Ammunition Management in the AI Era: Towards CBM+ and Shelf-life Analysis

초록

This study delves into ammunition management, focusing on the 81mm mortar high-explosive shell. Leveraging the Ammunition Stockpile Reliability Program (ASRP) and employing robust statistical methods, the research identifies factors influencing shelf-life prediction through outlier detection, multicollinearity assessment, and linear regression analysis. Meticulous analysis of ASRP data reveals key factors like 'Standard Deviation of Mean Ammunition Velocity' and 'Stabilizer,' crucial for determining functional grade and predicted shelf-life. The findings contribute to academic discourse and hold practical implications for the Republic of Korea Armed Forces (ROK Armed Forces). Emphasizing the importance of rigorous testing protocols, the study bridges theoretical insights with practical applications, paving the way for informed ammunition management practices. The developed predictive models and methodologies can inform future studies, enhancing defense capabilities and budget efficiency. © 2023 IEEE.

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