Forecasting Life Cycle Costs of Weapon Systems: An Open Source Approach With Artificial Intelligence

Military companies and organizations worldwide face the persistent challenge of conceptualizing, procuring, manufacturing, and sustaining weapon systems that align with their respective national strategic imperatives. A pivotal component of this endeavor involves the economic assessment of new systems or the modernization of existing platforms. Nonetheless, accurately estimating the lifecycle costs associated with these systems remains a profoundly intricate task, frequently leading to significant deviations from projected budgets or, in severe instances, the termination of projects. Traditional cost estimation methodologies primarily rely on analogical reasoning and parametric modeling, both of which depend heavily on expert judgment. These approaches are intrinsically sensitive to the degree of detail available in the system's specifications and its corresponding work breakdown structure (WBS). Although commercial proprietary tools have been developed to enhance precision, their adoption is often hindered by substantial costs, the necessity for specialized training, and the opacity of their “black box” algorithms, which undermine confidence in their applicability and reliability. This study investigates the use of artificial intelligence and machine learning methods, in combination with open‐source intelligence (OSINT) data, to forecast acquisition costs for several families of weapon systems (surface ships, transport aircraft, missiles, and helicopters). The proposed workflow integrates extensive data preprocessing, regularized and nonlinear regression techniques, and an analogy‐based clustering stage to derive early‐stage cost estimates from incomplete and heterogeneous public data. These acquisition‐cost forecasts are then linked to standard NATO/Department of Defense (DoD) life‐cycle cost breakdowns to construct illustrative life‐cycle cost scenarios, rather than to develop a fully‐fledged stochastic model of all life‐cycle stages. To our knowledge, this is one of the first studies to demonstrate an end‐to‐end acquisition‐cost forecasting workflow that relies exclusively on OSINT data while explicitly addressing multicollinearity, heteroscedasticity, and outliers and integrating an analog‐based clustering refinement stage.