Normal Data
  3.4.1 1 & 2 sample t-tests
  3.4.2 1 sample variance
  3.4.3 One Way ANOVA
  a. Including Tests of Equal Variance, Normality Testing and Sample Size calculation, performing tests and interpreting results.
• 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)
  a. Including Tests of Equal Variance, Normality Testing and Sample Size calculation, performing tests and interpreting results.

4.0 Improve Phase

• 4.1 Simple Linear Regression
  4.1.1 Correlation
  4.1.2 Regression Equations
  4.1.3 Residuals 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 Analysis
  4.2.5 Data Transformation, Box Cox
• 4.3 Designed Experiments
  4.3.1 Experiment 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

5.0 Control Phase

• 5.1 Lean Controls
  5.1.1 Control Methods for 5S
  5.1.2 Kanban
  5.1.3 Poka-Yoke (Mistake Proofing)
• 5.2 Statistical Process Control (SPC)
  5.2.1 Data Collection for SPC
  5.2.2 I-MR Chart
  5.2.3 Xbar-R Chart
  5.2.4 U Chart
  5.2.5 P Chart
  5.2.6 NP Chart
  5.2.7 Xbar-S Chart
  5.2.8 CuSum Chart
  5.2.9 EWMA Chart
  5.2.10 Control Methods
  5.2.11 Control Chart Anatomy
  5.2.12 Subgroups, Impact of Variation, Frequency of Sampling
  5.2.13 Center Line & Control Limit Calculations
• 5.3 Six Sigma Control Plans
  5.3.1 Cost Benefit Analysis
  5.3.2 Elements of the Control Plan
  5.3.3 Elements of the Response Plan