FARO - Safety and Resilience Guidelines for Aviation
The demand upon the ATM system is reaching its capacity levels, which introduces pressure on the economics of air transportation and accentuates the challenge of maintaining safety. Increased Automation promises to effectively balance the capacity issue while ensuring scalability and increasing resilience. But, to what extent is valid the previous statement?
Automation facilitates changes in the ATM Operational concept that enables the modernisation of ATM in Europe, following the strategic objectives of Single European Sky and the ATM Roadmap, and SESAR solutions are a realisation of the sustained effort during past years towards this goal. SESAR considers the automation and digitalisation principles, transposed to the European ATM Master Plan. They are founded on the application of technology impacting profoundly on organisations and changing how people operate the ATM system. These three pillars together represent the TOP (Technology, Organisation, People) approach and the impact of a solution can only be evaluated by addressing these three elements simultaneously.
Safety is paramount in aviation. Resilient performance is about managing the challenges of performance variability and its effect on operations. Automation goals aim at increasing the ATM performance while maintaining safety. But what is the impact on the ATM resilience capacity to respond to a disruption or a challenge if automation has modified the resilience state in the balance between safety and performance?
So, how can an organisation evaluate the impact of these new solutions on their current safety and resilience performance? ‘Digital solutions’ bring digital footprints, i.e. data. Data can be organised, transformed and exploited to quantify that impact as a function of technical, organizational, human and procedural dimensions.
The quantification of this impact will be based on a deep knowledge of how ATM provides safety before and after the solution is deployed. Learning from past incidents and accidents by using natural language processing and data analysis together with gathering operational expertise will pave the way to enrich accumulated safety knowledge with a more pro-active monitoring of the system.