No abstract

ROBUST DESIGN FOR QUALITY ENGINEERING AND SIX SIGMARobust Design for Quality Engineering and Six Sigma Downloaded from www.worldscientific.com by 54.191.190.102 on 05/10/18. For personal use only.v PrefaceCountless books have been published in the fields of both robust design and Six Sigma. However, only a few books have tried to combine robust design and Six Sigma to create synergistic effects for quality management. The primary scope of this book is to explain Taguchi's robust design principles and concepts for quality engineering in the earlier chapters, and to show the importance of robust design for implementation of Six Sigma -especially in the context of Design for Six Sigma (DFSS) -in the later chapters. The book also shows why robust design methodologies are working well in the industry, as demonstrated by several case studies taken from various manufacturing disciplines. We hope that this book will bridge the gap between quality engineering (in particular, robust design) and Six Sigma through value creation effort by the robust design methodology. This book has been written primarily for engineers and researchers who wish to use statistical robust design methodology for quality engineering and Six Sigma, and for statisticians who wish to know about the wide range of applications of experimental design in the industry. This book will also be of interest to students, managers, quality improvement specialists and other Six Sigma professionals such as Black Belts and Green Belts with an interest in robust design methods (e.g. parameter design and tolerance design) as well as implementation of Six Sigma.We have been actively involved for many years as professional teachers or consultants for quality management in the industry, and have personally experienced that both robust design Robust Design for Quality Engineering and Six Sigma Downloaded from www.worldscientific.com by 54.191.190.102 on 05/10/18. For personal use only.vi Robust Design for Quality Engineering and Six Sigma methodology and Six Sigma strategy are extremely effective in the quest for process and quality improvements. We firmly believe that if both robust design and Six Sigma are combined, they would be much more powerful for quality improvement effort -hence, the motivation for us to write this book. This book can be used as a textbook for courses in quality engineering in any engineering discipline, or for the experimental design module of a course in statistics for advanced undergraduate and graduate students. It can also be used for Six Sigma training courses, especially at Green Belt and Black Belt levels. It focuses on robust design and analysis of engineering problems from the standpoint of DFSS rather than on statistical design theory. Only statistical ideas relevant to the solution of the broad class of product and process design problems are discussed.We would like to thank the editors Ms Gow Huey Ling and Mr Tan Yi Xin of World Scientific for making the publication possible, and for their assistance and guidance. We also would l...

PurposeThis article aims to provide Design for Six Sigma (DFSS) practitioners, researchers and academicians with Ten Commandments to successfully deploy projects.Design/methodology/approachThe commandments are the brainchild of four authors' experience and expertise for more than 15 years of DFSS deployment in the spectrum of fields as a consultant, researcher, academic and Master Black Belt in Six Sigma and general quality management and engineering disciplines. Thus ascertained commandments were validated and classified through the “Delphi Study” to ensure its applicability.FindingsThe Ten Commandments from authors' perspective include: alignment of DFSS with organisational strategy; top management support and involvement; listening to the voice of the customers (VOC); effective training programme for right project teams; project selection and prioritisation; linking DFSS with ISO international standards; linking DFSS with organisational learning and innovation; linking DFSS with the 4th Industrial Revolution; effective use of DFSS methodology and the integrated tools within the methodology and reward and recognition schemes.Research limitations/implicationsThe commandments presented in this article are the authors' personal experience in different industrial scenarios and settings and demographical locations. The authors are planning to conduct a longitudinal survey to understand further insights of these commandments with the input of several DFSS Black Belts and Master Black Belts, academicians and leading researchers from various countries.Originality/valueAll the organisation's stakeholders can use this article as general guidelines to ensure effective deployment of the DFSS approach.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.Collinearity among independent variables in multiple linear regression can have severe effects on the precision of response estimation for some region of interest of independent variables. Collinearity is shown to be a situation in which there exist some linear restrictions on the regression parameters, p, that might yield better response estimators than the ordinary least squares estimators in the mean squared error context. This paper studies restrictions and formulates optimal restrictions in the sense of mean squared error. It is shown that the least squares estimator of p under the optimal restrictions is identical to a principal component estimator of p.

No abstract