CRM began with presentation at NASA in 1979 (Bruce, Gao, & King, 2018). Born against the backdrop of the Tenerife disaster in 1977 and the United Airlines incident over Portland, Oregon in 1978, CRM has evolved and what we see today is known as 6th generation CRM (Helmreich, Merritt, & Wilhelm, 1999). Major changes have occurred between the Cockpit Resource Management of 1979 and the Crew Resource Management models of today. The primary shifts have been around scope and inclusiveness. The Colgan Air mishap in 2014 then led to a shift from passive CRM to a far more active Threat and Error model-based CRM (Holt & Poynor, 2016).

While very complex when studied in detail, stated simply, ‘Threats’ and ‘Errors’ necessitate CRM-based actions/behaviors. Fatigue is a ‘Threat’, can cause ‘Errors’, and needs CRM-based behavior to remediate or recover from the situation. Given this simplistic formulation of the model, it is pertinent that we model the various types of threats that fatigue can pose before we can bake it into the CRM/TEM training programs. Fatigue has known to cause many incidents. American 1420 in June 1999, Colgan Air 3407 in February 2009, Corporate Airlines 5966 in October 2004 are all cases where fatigue has been called out as a leading factor (Avers & Johnson, 2011) 

Unlike skill or competency training, where measurement is somewhat easier, training for behavioral responses is not all that straightforward. For example, training for a response to deal with an engine flame out on takeoff is not the same as training someone for executing a flight control maneuver. Training on factors like fatigue is more complex. On one hand, the human mechanism will not produce behaviors of an individual in a fatigued state unless they are in a state of fatigue. On the other hand, it will be a logistical challenge to get pilot resources to be a part of a simulator scenarios when they are in actually in state of fatigue. 

However, a value-additive approach to building training around fatigue-related behaviors is to first demonstrate the outcomes that fatigue can produce through simulations and scenarios. Since it is a such a strong reality of aviation today, it is worth modeling, scheduling and planning for simulator training for individuals when they are really in a state of fatigue. As an example, scheduling an intense simulator session when the circadian rhythm is in a trough is a good start. This could be further intensified by scheduling a full day of work prior to the late evening simulator session. These could induce fatigue prior to being presented with scenarios.

Fatigue like many things can only be measured through the many symptoms of fatigue it produces. The Center for Human Sciences in Farnborough, UK has developed a model for fatigue describing the symptoms of fatigue (Belyavin & Spencer, 2004). Some of them are as follows – diminished perception, a general lack of awareness; diminished motor skills and sluggish reactions; problems with short-term memory; channeled concentration, fixation on a single possibly unimportant issue, to the neglect of others; being easily distracted by unimportant matters; poor judgement; and slow decision making.

Modeling simulator scenarios that are focused on amplifying the symptoms above will yield the best results from a training perspective. Let us choose the symptom of fatigue-induced short-term memory. Modeling a high traffic congested airspace with multiple air traffic control inputs such as altitude/heading/speed changes, approach restrictions and last-minute runway changes could provide for a scenario where effects of fatigue on short term memory can be assessed.

It is important to note that not everyone reacts the same way to fatigue. While the list of symptoms is generic, each human is different. The “Swiss Cheese (Reason) model” begins to come together when a human weakness aligns with a fatigue-induced symptom and the prevailing circumstance to cause an incident (Reason, Hollnagel, & Paries, 2006). To elaborate further, if a pilot monitoring (PM) and managing communications on the flightdeck is weaker on short-term memory capacity to begin with (when compared to say, her/his motor skills), then fatigue will impact her/his ability to read back and comply with air traffic control inputs. The fatigue threat, causes memory errors, leading to the need for CRM-based recovery. Recovery in this situation could be the pilot flying (PF) noticing it and taking remedial actions. On the other hand, if one has the propensity to be weaker at motor reflexes, then fatigue would impact their ability manually control the airplane. Other scenarios could include failure annunciations to appear late in the approach requiring a quick go-around decision. Fatigue impairs decision making and such scenarios could make for good insights.  

The challenge most times is that many/most individuals aren’t aware of their weak areas and believe that they can “pull it off”. 

The value in AQP, CRM/TEM models is that they allow for the program to be setup in a way that it exposes resources to reality of these situations and more importantly allows individuals, to some degree, understand their own limitations. No amount of Powerpoint presentations will provide the experience of being in the situation, even if it is only in a simulator.

References:

Avers, K., & Johnson, W. B. (2011). A review of Federal Aviation Administration fatigue research: Transitioning scientific results to the aviation industry. Aviation Psychology and Applied Human Factors, 1(2), 87–98. https://doi-org.ezproxy.libproxy.db.erau.edu/10.1027/2192-0923/a000016

Belyavin, A. J., & Spencer, M. B. (2004). Modeling performance and alertness: the QinetiQ approach. Aviation, space, and environmental medicine, 75(3), A93-A103.

Bruce, P. J., Gao, Y., & King, J. M. C. (2018;2017;). Airline operations: A practical guide (1st ed.). London, [England];New York, New York;: Routledge. doi:10.4324/9781315566450

Helmreich, R. L., Merritt, A. C., & Wilhelm, J. A. (1999). The evolution of crew resource management training in commercial aviation. The international journal of aviation psychology, 9(1), 19-32.

Holt, M. J., & Poynor, P. J. (2016). Air carrier operations (Second ed.). Newcastle, Washington: Aviation Supplies & Academics, Inc.

Reason, J., Hollnagel, E., & Paries, J. (2006). Revisiting the Swiss cheese model of accidents. Journal of Clinical Engineering, 27(4), 110-115.