Non-Routine Maintenance Workload in Aviation: A Comparative Analysis of Forecasting Methods

Aircraft maintenance scheduling is a focus point for airlines. Maintenance is essential to ensure the airworthiness of aircraft, but it comes at the cost of rendering them unavailable for operations. In current operations, aircraft maintenance scheduling must often be updated to include time for non-routine (i.e., non-schedule) tasks. Non-routine maintenance tasks (NRTs) introduce significant uncertainty into aircraft maintenance scheduling, often leading to delays and increased operational costs. Despite their importance, limited research has been conducted on predicting NRT maintenance workload using data-driven methods. This study compares Random Forest and LGBM with a Weibull survival model for NRT workload prediction at the individual routine task (RT) level. Results indicate that model performance depends on data availability and the evaluation metric: using data from all tasks can help in sparse settings, while using an independent occurrence model to correct labor predictions may improve aggregate error at the cost of other outcomes. The historical-average baseline performed comparably to Independent LGBM on MAE, ROC-AUC, and average precision. Finally, results show that summing task-level predictions does not result into a good forecast for the work packages that contains all the tasks. Further data and prospective validation are needed before these estimates can guide work-package planning.