Aircraft operations require careful balance between maintaining sufficient crew availability and managing costs effectively. At KLM, current static methodologies for reserve crew planning achieve a 42.8% prediction accuracy on average, leading to either costly overstaffing or potentially disruptive understaffing. This research addresses this operational challenge by investigating the central question: ”How can we develop a self-learning model that enhances the prediction of reserve crew demand in the aviation industry by considering key factors such as seasonal variations and sickness absence patterns?”

Our literature review revealed a significant gap in current research. While studies have explored various aspects of airline operations and crew management, few have addressed the specific challenge of predicting reserve crew demand through machine learning approaches. The literature particularly lacked understanding of how multiple predictive factors interact and how illness patterns impact crew availability.

The research methodology involved systematic analysis of operational data from KLM spanning January 2013 through December 2023, focusing on the Boeing 777/787 fleet and Captain rank. We developed an integrated prediction framework comprising three main components: illness pattern analysis, reserve demand modelling, and operational validation. After evaluating multiple approaches including neural networks (LSTM, CNN) and traditional time series models, we identified an ARIMA(2,1,1) model with exogenous variables as optimal, based on both statistical criteria and operational requirements.

Analysis revealed previously undocumented relationships between operational factors, notably stronger correlation between incidental illness and reserve demand (r = 0.780) compared to structural illness patterns (r = 0.666). The developed model achieved 93% prediction accuracy, translating to conservative cost savings of €3.15 million in the first three quarters of 2024 through improved resource allocation and prevented flight cancellations. The model demonstrated particular strength in capturing dynamic patterns, successfully predicting major disruption events including an unexpected global illness wave in April 2024.
While the implementation shows promise for transforming reserve crew planning, several important challenges and areas for future research remain. The model’s reliance on historical patterns creates potential vulnerability to structural changes, and maintaining consistent data quality across systems presents ongoing challenges. Additionally, the impact of pilot preferences on reserve usage represents a complex system that warrants dedicated future research, as these preferences can significantly influence reserve crew utilization patterns. Despite these limitations, the system has reached implementation maturity, with KLM planning to utilize it for 2025 reserve capacity determinations.

This research establishes new possibilities for airline resource management while providing a framework for similar applications across the aviation industry. The findings suggest that machine learning approaches can significantly enhance prediction accuracy, though careful consideration must be given to operational constraints and data quality requirements. Future research opportunities include investigating more sophisticated approaches to handling extreme events, exploring the integration of additional predictive factors such as weather patterns and Not Fit to Fly data, and developing comprehensive models to account for pilot preferences and their impact on reserve crew demand.