System Variations and Their Impact on the Validation of Digital Twin Models in Dynamic Manufacturing Environments

Digital Twins, as near real-time virtual replicas of physical systems, provide insight into systems’ behaviors, supporting optimization of manufacturing processes. The dynamic nature of modern manufacturing systems, however, poses unique challenges for validation of the underlying simulation models of the corresponding Digital Twins. Model validation, as an integral part of Digital Twin frameworks, ensures that Digital Twin models accurately reflect their real-world counterparts over time. In today’s flexible manufacturing systems, frequent technological upgrades, workforce changes, and operational modifications can all play a part in impacting Digital Twins’ accuracy. Consequently, Digital Twins require robust validation processes capable of detecting and reflecting these variations in manufacturing systems. In this paper, we present a comprehensive overview of the diverse types of system variations in manufacturing environments, categorizing them into machine-intensive, labor-intensive, and hybrid manufacturing systems. We analyze how these variations influence Digital Twin validation and highlight key challenges in maintaining Digital Twin fidelity in evolving manufacturing contexts. Finally, we discuss future research directions to enhance validation methodologies, ensuring that Digital Twins remain reliable and adaptive amidst continuous system variations.