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The concept of 'digital twins' has gained significant traction in industrial engineering, referring to virtual replicas of physical systems that are continuously updated with real-time data. These dynamic models enable predictive maintenance by simulating potential failures before they occur, thereby reducing downtime and operational costs. However, the efficacy of digital twins hinges on the quality and granularity of the data ingested; incomplete or biased datasets can lead to inaccurate simulations, undermining their reliability. Moreover, the integration of digital twins into legacy infrastructure poses substantial technical challenges, requiring robust cybersecurity measures to prevent data breaches. Recent studies suggest that while digital twins offer transformative potential for optimizing complex processes, their successful implementation demands a holistic approach that addresses data governance, system interoperability, and human expertise. Without such considerations, the technology risks becoming an expensive but underutilized asset.