Impact of AI-Assisted System on Vehicle Maintenance: A System Dynamics Approach

Maintaining the operational readiness of mission-based vehicles is critical for successful mission outcomes, and field maintenance plays a key role in achieving this goal. However, field maintenance faces challenges such as limited resources, low diagnosis and repair capacity, and unpredictable logistic delays. To address these issues and enhance maintenance efficiency, this study explores the integration of Artificial Intelligence (AI) systems in vehicle health management. Using a system dynamics approach, we model the causal interactions and dynamics of the maintenance process, evaluating the impact of AI-assisted health management systems and field conditions on maintenance efficiency. AI capabilities are categorized into three levels: baseline - no AI, failure diagnosis, and failure prognosis, while field conditions include baseline, winter, and desert scenarios. Simulation results highlight the advantages of AI-assisted systems in mitigating adverse effects of harsh field conditions, especially with the failure prognosis. Moreover, the study identifies potential bottlenecks, with the vehicle waiting for spare parts being sensitive to failure rate increases. However, AI capabilities help alleviate the bottleneck effect and improve maintenance efficiency. These findings offer insights into AI integration in vehicle maintenance, showcasing its potential to enhance efficiency and vehicle availability, particularly in challenging field conditions. This system dynamics framework, capturing intricate dynamics of the field maintenance process, holds value for evaluating future AI-assisted vehicle maintenance applications in the field given relevant data.