In this paper, a remedial measure is first proposed to eliminate the effect of autocorrelation in phase-ІІ monitoring of autocorrelated polynomial profiles, where there is a first order autoregressive (AR(1)) relation between the error terms in each profile. Then, a control chart based on the generalized linear test (GLT) is proposed to monitor the coefficients of polynomial profiles and an R-c...

The goal of engineering process control (EPC) is to minimize variability by adjusting some manipulative process variables. The goal of statistical process control (SPC) is to reduce variability by monitoring and eliminating assignable causes of variation. As suggested by Box and Kramer and others, it is possible to reduce both special cause and common cause variations by integrating EPC and SPC...

Mortality modelling is a practical method for the government and various fields to obtain picture of mortality up specific age particular year. However, some information on phenomenon may remain in residual vector be unrevealed from models. We handle this issue by employing multivariate control chart discover substantial cohort changes behavior that models still need address. The Hotelling T2 a...

Statistical process control (SPC) charts are commonly used to monitor quality characteristics in manufacturing processes. When monitoring two or more related simultaneously, multivariate T2 often employed. Like univariate charts, chart pattern recognition (CCPR) plays a crucial role SPC. The presence of non-random patterns indicates that is influenced by one assignable causes and corrective act...

Recent studies have shown that the variable sampling interval (VSI) scheme helps practitioners detect process shifts more quickly than the classical scheme (FRS). In this paper, the economically and statistically optimal design of the VSI T2 control chart for monitoring the process mean vector is investigated. The cost model proposed by Lorenzen and Vance (1986) is minimized through a genetic a...