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Safety intelligence: Incremental proactive risk management for holistic aviation safety performance

Semantic Scholar · Article (Safety Science) · ceccf029db92a4970941385a964f9491cca62f5a · Published 2019-10-01 · Safety Science · 4 authors

Abstract and citation only, verbatim from Semantic Scholar; full text lives there. All credit to the authors and Safety Science.

Abstract

Abstract Aviation safety is traditionally managed based on the analysis of accidents and incidents. Such strategies allowed aviation safety to evolve greatly, but have the major drawback of being reactive, i.e. mostly based on hindsight. Acknowledging the need to avoid restricting safety analysis only to negative aspects, such as errors and failures, regulations progressively aimed to delve into the complexity of the work domain. In this context, EUROCONTROL developed a reporting framework harmonized with EU and ICAO regulations, the Toolkit for ATM Occurrence Investigation (TOKAI), which allows a structured and unified reporting for Air Navigation Service Providers (ANSPs). This paper, starting from the theoretical benefits of a structured strategy for learning from events, and the operational application of TOKAI in line with EU and ICAO regulations, provides examples on how an incremental proactive risk assessment strategy can be structured, starting from reporting of adverse events. The paper includes examples of data-driven analyses and holistic safety dashboard that can be developed using TOKAI data to empower the decision-makers’ Safety Intelligence. All this is possible by the adoption of a neutralised language/taxonomy that facilitates a similar conversation for both occurrences but also day to day operations.

Authors

  • R. Patriarca
  • G. D. Gravio
  • R. Cioponea
  • A. Licu

Keywords

  • Computer Science
  • Engineering
  • Environmental Science

Citation

R. Patriarca, G. D. Gravio, R. Cioponea , et al. (2019). Safety intelligence: Incremental proactive risk management for holistic aviation safety performance. Safety Science. Semantic Scholar ID ceccf029db92a4970941385a964f9491cca62f5a. https://doi.org/10.1016/J.SSCI.2019.05.040 ↗