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Multistage process surveillance is considered to effectively improve the product reliability in manufacturing or service operations. To this end, the process output is commonly inspected under specific conditions and the values of the reliability-related quality characteristic are measured. However, in some cases, the observations from the process output are autocorrelated. This brings about the situation where the use of existing monitoring schemes is futile. Therefore, a class of survival analysis regression models called the proportional hazards (PH) model has been modified to justify the effect of cascade property in line with the autocorrelation issue. Subsequently, three monitoring... 

Monitoring industrial and service processes with the purpose of improving the product reliability has been largely addressed in the literature. The surveillance procedures have been proposed with the concentration on single-stage processes with independent quality characteristics. However, the cascade property in multistage processes entails specific monitoring methods which take into account the dependency structure among quality variables in successive stages of a process. This is referred to as regression-adjustment that justifies the heterogeneity in the study population and thus leads to optimal monitoring of multistage processes. In general, it is not straightforward to adjust a... 

Recent research works have shown that control statistics based on squared deviation of observations from target have the ability to monitor variability in both univariate and multivariate processes. In the current research, the properties of the control statistic S t that has been proposed by Huwang et al. (J. Quality Technology 2007; 39:258-278) are first reviewed and three new S t-based multivariate schemes are then presented. Extensive simulation experiments are performed to compare the performances of the proposed schemes with those of the multivariate exponentially weighted mean squared deviation (MEWMS) and the L 1-norm distance of the MEWMS deviation from its expected value (MEWMSL 1)... 

In recent years, much attention has been given to monitoring multistage processes in order to effectively improve the product reliability. To this end, the output of the process is investigated under special circumstances, and the values corresponding to reliability-related quality characteristic are measured. However, analyzing reliability data is quite complicated because of their unique features such as being censored and obeying nonnormal distributions. A more sophisticated picture arises when the observations of the process are autocorrelated in some cases, which makes the application of previous control procedures futile. In this paper, the accelerated failure time (AFT) regression...