Evaluation of Information Retrieving Architectures in Manufacturing for Online Testing Applications

In the rapidly evolving automotive industry, manufacturing centers face increasing demands for precise and timely vehicle information to support complex production processes. This study evaluates the effectiveness of different API architectures in managing and delivering large-scale vehicle models within a manufacturing environment, specifically at the Ford Almussafes plant. The research focuses on assessing two distinct architectural approaches: one that gathers and models data in real-time, and another that pre-models vehicles for efficient data retrieval. Key performance metrics such as latency, CPU usage, and cost are analyzed to determine the most suitable architecture for production systems. The findings indicate that pre-modeling architectures offer significant advantages in stability and cost-effectiveness, despite requiring more complex implementation. This study highlights the importance of aligning API architecture with manufacturing processes to enhance operational efficiency. Future work will explore scalability testing and the evaluation of more complex architectures to further optimize manufacturing workflows.