Detailed Course Description
The Six Sigma methodology is a systematic application that is focused on achieving significant financial results and increasing customer satisfaction. When properly deployed on carefully selected business projects, this methodology can lead to a significant reduction—and in many cases, elimination—of defects, process waste, and out-of-control processes, which translate into dramatic business gains.
The JK Michaels IASSC Lean Six Sigma Black Belt course provides a comprehensive and disciplined model for improvement, aligned and accredited to the Universal Body of Knowledge Standard of International Association of Six Sigma Certification ,USA. Every participant will learn how to meet his or her company’s business objectives through the recognized DMAIC process. Attendees will learn how to direct Lean Six Sigma projects and obtain the maximum improvements from the learned techniques and skills. This course is conducted in three five-day sessions with four weeks between sessions.
Learned skills are practiced and applied through individual and team exercises, as well as to the individual projects. Participants will be able to apply the concepts learned in the class to a business improvement project assigned to them by their management sponsor.
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Achieve significant improvements in critical business processes.
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Apply statistical and problem solving tools to an improvement project brought to class on the first day.
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Reduce process variation.
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Eliminate waste and defects by applying lean and Six Sigma.
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Collect, analyze, and quantify data that enable process improvements.
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Learn how to execute the Six Sigma methodology.
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Establish and define process capability.
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Identify and eliminate dominant process variation sources.
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Characterize and optimize processes by computing and applying statistical techniques.
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Design, simulate, and execute designed experiments that depict validated improvement.
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Learn how to plan and implement process control to hold project gains.
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Pass the IASSC Black belt certification at first sitting
WEEK ONE
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
WEEK TWO
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
WEEK THREE(EXAM WEEK)
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
5.6.4 MINITAB 16 PRACTICE EXERCISES
5.6.5 EXAM DAY
