An expanded case against synthetic‐type control charts

Knoth, Sven

doi:10.1002/qre.3128

Cited by 7 publications

(3 citation statements)

References 48 publications

(154 reference statements)

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“…It is important to know the process components and behavior to be monitored to select the best control chart or a set of control charts to compare with. Also, it is important to identify deceiving trends in certain types of control charts recalled in the SPM literature [77], [78], [79], [80], [81] to avoid their use. If these recommendations are not followed, the number of false alarms or undetected signals could increase significantly.…”

Section: Gaps In Using Data-driven Methods Combined With a Control Chartmentioning

confidence: 99%

A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

2023

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In recent years, the adoption of statistical process monitoring (SPM) techniques in healthcare has been successful. For instance, biosurveillance and biosignal monitoring have demonstrated direct benefits. As the latest reviews of the literature show, parametric SPM techniques have been implemented to evaluate the quality-of-service hospitals provide, track medical equipment, monitor safety markers, or assess the improvements made by quality projects. However, as shown in this research, world-trending topics in data science that include data-driven approaches integrated with SPM have not been reviewed. To bridge this gap and shed light on new research, a systematic review of scientific databases and a taxonomic literature analysis were performed. For the scientometric analysis, a set of bibliometric indicators were obtained to portray the performance of each subtopic, such as examining growth kinetics, identifying top authors, journals, countries and affiliations, as well as creating network maps of co-authorship and keyword co-occurrence. Additionally, the taxonomic analysis involved grouping proposals by methodological approach. Each approach was explained and discussed to identify the advantages, limitations, and challenges that researchers and practitioners may encounter. SPM researchers and practitioners require more flexibility in data-driven approaches to account for frequency unbalance, complexity, dimensionality problems, and speed. Those working in data-driven and computer-oriented areas can expand their toolbox by incorporating sequential approaches to enhance the power of their classifiers, assess risk, reduce misspecification, and adopt model-oriented mindsets.INDEX TERMS Data-driven, healthcare, scientometric, statistical process monitoring.

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Section: Gaps In Using Data-driven Methods Combined With a Control Chartmentioning

confidence: 99%

A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

2023

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“…A number of researchers have argued that when the IC process distribution is unknown or unspecified, then the OOC 𝐴𝑅𝐿 metric is not a good criterion for the performance comparison of different schemes; see for instance, Kenett and Pollak 18 and Knoth. 19 In such case, they recommend the use of the CED criterion. Kenett and Pollak 18 defined the CED as "the delay from the first opportunity to detect a change and not from the time of change itself".…”

Section: The Ced Criterionmentioning

confidence: 99%

A new multivariate extended homogeneously weighted moving average monitoring scheme incorporated with a support vector machine

Pieters

Malela‐Majika

Human

et al. 2023

Quality & Reliability Eng

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Statistical process monitoring (SPM) provides a way, by means of monitoring schemes, to be alerted when a process significantly changes, and to initiate investigations for potential causes of variation. However, these schemes can only determine if the process is stable or unstable and they cannot provide any additional information. Several researchers have recommended the integration of machine learning (ML) approaches into SPM as a solution for the shortfalls of the techniques used to construct traditional monitoring schemes. This paper introduces a new multivariate extended homogeneously weighted moving average (MEHWMA) monitoring scheme and investigates its performance in terms of the run‐length distribution using simulation. In addition, the proposed MEHWMA scheme is integrated with a support vector machine to allow for the classification of the out‐of‐control events which facilitates the identification of the root causes of variation in the process. A numerical illustrative example is provided using real‐life data.

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“…Davis and Woodall 100 indicated that the synthetic chart is nothing more than a particular runs‐rule chart. Moreover, Knoth 101,102 criticized the design performance of synthetic charts and showed that not only these charts do not have an advantage over the standard charts, they also cause information loss about the process data.…”

Section: Categorizing Literature Based On Type Of Control Charts Unde...mentioning

confidence: 99%

A review of ranked set sampling and modified methods in designing control charts

Mohammadkhani

Amiri

Khoo

2023

Quality & Reliability Eng

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Statistical process monitoring (SPM) and designing control charts include measuring quality characteristics by considering the time and cost constraints. Ranked set sampling (RSS) is a very efficient and an inexpensive approach in obtaining a more representative sample when the actual measurement of the sampling units is too expensive or destructive, where it is easier to rank the observations. Since 1997, when the first paper on designing control charts based on the RSS scheme was presented, a significant number of papers have been published in this area. This paper provides a literature review on using the RSS schemes in designing control charts. Relevant papers are classified and analyzed to identify the research gaps and provide recommendations and directions for future studies.

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An expanded case against synthetic‐type control charts

Cited by 7 publications

References 48 publications

A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

A new multivariate extended homogeneously weighted moving average monitoring scheme incorporated with a support vector machine

A review of ranked set sampling and modified methods in designing control charts

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