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Accuracy Analysis of the Estimated Process Yield Based on S p k
Author(s) -
Pearn W. L.,
Chuang C. C.
Publication year - 2004
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.544
Subject(s) - estimator , measure (data warehouse) , process capability , process (computing) , yield (engineering) , sampling (signal processing) , statistics , mathematics , factory (object oriented programming) , mean squared error , base (topology) , computer science , algorithm , work in process , engineering , data mining , programming language , operations management , materials science , filter (signal processing) , metallurgy , computer vision , operating system , mathematical analysis
Abstract Process yield has been the most basic and common criterion used in the manufacturing industry as a base for measuring process performance. Boyles considered a measurement formula called S p k , which establishes the relationship between the manufacturing specification and the actual process performance, providing an exact (rather than approximate) measure of process yield. Unfortunately, the sampling distribution and the associated statistical properties of S p k are analytically intractable. In this paper, we consider the natural estimator of the measure S p k . We investigate the accuracy of the natural estimator of S p k computationally, using a simulation technique to find the relative bias and the relative mean square error for some commonly used quality requirements. Extensive simulation results are provided and analyzed, which are useful to the engineers for factory applications in measuring process performance. Copyright © 2004 John Wiley & Sons, Ltd.