Optimization of ammunition preparation strategies for modern artillery operations in computer simulation

Abstract

The experience of modern warfare, particularly from public reports on the Russia-Ukraine conflict, highlights significant changes in military strategies, tactics, and technology.

The heavy reliance on artillery and the high demand for shells pose major logistical, storage, and strategic challenges. Poor-quality ammunition can reduce combat effectiveness, damage equipment, jeopardize operations, and put personnel at risk, creating a cascade of additional problems.

The study was aimed at studying the effectiveness and optimization of the additional quality control strategy for ammunition. The focus was on acceptance sampling algorithms to maintain high productivity while optimizing inspection efficiency. The impracticality of 100 % inspection was taken into account.

The study develops and implements specialized acceptance sampling plans adapted to the unique quality and operational requirements of each type of artillery mission. Using iterative calculations, optimal sample sizes and acceptance criteria are established to meet predefined quality levels, minimizing resource consumption and inspection time. The developed sampling plans are structured to find balance between the allowed number of defects and inspection efficiency, ensuring that high-quality ammunition is allocated for destructive fire missions, while properly inspected but larger batches of ammunition are allocated for suppressive fire combat missions.

The new quality control step could be added to the game scenarios of ARMA 3, or to any other warfare simulations, and show that the acceptance plan strategy effectively reduces costs, increases operational safety and ensures readiness for artillery missions. The proposed statistical methods provide a reliable and adaptable approach for integrating quality control into the preparation of artillery ammunition, ensuring reliable supply in difficult combat conditions.