Network Resiliency and Fault Tolerance through Digital Twins and Data Science

Purpose: As telecom networks evolve with the integration of 5G, 6G, and IoT technologies, their increasing complexity presents significant challenges to maintaining network stability. Traditional management methods are no longer sufficient to ensure the resiliency required in these dynamic environments. Materials and Methods: To address this, we explore the application of digital twin technology as a transformative solution for network operations. Digital twins enable real-time monitoring, predictive analytics, and scenario simulation by creating a dynamic, virtual representation of the telecom network. These capabilities allow for proactive identification and resolution of potential failures, enhancing predictive maintenance and supporting real-time decision-making during network anomalies. The digital twin continuously synchronizes with the live network through integration of data from diverse components, ensuring an up-to-date reflection of operational conditions. Findings: Our analysis identifies key technical and organizational challenges in implementing this approach namely, the complexity of data integration, the demand for scalable architectures, and the necessity for advanced AI-driven analytics to interpret high-volume, high-velocity data effectively. Addressing these challenges is critical to unlocking the full potential of digital twins in telecom settings. The findings suggest that digital twin technology holds substantial promise in improving network resiliency and operational efficiency. Unique Contribution to Theory, Practice and Policy: By enabling telecom operators to shift from reactive to predictive and adaptive network management, this approach offers a robust framework for future-proofing infrastructure in the face of rising complexity. The study contributes to operations research by highlighting a scalable, data-driven pathway to more resilient and reliable telecom networks through the integration of digital twins.