Airline operators face accidental damages on their fleet of aircraft as part of operational practice. Individual occurrences are hard to predict; consequently, the approach towards repairing accidental damage is reactive in aircraft maintenance practice. However, by aggregating occurrence data and predicting future occurrence rates, it is possible to predict future long-term (strategic) demand for maintenance capacity. In this paper, a novel approach for integration of reliability modelling and inventory control is presented. Here, the concept of a base stock policy has been translated to the maintenance slot capacity problem to determine long-term cost-optimal capacity. Demand has been modelled using a superposed Non-homogeneous Poisson Process (NHPP). A case study has been performed on damage data from a fleet of Boeing 777 aircraft. The results prove the feasibility of adopting an integrated approach towards strategic capacity identification, using real-life data to predict future damage occurrence and associated maintenance slot requirements.

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Airline Maintenance and Engineering (M&E) organizations face accidental damages on their fleet of aircraft as part of daily practice. As this type of damage is stochastic in nature, the approach towards repairing accidental damage is reactive in practice. However, it is possible to predict future long-term (strategic) demand for maintenance resources associated with accidental damages and use this to identify required capacity. To achieve the mutually related goals of prediction of future repairs and determination of capacity, a novel approach for integration of reliability modelling and inventory control is presented in this paper. Here, the concept of inventory control has been specifically applied to determine the maintenance capacity by taking into account the stochastic demand related to unscheduled repairs following from accidental damages. To predict demand, a Non-homogeneous Poisson Process (NHPP) reliability model has been adopted. The reliability model includes superpositioning, through which failure behaviour at aircraft fleet-level can be estimated and subsequently simulated. The resulting demand is fed into a single-system, single location base-stock inventory model. This allows for determination of strategic capacity based on optimum costs as well as service level requirements. A case study has been performed on a fleet of Boeing 777 aircraft of a major European airline. The results prove the feasibility of adopting an integrated approach towards strategic capacity identification, using real-life data to predict future demand occurrence.@en