Human Error Mitigation: Providing interventions to assist uncertainty quantification and improve the safety management system in aircraft maintenance in the aviation industry.

  1. Yannakides, Demitris
  2. School of Business |
  3. Department of Management
  4. English
  5. 327 p.
  6. Drikakis, Dimitris
  7. Epaminonda, Epaminondas | Ktoridou, Despo
  8. Aviation | Constructivist | Human Error | Human Factors | Mixed Methods | Modelling | Reliability | Risk | Safety Management System (SMS) | Uncertainty Quantification
    • Human-centric uncertainty is a persistent challenge in aviation maintenance and a significant contributor to human factors-induced errors (HFiEs). Despite the progress of safety frameworks such as the Safety Management System (SMS), the Systems-Theoretic Accident Model and Processes (STAMP), and the Functional Resonance Analysis Method (FRAM), current models lack the capacity to capture the dynamic variability of human performance under operational conditions. This doctoral research addresses this gap by developing the Uncertainty Quantification in Aircraft Maintenance (UQAM) framework, an integrated, mixed-methods approach that bridges interpretive inquiry with quantitative modelling. The framework introduces the Integrated Uncertainty Equation (IUE), which combines eight empirically derived uncertainty factors into a single scalar score, enabling task-specific assessment of risk. The study comprises two epistemologically linked but methodologically distinct phases. The qualitative phase, grounded in a constructivist/interpretivist paradigm, explored engineers’ perceptions of error, human factors, and safety defences through 49 semi-structured interviews and ethnographic observation across civil and military maintenance organisations. These interpretive findings informed the design and parameterisation of the IUE. The subsequent quantitative phase validated UQAM through a 12- month field study of four representative maintenance tasks, confirming that the IUE effectively differentiates low, moderate, and high-risk scenarios while adapting to contextual variability. UQAM was further examined for integration into STAMP, FRAM, and SMS, demonstrating its potential to enhance predictive safety management and regulatory practice. By linking meaning-making with measurement, this thesis contributes a coherent, future-ready model for uncertainty-aware safety architectures in aviation and other safety-critical domains.
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