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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, reducing downtime by anticipating equipment failures before they occur. However, the implementation of digital twins is not without challenges. High-fidelity simulations require substantial computational resources and vast datasets, which can be prohibitively expensive for small-to-medium enterprises. Moreover, the reliance on interconnected sensors raises cybersecurity concerns, as a breach could compromise both the virtual model and the physical asset it mirrors. Despite these obstacles, proponents argue that the long-term efficiency gains and cost savings justify the initial investment, particularly in sectors like aerospace and energy, where unplanned outages carry severe economic and safety repercussions.