Objective: To explore the types of errors that commercial pilots may make when trying to resolve a suspected engine oil leak using the interfaces currently available. Background: The decisions that pilots make often have to be made quickly and under time pressure, with the emphasis on avoiding critical situations from arising. To make the correct decisions, it is vital that pilots have accurate and up-to-date information available. However, interaction with flight deck interfaces may lead to error if they are not effectively designed. Method: A hierarchical task analysis was conducted using evidence from pilot interview data to understand the pilots’ typical response to a suspected engine oil leak scenario. This was used as the primary input into the Systematic Human Error Reduction and Prediction Approach (SHERPA). Results: A total of 108 possible errors were identified. The most common error type was a retrieval error, in which flight crews may retrieve the wrong information about the engine. A number of remedial measures are proposed to try and overcome such issues. Conclusion: This analysis provides an initial starting point for identifying potential future design ideas that can assist the pilots in dealing with oil leaks. Application: This work has identified the value of applying human error identification methodologies to the assessment of current flight deck processes surrounding engine oil leaks. The method presented permits the operational analysis of possible errors on the flight deck and facilitates the proposition of remedial measures to implement technological innovations that can mitigate error.

Management of risk in complex domains such as aviation relies heavily on post-event investigations, requiring complex approaches to fully understand the integration of multi-causal, multi-agent and multi-linear accident sequences. The Event Analysis of Systemic Teamwork methodology (EAST; Stanton et al. 2008) offers such an approach based on network models. In this paper, we apply EAST to a well-known aviation accident case study, highlighting communication between agents as a central theme and investigating the potential for finding agents who were key to the accident. Ultimately, this work aims to develop a new model based on distributed situation awareness (DSA) to demonstrate that the risk inherent in a complex system is dependent on the information flowing within it. By identifying key agents and information elements, we can propose proactive design strategies to optimize the flow of information and help work towards avoiding aviation accidents. Statement of Relevance: This paper introduces a novel application of an holistic methodology for understanding aviation accidents. Furthermore, it introduces an ongoing project developing a nonlinear and prospective method that centralises distributed situation awareness and communication as themes. The relevance of findings are discussed in the context of current ergonomic and aviation issues of design, training and human-system interaction.

The network analysis method, Event Analysis of Systemic Teamwork (EAST), was used to examine routine aviation operations from multiple perspectives from six key areas (i.e. Dispatch, ATC, ATM, Maintenance, Loading, and the Cockpit). Data was collected over a five-day observational field trial at an international air cargo operator. Researchers recorded the activities of agents operating within the six key areas over three outbound and two inbound flights. Three networks (i.e. social, information and task) were created for four key phases of flight: (i) pre-flight checks and engines start (ii) taxi, take-off and assent, (iii) descent, landing and taxi, and (iv) park and shut down. The networks represent a 'work audit' of short-haul cargo operations, which enabled a detailed understanding of the interactions and connections within the current system. Implications for the future of distributed crewing concepts are discussed. Practitioner Summary: An analysis of the aviation system was undertaken using the amalgamated data from three outbound and two inbound flights. These analyses show the social, information and task interactions for cargo operations. This has been used to specify requirements for future distributed crewing options.

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