Provided with clear, objective operational data, an operator and line pilots can learn and adjust policies and procedures. As a result at least in part from information gained through their FOQA program, the Bristow/Air Logistics pilots delivered a 95% reduction in low cruise events over a five-month period.
While flight visualization software is immensely useful in helping understand an event, to successfully analyze a lot of operational data, the analyst will need an inquisitive mind and a solid background in actual operations. This is why the involvement of active, scientifically savvy line pilots is important in evaluating FOQA data.
For example, a trigger event involving a yaw rate over 30 deg. per second would indicate a possible loss of tail rotor effectiveness. However, the data do not reveal what caused such an event to occur. In this case, having an Aviation Safety Action Program (ASAP) in place as well could reveal why the event took place. Alternately, an insightful line pilot might point out that the remote landing site involved is prone to hard-to-predict winds from varying directions and pilots are left without definitive wind information for landings.
The selection of trigger events will be partially dictated by the complexity of the onboard recording devices since the number of parameters can range from 30 being recorded to sometimes hundreds. Each operator, software setup, aircraft type, SOP and recording device will influence what can/should be monitored. Some operators have found that once they start a FOQA program they will narrow it down to some of the first trends they notice. A good FOQA program will evolve continuously, especially when combined with input from an ASAP or Line Oriented Safety Audit (LOSA) program as those point out areas of concern that should receive future emphasis.
As part of a just and safety-conscience culture, FOQA data — like reports submitted through ASAP — should be non-punitive and focus on identification of root causes and their correction, not on placing blame. According to the International Helicopter Safety Team, “A 'just culture' is absolutely mandatory for the success of an effective FOQA program. A just culture is a culture in which personnel are encouraged to and feel comfortable disclosing errors, including their own, while maintaining professional accountability. A just culture is not, however, tolerant of reckless behavior or intentional noncompliance with established rules or procedures. The target for a FOQA program is to maintain data security and crew confidentiality within a just culture.”
According to Bristow's Morgan, “In order to have a successful program it is imperative that open communications between concerned parties always be maintained. An agreement between the company [Air Logistics] and the pilots' union was completed to insure crewmember confidentiality and that except in the case of deliberate or illegal acts, the program would be nonpunitive in nature.”
If the program is managed correctly utilizing just culture, an improvement in trust and respect between stakeholders is possible and communication improves as a result. Increased communications leads to improvements not only in safety but efficiency of operations and customer satisfaction. Problems and deviations are more readily identified.
On the other hand, those companies that have used FOQA data for disciplinary purposes have come to suffer unintended consequences. Pilots have changed their flight behavior by concentrating narrowly and making decisions based solely on avoiding a FOQA limit rather than on the overall safety of the flight.
Another ploy that destroys a just culture is the misinterpretation or manipulation of data so that blame defaults to crew error rather than the company's role or other factors in the event. For instance, an analysis of airspeed deviations on final could simply blame the crew for failure "to observe stabilized approach criteria” while ignoring the fact that the speed deviations were caused by the ups and downs of strong thermals. Other examples include exaggerating the scale of a graph to make, say, a 7-kt. deviation appear to be severe.