Aircraft Maintenance, Repair & Overhaul Spare Parts Management

Abstract

Managing spare parts for Aircraft Maintenance, Repair, and Overhaul (MRO) is challenging because there is a significant gap between long-term maintenance schedules and daily procurement decisions. While existing research often addresses demand forecasting and inventory control in isolation using abstract assumptions, a new framework is presented to bridge this gap by directly connecting day-today procurement decisions with the fixed, fleet-wide maintenance schedule. A task-based approach enhances traditional planning, which often relies on aggregate forecasts and can miss the specific needs of individual checks. The result is a transparent, cost-based model built for operational utility, where every decision accounts for probabilistic predictions and remains auditable. This traceability is crucial in an environment where complete historical data is often unavailable. The framework consists of two modular stages. First, a demand forecasting methodology converts raw maintenance tasks into a usable, time-phased, and probabilistic demand signal. To accomplish this, maintenance tasks are systematically grouped based on their technical attributes. The outcome is a repeatable method to describe the demand potential of each scheduled task. Second, a daily procurement optimization model was created to act on this detailed forecast. The algorithm replicates a planner’s decision-making process by explicitly comparing the expected future costs of buying, waiting, or selling surplus stock. To mirror operational reality, the model utilizes regular orders with uncertain lead times, reactive express orders, and pre-procurement. Every decision becomes a justifiable trade-off regarding the cost of purchasing and holding inventory, and the high financial penalty of a stockout. Finally, the model was validated against two benchmarks: a fully conservative (100% service) strategy and a standard periodic-review policy. The proposed model reduced total net costs for both benchmarks, achieving savings of 17.5% and 9.2% respectively. The analysis shows this advantage stems from the strategic acceptance of controlled risks when doing so leads to a lower expected total cost. Further sensitivity analysis revealed that the cost-driven logic is robust even under poor forecasts, as it automatically compensates to maintain a safe inventory level. The analysis also identifies key non-linear trade-offs, finding that total net cost is minimized at a moderate level of caution regarding stockout penalties, rather than at the extremes of underor over-estimation. The framework ultimately provides a practical and transparent decision support tool, demonstrating that a task-specific, dynamic, and cost-based approach is more effective and resilient than traditional, static planning rules.