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The ADMD Six Sigma Black Belt Self-Training Course (Version 2.1) is comprised of two PowerPoint files. The first file incudes 205 PowerPoint slides. It is designed to help the student gain the necessary knowledge to receive Black Belt Certification. The second file is a subsection of key statistical concepts and includes an additional 832 PowerPoint slides.
Unlike other programs, our instructions maintain the Six Sigma ideology: maximum gains with least amount of effort. Our Six Sigma self-training kit is specifically designed to help you effectively learn and quickly take advantage of what Six Sigma really has to offer.
This 7-day training program is best described as follows:
- Teaches how to think, as opposed to just teaches tools
- Readily applies to any real-world business situation
- Turn-key and focused on immediate gains
- Focus on quality tools which achieve most gains and clearly guide their usage
- Introduce only the statistics needed for most situations
- Can be completed in matter of days
- Concise and uses layman examples to aid quick comprehension and long- term understanding
- Self-paced training, without the need for an expert
- Avoids the formalities that actually slow improvements Affordable
- Simple, comprehensive, right to the point, and effective
Introduction to Improvement
Overview of DMAIC
Define
- Problem Statement
- Voice of customers
- CTQ Tree
- SIPOC
- Process Mapping
- Prioritization Matrix
- Measurement Issues Introduction
- Control Charts Introduction
- Capabilities Analysis Introduction
- 5 Why’s/Fault Tree/Cause&Effect Analysis
- FMEA
- DOE Introduction
- Supporting Topics (in depth)
- Human Error Proofing
- Visual Workplace
- Project Management
- Key Statistical Concepts
The Key Statistical Concepts Outline
- Nature of Data
- Frequency Distribution
- Samples & Population
- Measures of Location
- Measures of Dispersion
- Probability
- Probability Distribution
- Sampling Distribution
- Statistical Intervals
- Foundations of SPC
- Understanding of Variation
- Control Charts Theory
- Process Capability
- Design of Experiments





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