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. 2023 Feb 20;13(1):2914.
doi: 10.1038/s41598-023-29735-1.

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

Tanel Aruväli  1 , Matteo De Marchi  2 , Erwin Rauch  2
Affiliations

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

Tanel Aruväli et al. Sci Rep. .

Abstract

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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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Performance dependence from disruptive event and recovery action (adapted from Refs. and ).
Figure 2
Figure 2
Classification of resilience assessment methodologies (redrawn from Ref.).
Figure 3
Figure 3
Main methodology of the research.
Figure 4
Figure 4
Pairwise comparison of Zhang et al. proposed resilience metric against other selected metrics under criteria of relevance (screenshot from AHP-OS software).
Figure 5
Figure 5
Decision matrix for the criterion of relevance (screenshot from AHP-OS software). The row no. 1 and the column no. 1 correspond with Fig. 4 comparison results.
Figure 6
Figure 6
Consolidated priority scores under the criterion of relevance comparison (screenshot from AHP-OS software).
Figure 7
Figure 7
Literature review in numbers.
Figure 8
Figure 8
Decision matrix with comparison criteria weights, resilience metrics consolidated priority scores, and final AHP analysis results comparison (screenshot from AHP-OS software).
See this image and copyright information in PMC

References

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