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<p>In the realm of Industry 4.0, digital twins (DTs) are profoundly transforming the way industrial systems are designed, managed, and optimized. By creating a dynamic link between the physical world and its virtual counterpart, they bring new capabilities to production processes. This review explores the foundations, architectures, and essential components of DTs, while clarifying their distinctions from digital models (DMs) and digital shadows (DSs). Through numerous concrete examples, from aeronautics and automotive to energy, healthcare, and smart cities, it highlights the central role played by DTs in monitoring, optimization, prediction, customization, and improving system resilience. Beyond their promises, this review also examines persisting challenges, including interoperability, security, cost, and implementation complexity. Particular attention is given to emerging trends such as the integration of artificial intelligence (AI), extended reality (XR), and reconfigurable systems. In addition, the review emphasizes the growing importance of Manufacturing Execution Systems (MES) (software systems that manage, monitor, and control production activities on the factory floor) and discusses how their integration with DTs enables real-time data exchange, decision-making, and intelligent autonomy. In conclusion, this review offers an innovative perspective: transforming the DT from a simple visualization tool into an intelligent co-pilot of production systems, where simulation, decision, and action converge to build the industry of tomorrow.</p>

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A review on integration of digital twins within industry 4.0

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Wail Tabet,
  • Khaled Benfriha,
  • Belkacem Bounab

摘要

In the realm of Industry 4.0, digital twins (DTs) are profoundly transforming the way industrial systems are designed, managed, and optimized. By creating a dynamic link between the physical world and its virtual counterpart, they bring new capabilities to production processes. This review explores the foundations, architectures, and essential components of DTs, while clarifying their distinctions from digital models (DMs) and digital shadows (DSs). Through numerous concrete examples, from aeronautics and automotive to energy, healthcare, and smart cities, it highlights the central role played by DTs in monitoring, optimization, prediction, customization, and improving system resilience. Beyond their promises, this review also examines persisting challenges, including interoperability, security, cost, and implementation complexity. Particular attention is given to emerging trends such as the integration of artificial intelligence (AI), extended reality (XR), and reconfigurable systems. In addition, the review emphasizes the growing importance of Manufacturing Execution Systems (MES) (software systems that manage, monitor, and control production activities on the factory floor) and discusses how their integration with DTs enables real-time data exchange, decision-making, and intelligent autonomy. In conclusion, this review offers an innovative perspective: transforming the DT from a simple visualization tool into an intelligent co-pilot of production systems, where simulation, decision, and action converge to build the industry of tomorrow.