Analyzing Open-Source Data to Inform Military Vehicle Design

The modern battlefield is increasingly transparent, generating large volumes of open-source data on the use, damage, and loss of military vehicles. This paper presents a structured methodology to exploit such data for deriving operational requirements for future vehicles. It uses a mixed-method framework combining qualitative reporting with quantitatively verified loss data. Daily battlefield reports are analyzed with large language models to extract operational context, employment patterns, and tactical conditions. These insights are cross-referenced with loss data to assess how operational factors affect vehicle survivability, with the findings being used to prioritize requirements that improve vehicle performance. The approach is demonstrated through a case study of Leopard tanks in the Russia-Ukraine war, using Institute for the Study of War reports and Oryxspioenkop loss data. Results show how open-source intelligence can systematically inform survivability, mobility, and combat effectiveness in modern vehicle design.