A new adaptive EWMA control chart for monitoring the process dispersion

In this study, a new process dispersion monitoring control chart is proposed based on a function-based adaptation to select the smoothing constant value, named as a function-based adaptive exponentially weighted moving average (EWMA) dispersion control chart. It is suggested to track shift ranges first expected in process dispersion by opting for smoothing constant computation with the help of a function. The shift magnitude assessment is made by an unbiased estimator that determines the smoothing constant value through the proposed function. The enhanced efficiency of the proposed chart can be assessed in terms of smaller run-length profiles, which are determined through Monte Carlo simulations. The proposed chart is compared with the existing adaptive EWMA dispersion chart, and it turned out to perform substantially efficiently in detecting all kinds of decreasing and increasing process dispersion shift magnitudes.Moreover, a real-life dataset application is explained in the example section to elaborate on the ease of implementation in the real-life scenario.

Section: Design Of the Proposed Faewma-s Chartmentioning

confidence: 99%

Function‐based adaptive exponentially weighted moving average dispersion control chart

Arshad

Noor‐ul‐Amin

Hanif

2021

“…We used a real‐life data example of the hard‐baked process. The data are taken from Montgomery 36 . We have taken 25 samples, each sample is taken from the process of size 5 wafers.…”

Section: Real‐life Examplementioning

confidence: 99%

An adaptive EWMA control chart for monitoring the process mean in Bayesian theory under different loss functions

Noor‐ul‐Amin

Noor

2020

The Shewhart control chart is used for detecting the large shift and an exponentially weighted moving average (EWMA) control chart is used for detecting the small/moderate shift in the process mean. A scheme that combines both the Shewhart control chart and the EWMA control chart in a smooth way is called the adaptive EWMA (AEWMA) control chart. In this paper, we proposed a new AEWMA control chart for monitoring the process mean in Bayesian theory under different loss functions (LFs). We used informative (conjugate prior) under two different LFs: (1) squared error loss function and (2) linex loss function for posterior and posterior predictive distributions. We used the average run length and standard deviation of run length to measure the performance of the AEWMA control chart in the Bayesian theory. A comparative study is conducted for comparing the proposed AEWMA control chart in Bayesian theory with the existing Bayesian EWMA control chart. We conducted a Monte Carlo simulation study to evaluate the proposed AEWMA control chart. For the implementation purposes, we presented a real‐data example.

“…Due to the remarkable adaptability of this AEWMA scheme, it has been investigated by several researchers, see, for instance, Refs. [4–12].…”

Section: Introductionmentioning

confidence: 99%

Optimal one‐ and two‐sided adaptive EWMA scheme for monitoring Poisson count data

Tang

Castagliola

et al. 2021

The Poisson distribution assumption often arises in several industrial applications for modeling defects or nonconformities. In this work, we investigate the one‐ and two‐sided performance of a new adaptive EWMA (exponentially weighted moving average)‐type chart for monitoring Poisson count data. An appropriate discrete‐state Markov chain technique is provided to compute the exact ARL (average run length) properties. Moreover, comparative studies are conducted to demonstrate the higher sensitivity of the proposed chart in the detection of shifts with various magnitudes. Advices on how to select the appropriate chart parameters are provided and an illustrative numerical example is proposed.