

Continuous monitoring and improving the production process is a crucial step for the entrepreneur to maintain its position in the market. A successful process monitoring scheme depends upon the specification of the quality being monitored. In this paper, the monitoring of temperature is addressed using the specification of moving average under uncertainty. We determined the coefficients of the proposed chart utilizing the Monte Carlo simulation for a different measure of indeterminacy. The efficiency of the proposed chart has been evaluated by determining the average run lengths using several shift values. A real example of weather-related situation is studied for the practical adoption of the given technique. A comparison study shows that the proposed chart outperforms the existing chart in monitoring temperature-related data.



Moving average control chart under neutrosophic statistics

Diel pattern of locomotor activity of the climbing perch Anabas testudineus: baseline characterisation and alteration under artificial light

Abstract The Max-Mixed EWMA Exponentially Weighted Moving Average (MM EWMA) control chart is a statistical process control technique used for joint monitoring of the mean and variance of a process. This control chart is designed to detect small and moderate shifts in the mean and variance of a process by comparing the maximum of two statistics, one based on the mean and the other on the variance. In this paper, we propose a new MM EWMA control chart. The proposed chart is compared with existing control charts using simulation studies, and the results show that the chart performs better in detecting small and moderate shifts in both the mean and variance. The proposed chart can be helpful in quality control applications, where joint monitoring of mean and variance is necessary to ensure a product’s or process’s quality. The real-life application of the proposed control chart on yogurt packing in a cup data set shows the outperformance of the MM EWMA control chart. Both simulations &amp; real-life application results demonstrate the better performance of the proposed chart in detecting smaller shifts during the production process. 

Max-mixed EWMA control chart for joint monitoring of mean and variance: an application to yogurt packing process

A statistical study on the variability of aggregate characteristics of asphalt mixtures

On moving average control charts and their conditional average run lengths

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A statistical study on the variability of aggregate characteristics of asphalt mixtures

On moving average control charts and their conditional average run lengths