Why are go-around policies ineffective?

Transcription

1 Why are go-around policies ineffective? Understanding Go-Around Decision Making; The Psychology Dr. David Jamieson and Captain Bill Curtis

2 Why are we looking at this? 1 in 1,600,000 How do we improve?

3 FAA Statements (ACSF Safety Symposium 2011) The bottom line: as the number of serious accidents and corresponding rates continue to decrease throughout aviation, achieving the next breakthrough in rates and absolute numbers becomes more challenging We need to monitor known precursors to accidents and we need to integrate complex databases to identify exactly where and why certain risks persist...

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5 Consolidated 70% 2011 Accidents 60% 50% 40% 30% 20% 10% 0% Appr & Land Everything Else

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9 Accident Rate Comparison

10 How are we doing at preventing unstable approaches % unstable - Airbus study 3.4 % unstable - LOSA study Stable Unstable

11 What is the compliance rate of executing go around policies? % of all ALAs preventable with a go around [Jim Burin, IASS 2011] % compliance - Airbus study 4% (13%) compliance LOSA 3% compliance - ASIAS analysis no other single decision can have such an impact on the industry accident rate 0 Continue to Landing Go Around

12 Let us look at this another way Go Around Policies

13 Why are we so poor at go-around decision making? When we looked in the databases on why, psychologically, crews make their decisions the cupboards were almost bare Aviation industry psychological tools little What we mostly had was Opinion Conjecture We needed to understand pilot decision making through science

14 Flight Safety Foundation Project Launched at end of 2011 In response to recognition of poor GA compliance rates, and impact on accidents Go-Around Decision Making, and Execution Joint project between the IAC and EAC Commissioned Presage to analyze the psychology behind the decision making

15 Who is Presage Our expertise and solution; shines a light on those psychological and social factors that either contribute to safety or compromise it Gives us an understanding of the psychology of non-compliance Designed to act as an early warning system for behavioral risk Provides the intelligence for the development of focused mitigations

16 Mature Risk Prevention Measureme nt: Data Description Mitigation: Control Interventio n Analysis: Postdiction Prediction Causal Theory building and inferencing

17 Is this also an Air Charter/Fractional Ownership Issue?

18 ASPEN N115WF N115WF utilized the localizer DME-E approach into KASE First approach - ASE ATC reported winds as 290º at 19 knots, with winds gusting to 25 knots to the crew before landing. Crew executed a missed approach and reported a tailwind of 33 kts, then executed a second approach 2 minutes before the accident: wind 320 true/ 14 gusting 25 kts.

19 ASPEN N115WF Slope 6.59 degrees Rates of descend 2500 FPM not uncommon Prevailing winds

20 Decision Making Project Scoping Three Legged Stool Effective Go Around Policy Decision to Execute the Policy Flight Crews Management of the Policy Managers Criteria To Go Around

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23 Decision to Execute (Flight Crew) The Psychology Results

24 Situational Awareness Results 5 GA UA Gut feel for threats Knowing the instruments & equipment Relying on experience Seeing the threats Knowing the limits Adjusting to threats Keeping each other safe Knowing the procedures Company support for safety

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27 Strategies for Mitigation 1. Enhance Dynamic Situational Awareness of Flight Crews (S1) 2. Redefine the Go Around Policy (stable approach parameters, stable approach height, decision points) (S2) 3. Minimize the Subjectivity of Go Around decision making (S3)

28 Recommendations for Mitigation, samples Recommendation Strategy Construct R1 Re-define the stable approach criteria and stable approach height(s). In redefinition there is a valid argument to separate the profile (vertical and lateral) from the other stable approach criteria. Additionally, separation should be considered between the desired SAH and GA decision points. S2 C4, C9 R4 Develop active communications procedures for each approach that are objective, progress in escalation, and sequential, similar in concept to EGPWS or TCAS systems warnings. E.g., at 1000 feet: on profile/off profile ; at 500 feet: stable/unstable ; and at SAH; stable/unstable ; Go Around R7 Develop automated stable approach monitor and alerting systems. Consider efforts to mandate their use. R8 Avoid directive or suggestive standard calls that may compromise ongoing decision making, e.g., announcing, Landing at minimums. S1, S3 C2, C7, C8 S1, S3 C1, C2, C5, C9 S1, S3 C2, C3, C4, C8

29 Management of the Policy (Management) The Psychology Results

30 Managers Results Outreach via FSF, IATA, A4A, as well as some individual airlines Geographical representation 880 hits to the survey, of which 164 (18%) submitted their responses for analysis, as compared with flight crew survey response rate of 33%

31 UA-GA Survey Data Results 68% of managers indicated that they did not know the rate of compliance in the industry 55% did not know their company s rate of compliance 16% estimated an industry compliance rate close to the actual rate of 3% ( close was defined as < 10%) 12% estimated their companies compliance rates as being <10% Only 31% of respondents were dissatisfied with their company s rate Only 20% saw their policies as ineffective

32 Psychological Analysis We measured perceptions, beliefs, attitudes about UA- GA policies and rates as well as Presage s Situational Awareness constructs. Analysis of the data yielded three "factors" that crosscut Presage SA metrics: 1. Perceptions, beliefs, attitudes about company support (informational/organizational) for understanding/management of GA rates 2. Perceptions, beliefs, attitudes about the clarity, appropriateness and effectiveness of companies' UA-GA definitions, policies and procedures 3. Perceived threat/risk contained in companies GA rates

33 Risk Awareness Findings Managers reported they felt moderate support from their companies for the management of their GA policies Managers had moderate confidence in their companies GA policies and procedures The confidence levels on these first two measures, plus lack of awareness of GA rates, lends itself to minimizing the risk = a normalization effect Manager s perceived only a slight risk with respect to the GA rate issue

34 Manager Group Types Sees Lower Risk Over-stated company GA rate Had significantly lower SA scores Felt higher company support on GA management Perceived higher effectiveness of GA policies Sees Higher Risk Had higher sense of urgency More accurate assessment of GA rate More critical of company s GA policy effectiveness Felt less support from company on GA management Had significantly higher SA scores

35 A simple example of a change in one variable

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37 Normalization of Deviance The Historical Normal Dimmed Situational Awareness Minimizes the identification of risk and its level of significance (empirical data is negated) Thwarts the engagement and conversation of risk The risk continues to percolate just below the collective radar until

38 Strategies for mitigation 1. Enhance situational awareness of the GA policy non-compliance rates for their airline. (S4) 2. Enhance situational awareness of the GA policy non-compliance rates for the industry. (S5) 3. Operators set specific compliance rate targets, and establish initiatives to achieve them. (S6) 4. Industry oversight programs be amended to include SARPs that address this specific noncompliance issue.(s7)

39 Other Application Examples

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42 Airline XXX

43 QUESTIONS? Thank you!