| Autor: |
Sell, Denilson1,2,3 denilson@stela.org.br, Trierveiler, Heron2 heronjt@gmail.com, Todesco, José2,3 tite@stela.org.br, Morales, Aran3 aran@stela.org.br, Selig, Paulo2,3 pauloselig@gmail.com, Giugliani, Eduardo4 giugliani@pucrs.br, dos Santos, Jane Lúcia4 jane.santos@pucrs.br |
| Předmět: |
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| Zdroj: |
Proceedings of the European Conference on Knowledge Management. 2022, Vol. 2, p1050-1059. 10p. |
| Abstrakt: |
Resilience is presented in literature as the capacity of a system to disarm, adapt and recover from unexpected events. Despite the increase of interest of industries and academia in the subject, there are a lack of models that describes the elements that condition and determines resilient performance. This article presents a knowledge model that characterizes intangibles which determine resilient responses and is a central piece in a data science strategy supporting monitoring and analysis of potential for resilience in high-risk industries. Through an interdisciplinary approach, this model was established using an integrative review of the literature and the contribution of experts from several areas. The potential for resilience is represented by a set of leading indicators that allows continuous monitoring of both static characteristics of complex operations and dynamic resource mobilization in the face of unexpected events. Knowledge engineering and data science techniques are applied to treat data from various sources. The established approach addresses several elements that are not traditionally explored in safety management systems, including those related to knowledge that determine resilient responses, as well as factors related to human, structural and relational capital that condition resilient performance. Results of the application of the model are presented, including how the analytical model supports the definition of knowledge management and safety investment strategies in oil and gas companies in Brazil. The approach supports the prioritization of actions and investments to promote safety and enable strategies to learn from accidents and positive conditions that make operations safer despite unpredictability in daily operational routine. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Library, Information Science & Technology Abstracts |
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