Automation in the service industry is emerging as a new wave of industrial revolution. Standardization and consistency quality an important part automation process. The control methods widely used manufacturing can provide measurement process monitoring. In particular, chart online monitoring technique be to quickly detect whether out control. However, more difficult than that because variabili...

Most commonly used control charts for monitoring quality characteristics of the processes were developed under the assumption that the observations are randomly sampled from a normal population. It is well known that these control charts have more false alarms than usual when processes are positively autocorrelated. One remedy is to adjust the control limits such that the modified control chart...

One of the objectives of the research done in statistical process control is to obtain control charts that show few false alarms but, at the same time, are able to detect quickly the shifts in the distribution of the quality variables employed to monitor a productive process. In this paper the synthetic-T control chart is developed, which consists of the simultaneous use of a CRL chart and a Ho...

The existing optimal design of the fixed sampling interval S2-EWMA control chart to monitor the sample variance of a process is based on the average run length (ARL) criterion. Since the shape of the run length distribution changes with the magnitude of the shift in the variance, the median run length (MRL) gives a more meaningful explanation about the in-control and out-of-control performances...

Most of the inventory control models assume that quality defect never happens, which means production process is perfect. However, in real manufacturing processes, the production process starts its operation in the in-control state; but after a period of time, shifts to the out-of-control state because of occurrence of some disturbances. In this paper, in order to approach the model to real man...

Normality is a common assumption for many quality control charts. One should expect misleading results once this assumption is violated. In order to avoid this pitfall, we need to evaluate this assumption prior to the use of control charts which require normality assumption. However, in certain cases either this assumption is overlooked or it is hard to check. Robust control charts and bootstra...

Acceptance control charts are effective tools tomonitor capable processes in which the fraction of the produced nonconforming items is very low. In these charts, some controlled changes in the process mean are allowed, and the production of a specified number of defectives is tolerated. Designing these acceptance control charts by considering the cost of sampling, detecting, and investigating o...

Multivariate statistical process control deserves particular attention in the recent scenario. Though, Hotelling control chart is quite popular and widely used technique in this field but its performance is deteriorated when the underlying distribution of the quality characteristics is not following multivariate normal distribution. Hence the need of developing a non-parametric multivariate con...

This paper presents an improved version of the Shewhart-type generalized variance |S| control chart for multivariate Gaussian process dispersion monitoring, based on the Cornish-Fisher quantile formula for non-normality correction of the traditional normal based 3-sigma chart limits. Also, the exact sample distribution of |S| and its quantiles (chart exact limits) are obtained through the Meije...