A new book, Quality Engineering Applications of Statistical Design, by T. N. Goh, was recently published by Wiley. Links to three previous articles by the author are provided below.
Improving on the Six Sigma Paradigm [the broken link was removed]
As six sigma has taken the business world by storm in the past 15 years, many organizations have focused on acquiring and implementing the DMAIC methodology with performance benchmarks defined by “sigma levels”. However, after perhaps proclaiming the “six sigma organization” label for the company, it is important for the business leaders to look beyond immediate concerns, i.e. those issues embodied in black belt projects, and adopt holistic and forward-looking perspectives in seriously advancing organizational interests.
Statistical Techniques for Quality [the broken link was removed] by T.N. Goh and M. Xie:
Statistical process control techniques and their role in process improvement are first discussed and some issues related to the interpretation and use of experimental design techniques are also summarised. The focus will be on continuous quality improvement using statistical techniques.
Perspectives on Statistical Quality Engineering [the broken link was removed] by T.N. Goh:
“How is statistical quality engineering related to Six Sigma?” The concepts, techniques and illustrations, explained in a non-mathematical language, are useful to both management and technical personnel interested in strategies and tools for cost-effective quality improvement.
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Statistical quality engineering constitutes the backbone of the Improve phase, where design of experiments is used to identify the critical parameters (the “vital few” among the “trivial many”) in a process or product. It can be said that a Six Sigma program will make or break depending on the success of deployment of statistical quality engineering during the Improve phase.
…
Statistical quality engineering constitutes the backbone of the Improve phase, where design of experiments is used to identify the critical parameters (the “vital few” among the “trivial many”) in a process or product. It can be said that a Six Sigma program will make or break depending on the success of deployment of statistical quality engineering during the Improve phase.
