Lean Six Sigma Black Belt Certificate: Course Overview Black Belts are at the core of every Six Sigma Implementation. Black Belts are the real “change agents” in a Lean Six Sigma initiative. They will continually work towards institutionalizing the effective use of both Lean and Six Sigma tools throughout their organization. Our program follows the DMAIC model and teaches the soft skills, along with statistical tools required to effectively lead projects and obtain bottom line benefits. The program consists of four modules. Practical application of training is performed through each participant’s project, which will be reviewed throughout the program by a committee Objectives: Upon the completion of process improvement Lean Six Sigma Black Belt program, participants shall be able to: 1. Learn concepts, tools, skills and techniques necessary for application of Lean Six Sigma methodology. 2. Master how to apply statistical methods for business process improvement using Minitab software. 3. Communicate the benefits of lean Six Sigma as a business strategy across the organization. 4. Select and coordinate with top management successful lean Six Sigma projects and project teams. 5. Become effective team leaders in the six sigma projects. 6. Act as a change agent in the organization and learn how to significantly increase profitability through Six Sigma projects.
Who Should Attend? Those interested in obtaining advanced Six Sigma training including: Quality Professionals , Business Process Improvement professionals, Improvement Managers,Process Professionals , R&D, Process Engineers, IT Professionals, Performance Managers and supply chain professionals….. Course contents: Define Phase The Basics of Six Sigma Meanings of Six Sigma General History of Six Sigma & Continuous Improvement Deliverables of a Lean Six Sigma Project The Problem Solving Strategy Y = f(x) Voice of the Customer, Business and Employee Six Sigma Roles & Responsibilities The Fundamentals of Six Sigma Defining a Process Critical to Quality Characteristics (CTQ’s) Cost of Poor Quality (COPQ) Pareto Analysis (80:20 rule) Basic Six Sigma Metrics DPU, DPMO, FTY, RTY Cycle Time Selecting Lean Six Sigma Projects Building a Business Case & Project Charter Developing Project Metrics Financial Evaluation & Benefits Capture The Lean Enterprise Understanding Lean The History of Lean Lean & Six Sigma The Seven Elements of Waste 5S
Measure Phase Process Definition
Cause & Effect / Fishbone Diagrams
Process Mapping, SIPOC, Value Stream Map
X-Y Diagram
Failure Modes & Effects Analysis (FMEA)
Six Sigma Statistics
Basic Statistics
Descriptive Statistics
Normal Distributions & Normality
Graphical Analysis
Measurement System Analysis
Precision & Accuracy
Bias, Linearity & Stability
Gage Repeatability & Reproducibility
Variable & Attribute MSA
Process Capability
Capability Analysis
Concept of Stability
Attribute & Discrete Capability
Monitoring Techniques
Analyze Phase
Patterns of Variation
Multi-Vari AnalysisClasses of Distributions
Inferential Statistics
Understanding Inference
Sampling Techniques & Uses
Central Limit Theorem
Hypothesis Testing
General Concepts & Goals of Hypothesis Testing
Significance; Practical vs. Statistical
Risk; Alpha & Beta
Types of Hypothesis Test
Hypothesis Testing with Normal Data
1 & 2 sample t-tests
1 sample variance
One Way ANOVA
Hypothesis Testing with Non-Normal Data
Mann-Whitney
Kruskal-Wallis
Mood’s Median
Friedman
1 Sample Sign
1 Sample Wilcoxon
One and Two Sample Proportion
Chi-Squared (Contingency Tables)
Including Tests of Equal Variance, Normality Testing and Sample Size calculation, performing tests and interpreting results. Improve Phase
Simple Linear Regression
Correlation
Regression Equations
Residuals Analysis
Multiple Regression Analysis
Non- Linear Regression
Multiple Linear Regression
Confidence & Prediction Intervals
Residuals Analysis
Data Transformation, Box Cox
Designed Experiments
Experiment Objectives
Experimental Methods
Experiment Design Considerations
Full Factorial Experiments
2k Full Factorial Designs
Linear & Quadratic Mathematical Models
Balanced & Orthogonal Designs
Fit, Diagnose Model and Center Points
Fractional Factorial Experiments
Designs
Confounding Effects
Experimental Resolution
Control Phase
Lean Controls
Control Methods for 5S
Kanban
Poka-Yoke (Mistake Proofing)
Statistical Process Control (SPC)
Data Collection for SPC
I-MR Chart
X bar-R Chart
U Chart
P Chart
NP Chart
Xbar-S Chart
CuSum Chart
EWMA Chart
Control Methods
Control Chart Anatomy
Subgroups, Impact of Variation, Frequency of Sampling
Classes of Distributions
