TY - JOUR
T1 - Capability indices for Birnbaum-Saunders processes applied to electronic and food industries
AU - Leiva, Víctor
AU - Marchant, Carolina
AU - Saulo, Helton
AU - Aslam, Muhammad
AU - Rojas, Fernando
N1 - Funding Information:
The authors wish to thank the Editor-in-Chief, Prof. Robert Aykroyd, and two anonymous referees for their constructive comments on an earlier version of this manuscript which resulted in this improved version. The research was partially supported by FONDECYT 1120879 and DIUV 14/2009 grants from the Chilean government and the University of Valparaiso-Chile, respectively, and by CAPES, CNPq and FACEPE grants from Brazil.
PY - 2014/9
Y1 - 2014/9
N2 - Process capability indices (PCIs) are tools widely used by the industries to determine the quality of their products and the performance of their manufacturing processes. Classic versions of these indices were constructed for processes whose quality characteristics have a normal distribution. In practice, many of these characteristics do not follow this distribution. In such a case, the classic PCIs must be modified to take into account the non-normality. Ignoring the effect of this non-normality can lead to misinterpretation of the process capability and ill-advised business decisions. An asymmetric non-normal model that is receiving considerable attention due to its good properties is the Birnbaum-Saunders (BS) distribution. We propose, develop, implement and apply a methodology based on PCIs for BS processes considering estimation, parametric inference, bootstrap and optimization tools. This methodology is implemented in the statistical software {\tt R}. A simulation study is conducted to evaluate its performance. Real-world case studies with applications for three data sets are carried out to illustrate its potentiality. One of these data sets was already published and is associated with the electronic industry, whereas the other two are unpublished and associated with the food industry.
AB - Process capability indices (PCIs) are tools widely used by the industries to determine the quality of their products and the performance of their manufacturing processes. Classic versions of these indices were constructed for processes whose quality characteristics have a normal distribution. In practice, many of these characteristics do not follow this distribution. In such a case, the classic PCIs must be modified to take into account the non-normality. Ignoring the effect of this non-normality can lead to misinterpretation of the process capability and ill-advised business decisions. An asymmetric non-normal model that is receiving considerable attention due to its good properties is the Birnbaum-Saunders (BS) distribution. We propose, develop, implement and apply a methodology based on PCIs for BS processes considering estimation, parametric inference, bootstrap and optimization tools. This methodology is implemented in the statistical software {\tt R}. A simulation study is conducted to evaluate its performance. Real-world case studies with applications for three data sets are carried out to illustrate its potentiality. One of these data sets was already published and is associated with the electronic industry, whereas the other two are unpublished and associated with the food industry.
KW - Monte Carlo simulation
KW - bootstrapping
KW - data analysis
KW - maximum likelihood method
KW - non-normality
KW - optimization
KW - quality tools
KW - statistical software
UR - http://www.scopus.com/inward/record.url?scp=84901284053&partnerID=8YFLogxK
U2 - 10.1080/02664763.2014.897690
DO - 10.1080/02664763.2014.897690
M3 - Article
AN - SCOPUS:84901284053
SN - 0266-4763
VL - 41
SP - 1881
EP - 1902
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
IS - 9
ER -