Bob Dodd International Air Safety Seminar Singapore November 2011

Transcription

1 Bob Dodd International Air Safety Seminar Singapore November 2011

2 ! Data sharing history! Successful but...! The filing cabinet problem! Incoherent data! Integration via neurons! Putting risk at the centre! Getting coherence! The bowtie as an organising structure! Causal models! Getting there

3 1985 Interna.onal Data Exchange on Avia.on Safety (IDEAS) Controlled Flight into Terrain (CFIT) Task Force Global Analysis and Informa.on Network (GAIN) Commercial Avia.on Safety Team (CAST) Safety Trend Evalua.on, Analysis & Data Exchange System (STEADES) Avia.on Safety Informa.on Analysis Sharing (ASIAS) Global Safety Informa0on Exchange (GSIE)

4 ! Joint safety analysis initiatives (CAST, ECAST) successful identification of safety improvements! GAIN many successes although data exchange held back! STEADES some comparative data for airlines But! Majority of analysis remains with accidents! Understanding of causal patterns for risk based on small numbers of accidents! Combination of incidents, investigations. flight data & audit data very rare if ever. Why?

5 ! Most safety data is structured around the collection tool:! Safety Reports! Investigations! Flight Data! Audits! HF Analysis! Climate surveys! Data in each drawer is generally incompatible

6 ! Safety reports classified by events! Investigation findings classified by causal structure eg Reason model! Audit findings structured by standards (IOSA, ISO) or regulatory requirements! Human Factors analysis coded by often unique codes such as HFACS! Flight data structured around flight standards, SOPs etc

7 ! In many cases each element of data collected is risk assessed separately:! Safety events! Hazards! Findings! Flight Data events! Unclear what is being assessed often! Classes don t add up! At some level the risks must be common But where?

8 ! Most analysis relies on human assessments! Humans are! good at stories, but! bad at data and risk analysis! Most analysis based on accident and incident investigation stories! Selective use of other data when it supports

9 ! For example FRMS! Great work based on 30 years of science! New models as tools for operations! Measure and predict fatigue levels! But deciding risk level based on fatigue level still mainly a black art! Bottom line more tools, more data, more process added to existing deluge of data for managers to somehow combine with existing risks.! Its all in SMS!

10 ! Let risk drive data not data drive risk! Better understanding of risk based on:! Models that support measures of risk levels and vulnerabilities! Analysis of the driving structures under those risk levels! Informed assessment of alternative management actions! Coherent structure to incorporate new and changing risk issues in a cumulative not fragmenting way

11 Build bow.e models for each major accident family eg runway excursions, loss of control etc

12 Threats Control Failures Undesired Opera0onal State Safety Reports LOSA- style audits (Threats) Safety Reports LOSA- style audits (Errors) Inves0ga0on Findings Audit Findings Flight Data Events Safety Reports LOSA- style audits (UOS) Inves0ga0on Findings Flight Data Events! Structure data collection to support model! Use flight data to support more qualitative data Recovery Ac0va0ons / Failures Safety Reports LOSA- style audits (Errors) Inves0ga0on Findings Flight Data Events Consequences Interna0onal Data

13 ! Risk Precursor Index! Combines data on undesired states, recovery control activation, consequence rates! Vulnerability Assessment! Combines threat rates with control weaknesses! Safety System Threat Assessment! Combines data on threats to system integrity that undermine control effectiveness throughout the operation! Culture survey results

14 ! Time to reverse the traditional approach! Put risk at the centre! Build models that capture the understanding of safety risks built over last thirty years! Change the way we record and classify data to support model estimates! Analyse the causes of risks not just the consequences