The growth of commercial air transport arguably translates into more aging passenger aircraft queuing up for major maintenance, modifications, and/or freighter conversion with the aircraft maintenance industry. In the competitive business environment, this increased maintenance demand possesses the potential to stress the industry and make safety vulnerable. In the aircraft maintenance industry, several aircraft accidents and incidents have resulted from organizational failure to learn from the past. To address this chronic problem, this study aims to (a) establish a learning process model for the aircraft maintenance industry, (b) identify the factors that influence learning, and (c) determine the effect of identified factors on learning from the past. A review of scholarly articles and regulatory publications enabled the development of learning from the past process model and a data collection tool, followed by structural equation modeling to quantify the relationship among influencing factors. The study was conducted in the Indian aircraft maintenance environment and is based on the perspective of the front-line maintenance staff. The study found that safety communication is the decisive stage for learning from the past. Contextualization of the safety information and evaluating the lessons learned during safety communication strongly impact learning from the past, for which existing regulatory provisions are vulnerable. The findings of this study are meant to assist State regulators and management of the aircraft maintenance industry; nevertheless, safety managers and practitioners in other ultra-safe, high-risk sectors may also apply the results in compliance with the respective regulatory guidelines.

In the last decade, the aircraft maintenance industry has experienced a paradigm shift in safety management. This is primarily due to the implementation of Safety Management Systems (SMS) in its business practices. The critical facet of such SMS recognizes hazards ahead of time. This review aims to undertake scholarly research to enable the identification of numerous hazards within the aircraft maintenance industry. This will be done by reviewing research articles indexed in Scopus and Web of Science databases from 2010 to September 2022. Complying with the guidelines of the PRISMA 2020 updated statement, the Systematic Literature Review (SLR) methodology is adopted for the review. The SMS-based framework was formulated to determine the inclusion and exclusion criteria, which identified 39 studies for inclusion. The key outcomes are (i) Thirty-five studies identified six hazard-prone areas and associated hazards of the aircraft maintenance industry, whereas four research studies (two each) underscored the factors impeding the safety critical SMS enactment and organizational learning from past accidents and incidents, (ii) Reviewed literature is a mix of both reactive and proactive methodologies of hazard identification (iii) Learning from past events is critical in safety management.

Each day, airlines face disturbances that disrupt their carefully planned operations. Events like adverse weather conditions, sick crew members, or damaged aircraft often result in delays in the airline's schedule. An airline recovers from such disruptions through the role played by its Airline Operations Control (AOC). A Multi-Agent System (MAS) approach to airline disruption management was recently proposed under the acronym MASDIMA (Multi-Agent System for Disruption Management in AOC). The purpose of this paper is to evaluate this MAS supported AOC approach on its performance and its practical introduction. This is done using a scenario-based analysis to compare the MAS supported policy to human-team based AOC policies. A task-based analysis identifies how well AOC is able to cover a set of tasks using the MAS supported policy. The scenario-based analysis shows that the MAS supported AOC is able to find the optimal solution, and to do this significantly faster. The task-based analysis identified two main challenges for implementing the MAS supported AOC policy: i) to overcome the loss of experience that is caused by significantly automating humans roles in AOC, and ii) to reduce the workload for people that remain in AOC after its introduction. The paper concludes that implementing the MAS supported AOC policy leads to both better and faster resolutions, though the replacement of human roles also poses novel challenges that remain to be resolved: a potential increase in workload for the remaining human role and loss of experience in handling exceptional situations.