An AI-enhanced Approach for optimizing life cycle costing of military logistic vehicles

Individual usage profile (aka true usage profile) of military logistic vehicles (MLV) is highly dependent on types of operations, duration and areas of use. Compared to non-military vehicles, the operation life is considerably longer, mileage tends to be several times lower and the characteristics of the application area, including environmental factors, is much more diverse. Transparent and realistic determination of MLV’s true usage profile facilitates, inter alia, optimal life cycle costing in particular operation costs. In practice, however, effects of non-usage, such as material degradation or damage during service of the vehicles are not explicitly recorded. Important aspects of assets management, such as identifying maintenance measures and logistic disposition calculations, are often conducted non-systematically and non-data driven but rather based on human experiential knowledge and manufacturer’s specifications. Hence, the modelling and predictive analysis of MLV’s true usage profile is subject to significant uncertainties. The body of knowledge in assets management does not consider the aforementioned problems. Therefore, lack of novel AI-enhanced approaches for modelling MLV’s true usage profile is evident. This paper introduces an integrative, data-driven approach for identifying the true usage profile and thus improving the life cycle costing of MLV. The proposed approach involves an AI-enhanced analysis of heterogeneous data sources, such as sensor-, vehicle control-, GPS-, and logistics data as well as textual logbooks. It enables a transparent and realistic identification of MLV’s true usage profiles, which facilitates accurate and timely calculation of life cycle costs under consideration of data quality properties. Finally yet importantly, classification of usage profiles enables a data-driven decision support system to provide i) proactive maintenance measures, and ultimately ii) to optimize distribution of vehicles and spare parts in decentralized warehouses.