System-Level Integration of Optical Measurement, Data Fusion, and Digital Twins in Automotive Service Environments

This paper presents an integrated engineering framework for automotive service systems based on the combination of optical measurement, adaptive data correction, digital twin technologies, and early warning analytics. It is shown that fragmented use of isolated diagnostic tools in real-world service environments leads to reduced measurement reproducibility and operational instability. The proposed system-level approach improves metrological reliability, reduces human-factor-related errors, and enables the development of scalable service management architectures. The study is based on practical implementation results obtained in operating automotive service centers.