While researchers and practitioners may seamlessly develop methods of detecting outliers in control charts under a univariate setup, screening multivariate pose serious challenges. In this study, we propose robust chart based on the Stahel-Donoho estimator (SDRE), whilst process parameters are estimated from phase-I. Through intensive Monte-Carlo simulation, study presents how estimation presen...

To monitor time and magnitude, different types of control charts have been proposed in the literature. This article proposes a new maximum exponentially weighted moving average (Max-EWMA) chart by assuming Weibull distribution. The test-statistic Max-EWMA consists two EWMA statistics, where one statistic is used for monitoring magnitude while other to interval between events. performances asses...

Statistical process control (SPC) has been used to achieve and maintain control of various processes in industry (Stoumbos, Reynolds, Ryan, and Woodall 2000). The control chart is a primary SPC tool to monitor process variability and promote quality improvement by means of detecting process shifts requiring corrective actions. As a graphical monitor, control charts generally contain a centerlin...

Quality control is an important role in industry for maintain quality stability. Statistical process can quickly investigate the occurrence of unforeseen causes or shifts using charts. Mixed Exponentially Weighted Moving Average - Cumulative Sum (MEC) chart a tool used to monitor and evaluate whether production not. The MEC method combination (EWMA) (CUSUM) Combining two charts aims increase se...

Nonparametric or distribution-free charts can be useful in statistical process control when there is limited knowledge about the underlying process. In this paper a Shewhart-type chart is considered for the location, based on the Mann-Whitney statistic. The control limit calculations use Lugannani-Rice saddlepoint, Edgeworth and other approximation methods along with Monte Carlo estimation and ...

The industrial sector is considered one of the fastest-growing sources greenhouse gases, due to excessive consumption energy required cope with growing production exhaustive products. statistical process monitoring (SPM) can be an effective tool for and controlling carbon emissions from industries. This article presents economic-statistical design combined Shewhart X¯ exponentially weighted mov...

Recently, monitoring the process mean and variability simultaneously for multivariate processes by using a single control chart has drawn some attention. However, due to the complexity of multivariate distribution, the existing methods in the univariate processes can not be readily extended to the multivariate processes. In this paper, we propose a new single control chart which integrates the ...

A traditional approach to monitor both the location and the scale parameters of a quality characteristic is to use two separate control charts. These schemes have some difficulties in concurrent tracking and interpretation. To overcome these difficulties, some researchers have proposed schemes consisting only one chart. However, none of these schemes is designed to work with individual observat...

When monitoring a process which has multivariate normal variables, the Shewhart-type control chart (Hotelling (1947)) traditionally used for monitoring the process mean vector is effective for detecting large shifts, but for detecting small shifts it is more effective to use the multivariate exponentially weighted moving average (MEWMA) control chart proposed by Lowry et al. (1992). It has been...

OBJECTIVE This study aims to compare different control charts to monitor the nosocomial infection rate per 1,000 patient-days. METHODS The control charts considered in this study were the traditional Shewhart chart and a variation of this, the Cumulative Sum and Exponentially Weighted Moving Average charts. RESULTS We evaluated 238 nosocomial infections that were registered in the intensive...