What question did this study set out to answer?

The aim is to develop a method for accurately forecasting life cycle costs of weapon systems using open-source data and AI techniques.

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

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Study demonstrates an AI-based workflow to forecast life cycle costs in military weapon systems, suggesting enhanced accuracy.

The aim is to develop a method for accurately forecasting life cycle costs of weapon systems using open-source data and AI techniques.
Utilization of artificial intelligence and machine learning for cost forecasting
Integration of open-source intelligence (OSINT) data
Incorporation of regularized and nonlinear regression techniques
Application of analogy-based clustering to refine cost estimates
Linking forecasts to standard NATO/DoD life-cycle cost breakdowns
Demonstrated an end-to-end acquisition-cost forecasting workflow
Successfully addressed issues like multicollinearity, heteroscedasticity, and outliers
Provided early-stage cost estimates from incomplete public data
Developed illustrative life-cycle cost scenarios without a fully-fledged stochastic model

Cuesta et al. (2026) studied this question.

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