Six Sigma is a widely used methodology for measuring and improving an organization s operational performance through a rigorous analysis of its practices and systems.
This book presents a series of papers providing a systematic roadmap for implementing Six Sigma, following the DMAIC (Define, Measure, Analyse, Improve and Control) phased approach. Motivated by actual problems, the authors offer insightful solutions to some of the most commonly encountered issues in Six Sigma projects, such as validation of normality, experimentation under constraints and statistical control of complex processes. They also include many examples and case studies to help readers learn how to apply the appropriate techniques to real–world problems.
- Provides a comprehensive introduction to Six Sigma, with a critical strategic assessment and a SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis.
- Presents some prominent design features of Six Sigma, and a newly proposed roadmap for healthcare delivery.
- Sets out information on graphical tools, including fishbone diagrams, mind–maps, and reality trees.
- Gives a thorough treatment of process capability analysis for non–normal data.
- Discusses advanced tools for Six Sigma, such as statistical process control for autocorrelated data.
Consolidating valuable methodologies for process optimization and quality improvement, Six Sigma: Advanced Tools for Black Belts and Master Black Belts is a unique reference for practising engineers in the electronics, defence, communications and energy industries. It is also useful for graduate students taking courses in quality assurance.
PART A: SIX SIGMA: PAST, PRESENT AND FUTURE.
1. Six Sigma: A Preamble (H. S. Yam).
2. A Strategic Assessment of Six Sigma (T. N. Goh).
3. Six Sigma SWOT (T. N. Goh and L. C. Tang).
4. The Essence of Design for Six Sigma (L. C. Tang).
5. Fortifying Six Sigma with OR/MS Tools (L. C. Tang, T. N. Goh and S. W. Lam).
PART B: MEASURE PHASE.
6. Process Variations and Their Estimates (L. C. Tang and H. S. Yam).
7. Fishbone Diagrams vs. Mind Maps (Timothy Yoap).
8. Current and Future Reality Trees (Timothy Yoap).
9. Computing Process Capability Indices for Nonnormal Data: A Review and Comparative Study (L. C. Tang, S. E. Than and B. W. Ang).
10. Process Capability Analysis for Non–Normal Data with MINITAB (Timothy Yoap).
PART C: ANALYZE PHASE.
11. Goodness–of–Fit Tests for Normality (L. C. Tang and S. W. Lam).
12. Introduction to the Analysis of Categorical Data (L.C. Tang and S. W. Lam).
13. A Graphical Approach to Obtaining Confidence Limits of Cpk (L. C. Tang, S. E. Than and B. W. Ang).
14. Data Transformation for Geometrically Distributed Quality Characteristics (T. N. Goh, M. Xie and X. Y. Tang).
15. Development of A Moisture Soak Model For Surface Mounted Devices (L. C. Tang and S. H. Ong).
PART D: IMPROVE PHASE.
16. A Glossary for Design of Experiments with Examples (H. S. Yam).
17. Some Strategies for Experimentation under Operational Constraints (T. N. Goh).
18. Taguchi Methods: Some Technical, Cultural and Pedagogical Perspectives (T. N. Goh).
19. Economical Experimentation via Lean Design (T. N. Goh).
20. A Unified Approach for Dual Response Surface Optimization (L. C. Tang and K. Xu).
PART E: CONTROL PHASE.
21. Establishing Cumulative Conformance Count Charts (L. C. Tang and W. T. Cheong).
22. Simultaneous Monitoring of the Mean, Variance and Autocorrelation Structure of Serially Correlated Processes (O. O. Atienza and L. C. Tang).
23. Statistical Process Control for Autocorrelated Processes :ASurvey and An Innovative Approach (L. C. Tang and O. O. Atienza).
24. Cumulative Sum Charts with Fast Initial Response (L. C. Tang and O. O. Atienza).
25. CUSUM and Backward CUSUM for Autocorrelated Observations (L. C. Tang and O. O. Atienza).