Digital Twin Synchronization Strategies for Real-Time Control of Distributed Production Networks

Abstract

Digital twin synchronization is central to reliable real-time control in distributed production networks, yet current implementations remain constrained by latency, state inconsistency, heterogeneous data structures, limited scalability, cybersecurity exposure, and weak industrial validation. This review critically examines the conceptual foundations, architectures, performance metrics, synchronization strategies, control mechanisms, applications, and research gaps associated with physical–virtual alignment across geographically dispersed manufacturing systems. The reviewed strategies comprise time-driven, event-driven, hybrid, edge–cloud coordinated, AI-driven predictive, and blockchain-based synchronization. Their suitability depends on the required balance among update frequency, state accuracy, communication delay, computational burden, data consistency, security, and control criticality. Evidence from manufacturing, robotics, logistics, energy-aware production, and multi-plant coordination shows that effective synchronization can support closed-loop control, adaptive scheduling, predictive maintenance, resource allocation, energy optimisation, and disturbance response. However, no single strategy performs optimally under all operating conditions. Fixed updates may waste resources, event-triggered methods may overlook gradual drift, predictive approaches remain vulnerable to model error, and secure consensus mechanisms may introduce unacceptable delay. The review therefore positions synchronization as a first-class control and systems-engineering problem rather than a secondary data-management function. Future research should prioritise self-adaptive and uncertainty-aware synchronization, deterministic low-latency communication, causal and federated twin architectures, energy-efficient computing, secure governance, standardised metrics, shared benchmarks, and long-term industrial field validation. These advances are required to move digital twins from passive monitoring tools to dependable operational intelligence systems for distributed manufacturing