Reducing the variation in process outputs is a key part of process improvement. For mass produced components and assemblies, reducing variation can simultaneously reduce overall cost, improve function and increase customer satisfaction with the product. the authors have structured this book around an algorithm for reducing process variation that they call Statistical Engineering. the algorithm is designed to solve chronic problems on existing high to medium volume manufacturing and assembly processes. the fundamental basis for the algorithm is the belief that we will discover cost effective changes to the process that will reduce variation if we increase our knowledge of how and why a process behaves as it does. a key way to increase process knowledge is to learn empirically, that is, to learn by observation and experimentation. The authors discuss in detail a framework for planning and analyzing empirical investigations, known by its acronym QPDAC (Question, Plan, Data, Analysis, Conclusion). They classify all effective ways to reduce variation into seven approaches. a unique aspect of the algorithm forces early consideration of the feasibility of each of the approaches. PRAISE FOR Statistical EngineeringThis is the most comprehensive treatment of variation reduction methods and insights Ieve ever seen. - Gary M. Hazard TellabsThroughout the text emphasis has been placed on teamwork, fixing the obvious before jumping to advanced studies, and cost of implementation. all this makes the manuscript attractive for real-life application of complex techniques. - Guru Chadha Comcast IP Services.See confounding Amster, S., CD-253 analysis of variance (ANOVA), 95, 150, 157 , 162, CD-229, CD-333 gage RifeR and, ... gage Raamp;R investigation, CD-214 Measurement Systems Analysis manual, 91 , CD-211, CD-215 QS-9000 Manual, 46, anbsp;...
|Author||:||Stefan H. Steiner, R. Jock MacKay|
|Publisher||:||ASQ Quality Press - 2005-01-01|