Fatigue is a persistent risk in aviation and other industries that require operations around the clock. People are diurnal – we prefer to work during the day and sleep at night, so operations that force us to work at night and sleep during the day are not “natural” for the human species. As a result, our performance at night is impaired and our sleep during the day is fragmented. Yet, operations require pilots to fly “redeyes”, trains to run at night and the early morning, shift workers to work the graveyard shift – so how do we operate with minimal risk to workers and the public? The solution is Fatigue Risk Management Systems (FRMS), a continual improvement process driven by data and a system for using the data to drive toward reduced fatigue in operations. The FRMS process is diagram below, taken from the FAA Advisory Circular 1120-103A “Fatigue Risk Management Systems for Aviation Safety”:
Let’s walk through this process. The first step is to collect data (measure and assess current conditions), which for aviation might be a collection of all the flight schedules (pairings or rosters). The next step is to analyze the schedules for potential fatigue, and the FAA suggests using a fatigue model like SAFTE-FAST to conduct that analysis. The model estimates the sleep of the average crewmember and then processes that sleep and timing of duties to determine cognitive performance or “effectiveness”. SAFTE-FAST or virtually any model will identify flights and schedules that drive down performance and increase fatigue. The next step is to filter through those results for the worst cases and manage or mitigate the fatigue associated with those cases. Once the worst schedules have been adjusted to minimize fatigue, we then monitor the results to determine if fatigue has been reduced or if changes are needed in the future. This is where the system often breaks down because unless rules are added to the scheduling process to avoid fatiguing patterns, these vary same patterns will occur again the next month and the same or similar mitigations will be needed again. This is “insanity”: doing the same thing over and over again, expecting a different outcome!
So how do we stop the insanity? The current process involves sorting individual schedules for those with excessive fatigue and then individually identifying changes in those cases to mitigate the fatigue. Then the corrective action is specific to that pairing and not generally applicable to the entire solution. But fatigue is the product of a pattern of rest and duties, and not specific to cities or trips. So, we invented a process called Insights to find those patterns that are problematic so the fundamental problem can be corrected.
So how does Insights solve the fatigue problem caused by fatiguing pattens? Consider this picture. Find the “dog” in this pattern of “bears”.
What did you do? You scan all the patterns (which are not identical) to find the one pattern that is a dog and is not a bear. That is exactly what Insights in SAFTE-FAST does. It scans all the schedules in terms of duty start times, durations, and prior rest and finds those patterns (up to three duties in a sequence) which are “dogs” and not “bearable”. It does this using a process called Signal Detection Theory. The user has full control over the system. You can define what “breed” of dog you are looking for, like low effectiveness and reservoir OR low reservoir and high workload OR high fatigue hazard area – whatever combination defines the fatigue problem. Then it provides a report of any pattern that results in an extreme score over 90% of the time it occurs. The report shows the pattern, all the schedules that fit that pattern and the likelihood of fatigue. The report is very clear and general – it can cut across cities, calendar days, and fleets – so that it provides the knowledge needed in the scheduling system to not only reduce fatigue this month but avoid that fatiguing pattern in all future months by inserting rules to avoid problematic patterns. It stops the insanity!
This kind of system converts the monthly data and modeling information into KNOWLEDGE about the origins of fatigue so that future scheduling can benefit. Over months, this knowledge translates into WISDOM in the entire scheduling process.
By the way, did you find the dog? Here it is:
