Efficient maintenance scheduling is a critical objective for aircraft carriers and Maintenance Repair and Overhaul Organizations (MROs) to minimize aircraft downtime. While predictive maintenance models have improved, accurately identifying materials, especially spare parts, for
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Efficient maintenance scheduling is a critical objective for aircraft carriers and Maintenance Repair and Overhaul Organizations (MROs) to minimize aircraft downtime. While predictive maintenance models have improved, accurately identifying materials, especially spare parts, for specific maintenance events remains challenging. This paper combines the challenges of identifying spare parts by MROs and aftermarket distributor demand models by developing a robust prediction model (SPSO-CM) to forecast subsequent customer-specific purchases of maintenance planning document (MPD) related spare parts, considering technical documentation and previous sales records. The proposed architecture employs a gradient-boosting algorithm with numerous data mining improvement techniques to predict the likelihood of a subsequent spare part purchase from a customer. A k-means clustering algorithm is used to group spare parts with similar characteristics, as certain specific spare part properties significantly influence demand prediction models. A unique feature selector and nested group K-fold TimeSeriesplit cross-validation method were developed and incorporated into the Bayesian search space to optimize hyperparameters and improve performance. Two test cases were simulated, and the results demonstrated that SPSO-CM is more effective in forecasting proprietary parts and frequently purchased spare parts than those with extremely lumpy demand patterns. Two potential applications for an aftermarket distributor are discussed, one from a customer-level perspective and another at a larger supply-chain level, highlighting its promising capabilities.
