Process Optimization: Management Review and Update
The power of statistics tools for quality improvement has been well established. Among these tools, Design of Experiments has been commonly recognized as the most important in bringing about the best in product and process performance in realistic industrial environments. In the past three decades, various versions of Design of Experiments has been advocated for the quality profession, such as Shainin Techniques, Taguchi Methods, Robust Design and Six Sigma, each having its share of advocates, critiques, and controversies. It is therefore important that the various "brands" of Design of Experiments and Quality Engineering be well understood before an organization decides to adopt a particular one for implementation. This short course will cater to managers and engineers who do not have the time for the technical details but would like to acquire a good understanding and update of the essential principles and relative merits of the various statistical approaches available for quality excellence. No prior knowledge of Statistics is assumed.
Managers overseeing R & D, product, process, operations and quality functions, also engineers seeking an up-to-date review of this subject. No prior training in Statistics is assumed.
Participants are encouraged to bring their "problems" to class for discussion and possible solutions.
1 day (9 am - 5 pm)
Statistical approach to quality and reliability
From SPC to Six Sigma
Application of Shainin Techniques
Multi-factor studies in industry
Mechanics of experimental design
Screening, characterization, and optimization
Sequential experimentation strategies
Mutli-input, multi-output analysis
Integration with QFD and FMEA
Taguchi Methods: concepts and procedures
Manufacturing and environmental noise management
Robust design principles and examples
Potential and limitations of Taguchi Methods
Process optimality identification and tracking
Six Sigma: what, why, when, how, where, who
Phases and techniques in a Six Sigma project
Trends in Quality Engineering