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Development of new predictive maintenance models in aviation relies on traditional maintenance programs that cost-effectively ensure flight safety. Design individually oriented predictive maintenance programs for each group of units according to the specificity of systems, assemblies and structural elements of modern aircraft requires considering data of behaviour during aircraft operational life cycle (aircraft digital twin). This article describes the current practical approach and proposes a model of predictive maintenance together with airline warehouse stock optimization system and Maintenance Repair and Overhaul (MRO) organization capacity management with particular focus on tires. Analysis of parameters that can potentially affect aircraft nose wheel tires wear and selection of the most relevant factors to build a model for tire replacement forecast using flight data recorders are described in this study. The resulting model is used for the creation of automated production load management system for MRO organization facilities considering their ultimate capacity. The model determines optimal stock or buffer needed for wheel replacement and continuous aircraft fleet operation. The model gives the possibility to calculate the minimum necessary number of spare wheels in the warehouse to ensure continuous operation of a given number of aircraft and a given MRO capacity. As a result of this, I present the Analysis of method’s economic efficiency and evaluate losses associated with early wheel removal and spare wheels capacity.

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