In the context of automotive production, the variability in component tolerances poses a significant challenge, often leading to production downtimes. Deviations in dimensional accuracy and geometry can disrupt automated assembly processes, emphasizing the critical link between input quality and assembly outcomes. As automation gains prominence, robust process monitoring becomes imperative for quality assurance and maintenance cycle optimization. However, a prevalent issue persists data fragmentation and underutilization in production. Our approach is centred on leveraging existing production data to optimize automotive production and streamline final assembly processes. We investigate the interplay of upstream process influences on downstream assembly processes within large-scale production environments. Real-world manufacturing data is analysed and validated through finite element analysis (FEA) simulations coupled with Monte Carlo simulations. Focusing on one specific automotive assembly process—the automated assembly of cockpits—we examine the influence of various body concepts on assembly automation compatibility. Data analysis from two assembly lines identifies body concepts conducive to efficient assembly, subsequently validated using our innovative coupled simulation method. This holistic approach identifies production-suitable product concepts and integrates our novel simulation method into the design of future products, bridging the gap between production data fragmentation and optimized assembly in the automotive industry. By connecting real manufacturing processes with product concept suitability, we enhance production efficiency, advance data mining and simulation methods, and improve overall production quality.