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Analysis of quantitative metrics for assessing resilience of human-centered CPPS workstations

Manufacturing companies’ preparedness level against external and internal disruptions is complex to assess due to a lack of widely recognized or standardized models. Resilience as the measure to characterize preparedness against disruptions is a concept with various numerical approaches, but still l...

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Autores principales: Aruväli, Tanel, De Marchi, Matteo, Rauch, Erwin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941514/
https://www.ncbi.nlm.nih.gov/pubmed/36807299
http://dx.doi.org/10.1038/s41598-023-29735-1
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author Aruväli, Tanel
De Marchi, Matteo
Rauch, Erwin
author_facet Aruväli, Tanel
De Marchi, Matteo
Rauch, Erwin
author_sort Aruväli, Tanel
collection PubMed
description Manufacturing companies’ preparedness level against external and internal disruptions is complex to assess due to a lack of widely recognized or standardized models. Resilience as the measure to characterize preparedness against disruptions is a concept with various numerical approaches, but still lacking in the industry standard. Therefore, the main contribution of the research is the comparison of existing resilience metrics and the selection of the practically usable quantitative metric that allows manufacturers to start assessing the resilience in digitally supported human-centered workstations more easily. An additional contribution is the detection and highlighting of disruptions that potentially influence manufacturing workstations the most. Using five weighted comparison criteria, the resilience metrics were pairwise compared based on multi-criteria decision-making Analytic Hierarchy Process analysis on a linear scale. The general probabilistic resilience assessment method Penalty of Change that received the highest score considers the probability of disruptions and related cost of potential changes as inputs for resilience calculation. Additionally, manufacturing-related disruptions were extracted from the literature and categorized for a better overview. The Frequency Effect Sizes of the extracted disruptions were calculated to point out the most influencing disruptions. Overall, resilience quantification in manufacturing requires further research to improve its accuracy while maintaining practical usability.
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spelling pubmed-99415142023-02-22 Analysis of quantitative metrics for assessing resilience of human-centered CPPS workstations Aruväli, Tanel De Marchi, Matteo Rauch, Erwin Sci Rep Article Manufacturing companies’ preparedness level against external and internal disruptions is complex to assess due to a lack of widely recognized or standardized models. Resilience as the measure to characterize preparedness against disruptions is a concept with various numerical approaches, but still lacking in the industry standard. Therefore, the main contribution of the research is the comparison of existing resilience metrics and the selection of the practically usable quantitative metric that allows manufacturers to start assessing the resilience in digitally supported human-centered workstations more easily. An additional contribution is the detection and highlighting of disruptions that potentially influence manufacturing workstations the most. Using five weighted comparison criteria, the resilience metrics were pairwise compared based on multi-criteria decision-making Analytic Hierarchy Process analysis on a linear scale. The general probabilistic resilience assessment method Penalty of Change that received the highest score considers the probability of disruptions and related cost of potential changes as inputs for resilience calculation. Additionally, manufacturing-related disruptions were extracted from the literature and categorized for a better overview. The Frequency Effect Sizes of the extracted disruptions were calculated to point out the most influencing disruptions. Overall, resilience quantification in manufacturing requires further research to improve its accuracy while maintaining practical usability. Nature Publishing Group UK 2023-02-20 /pmc/articles/PMC9941514/ /pubmed/36807299 http://dx.doi.org/10.1038/s41598-023-29735-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Aruväli, Tanel
De Marchi, Matteo
Rauch, Erwin
Analysis of quantitative metrics for assessing resilience of human-centered CPPS workstations
title Analysis of quantitative metrics for assessing resilience of human-centered CPPS workstations
title_full Analysis of quantitative metrics for assessing resilience of human-centered CPPS workstations
title_fullStr Analysis of quantitative metrics for assessing resilience of human-centered CPPS workstations
title_full_unstemmed Analysis of quantitative metrics for assessing resilience of human-centered CPPS workstations
title_short Analysis of quantitative metrics for assessing resilience of human-centered CPPS workstations
title_sort analysis of quantitative metrics for assessing resilience of human-centered cpps workstations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941514/
https://www.ncbi.nlm.nih.gov/pubmed/36807299
http://dx.doi.org/10.1038/s41598-023-29735-1
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