A risk assessment approach application with analytic network process in a Turkish aviation company in the context of fatigue risk management system

In most of the studies, according to the statistical data 80% of the fatal accidents in the aviation industry is caused by human error and 20% of this 80% is related with fatigue. The definition of fatigue for crewmember is the following "A physiological state of reduced mental or physical performance capability resulting fromsleep loss or extended wakefulness, circadian phase, or workload (mental and/or physicalactivity) that can impair a crew member's alertness and ability to safely operate an aircraft orperform safety related duties"(ICAO-IATA-IFALPA, 2011). For most of the industries the major cause of fatigue is the shortage of rest time. For this reason in most of the studies the most important factors causing the fatigue are identified as sleep quality, sleep quantity and biological body clock. Sleep with adequate quality and quantity assist people to achieve their duties with normal level of attention and awakeness. Yawning, napping, increased reaction times, difficulty in focusing, decreased motivation and energy, occurrence of social interaction problems, loss of attention, decrease in awakeness level are the most common physical, mental and emotional symptoms of fatigue which lead people to make increased number of faults. Increased number of faults caused by fatigue can result with accidents which causes severe injuries and other serious results. At this point, it is obvious that fatigue is an crucial factor for safety. Risks based on fatigue should be managed by preventing the occurrence of fatigue and also decreasing the effects caused by fatigue. In order to manage fatigue in aviation industry, which is seen as a risk factor for safety, first of all, fatigue should be assessed. While doing this measurement, it should be considered that there are interrelationships between the factors and every factor does not have the same importance.In most of the studies, fatigue assessment is based on risk factors and mainly focused on high risk group factors. However, important factors like quality and quantity of sleep and circadian body clock are not considered. Also in these studies, if there are multi factors classified as high risk factor group, no priority is set among these factors. In this study, "Fatigue Risk Management System" (FRMS), which is applied by many aviation companies worldwide, is applied to a Turkish aviation company. Fatigue risk management system can be defined most commonly as follows "A data-driven means of continuously monitoring and managing fatigue-related safety risks, based upon scientific principles and knowledge and operational experience that aims to ensure relevant personnel are performing at adequate levels of alertness."(ICAO-IATA-IFALPA, 2011) The planned FRMS risk assessment process cycle will be accomplished with continuous improvement and proactive control of identification of fatigue hazard factors. In this scope, the importance of the fatigue factors is assessed by Analytic Network Process (ANP) approach with respect to interrelationships among related factors and a fatigue risk assessment process is defined to identify the fatigue of cockpitand cabin crew members in aviation industry with the most accurate way. The decision analysts, the authors of this study, interact with expertsto establish the fatigue risk assessment process. Experts of this study are a first officer with medical background, three captain pilots with flight experience more than twenty years,and three cabin crew members with different professions.By considering the previous studies in the literature and the opinions of these experts,factors related to the fatigue of cockpit and cabin crewmembers is identified, and then these factors are classified under three main clusters such as individual, environmental, and work related issues. Some of the individual factors considered in this study are age, gender, body mass index, sleep quality, sleep quantity, and sleep disorders. Second cluster which is environmental factors include weather conditions, working environment conditions, time zone differences, disruption of circadian body clock, and social interaction. The last but not least cluster, namely work related factors, is composed ofworking stress, late arrival flight duty, early duty, changes in the schedules, traveling time between home and workplace, consecutive night duty planning, intensity of food, and beverage services during short-haul flights. With an additional interaction with experts, the interrelations among related factors are identified. Based on the responses, a relationship matrix is constructed. As well known, for each parent element, a factor affected by sub-elements, pairwise comparison questions are prepared,cluster by cluster, to compare relative impact of sub-elements affecting this parent element.As a result, decision analysts come up with a pairwise comparison questionnaire. The pairwise comparison questions are posed to four cockpit crew members and three cabin crew members who are employees of a Turkish aviation company. In accordance with ANP, paired comparison judgments are arranged in corresponding pairwise comparison matrices.Then, eigenvectors of these pairwise comparison matrices computed are placed to asupermatrix. After converting supermatrix to a column stochastic weighted supermatrix, the supermatrix is raised to a significantly large power in order to have a limit matrix where the converged or stable values exist. The limit matrix exhibit the desired priorities of the factors from the point of view of cockpit and cabin crew members. After identifiying the hazards the measurement and risk assessment of this hazard should be done. To measure the fatigue, a risk assessment is done with ANP based on the respondents. In this approach the following tools are proposed; • Fatigue Control List which is developed for ANP and • The schedule proposed by AMA which is developed for the risk groups in the aviation industry. The hazards are prioritised with ANP method by appliying expert opinions. Control lists are filled by experts by considering each risk groups (low-medium-high). The points of risk groups and the fatigue hazards priority values are multiplied and as a result total risk score based on each hazard and total fatigue risk score based on each person can be specified. The points for each hazard are prioritised with pareto analysis and actions are planned for risks for the necessary stiuations. By identifiying the risk scores of hazards, objective data will be supplied to the experts with industrial experience. This will assist them to take actions to decrease fatigue. Calculating the total fatigue risk score based on each person (pilots and cabin crew), the ones which belong to the high and middle risk group can be identified. As a second step, one to one interviews can be done with the respondents and the reason of the high scores can be discussed. Also teaching these respondents, the strategies to cope with fatigue can prevent possible future accidents. As a result, the most effective factors causing fatigue for the pilots are the following; jetlag (time zone differences), circadian rhythm, the fatigue feeling before the duty, the fatigue feeling during the duty and social interaction. For the cabin crew, the most effective factors causing fatigue are the following; jetlag (time zone differences), circadian rhythm, the fatigue feeling after the duty, the fatigue feeling before the duty, working conditions. A Fatigue Risk Management System methodology will be established by considering current regulations and fatigue risk factors which are mostly contributing to the fatigue of the cockpit and cabin crew members. As a result of this methodology, more efficient and flexible schedules will be obtained which considers optimum flight time, duty time, and rest time. As further studies the following topics can be considered; • The fatigue model for pilots with ANP can be classified depending on the fleet type, pilot versus co-pilot roles and responsibilities. For the cabin crew also fleet type, hierarchical grouping of the members can be the classifying factors. • Pairwise comparisons and fatigue control lists can be applied to the pilots and cabin crew depending on their demographic properties (age, status, experience, sex, etc.). • In order to measure statistically the accuracy of the fatigue model the previous schedules which fatigue has occurred can be compared with the hazard factors of the model. • The effect of airbases on the occurrence of the fatigue can be added to the fatigue model. For instance, air traffic intensity, land traffic intensity, geographical location, weather conditions, physical conditions of the field can be considered while adding this factor to the fatigue model. • A mathematical model, which takes fatigue into account, for scheduling the duties of pilots and crew members can be developed. Also with this model optimum number of pilots and crew members can be determined by targeting a minimum level of fatigue.