Economic Control of Quality of Manufactured Product.

Calcul des Probabilités. By Paul Lévy. Pp. 350. Fr. 40. 1925. (Gauthier-Villars.)

Incorporating volatility updating into the historical simulation method for value-at-risk

In this paper, we propose an efficient control chart for monitoring small shifts in a process mean for scenarios where the process variable is observed with a correlated auxiliary variable. The proposed chart, called an auxiliary homogeneously weighted moving average (AHWMA) chart, is a homogeneously weighted moving average type control chart that uses both the process and auxiliary variables in the form of a regression estimator to provide an efficient and unbiased estimate of the mean of the process variable. We provide the design structure of the chart and examine its performance in terms of its run length properties. Using a simulation study, we compare its run length performance with several existing methods for detecting a small shift in the process mean. Our simulation results show that the proposed chart is more efficient in detecting a small shift in the process mean than its competitors. We provide a detailed study of the chart's robustness to non-normal distributions and show that the chart may also be designed to be less sensitive to non-normality. We give some recommendations on the application of the chart when the process parameters are unknown and provide an example to show the implementation of the proposed new technique.

Efficient Homogeneously Weighted Moving Average Chart for Monitoring Process Mean Using an Auxiliary Variable

Monitoring the coefficient of variation: A literature review

Enhancing the performance of the EWMA control chart for monitoring the process mean using auxiliary information

Efficient linear profile schemes for monitoring bivariate correlated processes with applications in the pharmaceutical industry

A fundamental strategy to diminish variations in manufacturing process urged the practitioners to characterize the quality of a process by a relationship between the response variable and one or more explanatory variables instead of a single quality characteristic; this state is known as a profile or a function. Profile monitoring mainly aims to test the stability of this relationship. Many researches have been carried out to study the different sampling techniques in the performance of linear profile under the maximum likely hood (MLE) estimation strategy, whereas using different estimation strategy has not been discussed so far. This paper is dedicated to introduce Bayesian estimation strategies with a proposal of novel control charts for jointly monitoring the linear profile. We considered restricted and pretest estimators, besides the estimation of distrust probability under the null hypothesis. Analytical and numerical results showed that the proposed estimators outperformed the MLE method. The proposed control charts have been used to monitor the two-phase flow in the oil industry to control the relationship between the flow rate and the pressure difference between two points.

On efficient estimation strategies in monitoring of linear profiles

On the performance of coefficient of variation control charts in Phase I

Using traditional control charts to monitor autocorrelated processes is not beneficial, because it will lead us to misleading detections in the processes. One of the methods used to deal with the control charts for autocorrelated process is the model-based approach. It uses an adequate time series model that fits the process and uses the residuals as monitoring statistics. For the said purpose, it is important to pick a suitable model that can adequately be used for different designs of control charts under specific time series model. This study intends to do the same for three popular types of charts namely Shewhart, exponentially weighted moving average, and cumulative sum. The models covered in this study include AR(1), MA(1), and ARMA(1,1) as the potential models to fit the process of interest. We have focused on two performance aspects namely efficiency and robustness. Average run length is used as a performance measure for different in-control and out-of-control states of the autocorrelated processes under varying levels of autocorrelation. An application example based on a real data set is also included in the study to highlight the importance of the study proposals. Copyright © 2016 John Wiley & Sons, Ltd.

Abdaljbbar B. A. Dawod

Papers

Enhancing the performance of the EWMA control chart for monitoring the process mean using auxiliary information

Efficient linear profile schemes for monitoring bivariate correlated processes with applications in the pharmaceutical industry

On efficient estimation strategies in monitoring of linear profiles

On the performance of coefficient of variation control charts in Phase I

On Model Selection for Autocorrelated Processes in Statistical Process Control