| Title: |
Learning from disasters: the 22/7-terrorism in Norway and COVID-19 through a failure modelling lens |
| Authors: |
Henriette Kirksæther Bendiksby; Ashraf Labib |
| Source: |
Emergency Management Science and Technology, Vol 3, Iss 1, Pp 1-7 (2023) |
| Publisher Information: |
Maximum Academic Press, 2023. |
| Publication Year: |
2023 |
| Subject Terms: |
norway attacks 22/7 2011; covid-19; fault tree analysis; reliability block diagram; black swan; Crisis management. Emergency management. Inflation; HD49-49.5 |
| Description: |
Disasters can have detrimental impacts on lives, reputations, trust, and resources. The aim of this paper is to illustrate how root cause analysis methods can be used to learn from failures in both security and safety domains. Utilising two case studies within the security and safety domains, respectively the 22-7 terrorism and Norway and the COVID-19 pandemic within the UK, we investigate how using a hybrid model approach consisting of Fault Tree Analysis (FTA), Reliability Block Diagram (RBD) and Minimum Cut Set Analysis (MCSA), helps identify the causality between failures and the catastrophic events. Results illustrate the benefits of using a hybrid of root cause analysis techniques to extract learning lessons, in order to mitigate against future similar incidents. We applied techniques that can assist organisations to apply the concept of learning from failures in practice. More specifically, the Fault Tree Analysis - for analysing causality, Reliability Block Diagram - for analysing relationships between causal factors, and Minimum Cut Set Analysis - for analysing vulnerable scenarios, were applied to the two cases, demonstrating how these models can aid in their 'de-blackening'. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2832-448X |
| Relation: |
https://doaj.org/toc/2832-448X |
| DOI: |
10.48130/EMST-2023-0007 |
| Access URL: |
https://doaj.org/article/9d31836db68a4e91aae8ec99bc6a46e1 |
| Accession Number: |
edsdoj.9d31836db68a4e91aae8ec99bc6a46e1 |
| Database: |
Directory of Open Access Journals |