The training is organized around the five main phases of the Six Sigma Process Improvement Roadmap: Define, Measure, Analyze, Improve, and Control (DMAIC). Candidates participate in four training sessions, with at least three weeks in between, allowing them to apply the material learned to their project.
For project-based training, project reviews during each training session maintain project focus. In this case, it is the responsibility of the Champion to ensure that their Belt candidates arrive at class with a proper project charter.
Optional: In between training, QSG can provide coaching and guidance to ensure that the methodology and roadmap are used appropriately. This is a separate service provided apart from this 160-hour training program.
Who Should Attend
This course is designed for process, product, and quality engineers who will lead project teams to achieve quality, yield, and productivity improvements. The Black Belts will normally lead complex production process related problems which may transcend across several processes and/or departments within an organization.
Through training, participants will:
- Know the process improvement roadmap DMAIC to systematically define, measure, analyze, improve, and finally control the process
- Know how to identify projects and define the project charter
- Achieve an in-depth understanding of the knowledge based and data based (statistical) methods and tools to characterize and baseline the process
- Be able to perform Multi-Vari studies to determine the various sources of noise in the process
- Be able to design experiments to screen, characterize, and optimize the process with respect to controllable process factors
- Know about various control methods to ensure that the improvement achieved is sustained
||Introduction to Six Sigma
Project Scoping and Chartering
Cause and Effect Matrix
Introduction to Minitab or JMP
Basic Quality Tools
Central Limit Theorem
Introduction to SPC
Variables & Attribute Measurement System Analysis (MSA)
Initial Control Plan
||Review of Week 1
Introduction to Multi-Vari Study
One and Two Sample Comparisons
Three or more Sample Comparison
Multiple Linear Regression
Chi Square Test
Non-Parametric Tests for Medians
Sample Size Determination
Failure Mode and Effects Analysis
||Review of Week 2
Logistic Regression for Attribute Response
Introduction to DOE and Attributes of a well-designed experiment
General Factorial Designs
Fractional Factorial Screening Designs
||Review of Week 3
Designs with Center points
Introduction to Response Surface Methods
Multiple Response Optimization
Introduction to Taguchi Methods
Total Control Methodology
Final Control Plan
Instructor-led class training, with opportunities to practice learned skills using prepared data, live demonstrations, and data collected real time in class
Minitab or JMP Statistical Software
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