Digital Twin Integration for Proactive Industrial Decision Support: Enhancing Traccar-Based Monitoring Systems

This paper presents an extended architecture for Digital Egiz, a Traccar-based monitoring system, by integrating Digital Twin capabilities to enhance real-time decision-making in industrial environments. While conventional tracking platforms primarily focus on location and basic telemetry data, our approach creates a bidirectional relationship between physical assets and their virtual representations, enabling predictive analytics and automated decision support. The proposed framework extends the existing three-layer architecture of Digital Egiz by incorporating a decision-making layer that leverages contextual data fusion, predictive modeling, and scenario simulation. We validate our approach through three case studies in different industrial settings, demonstrating significant improvements in maintenance scheduling efficiency (27%), resource allocation (18%), and anomaly detection accuracy (23%) compared to traditional reactive monitoring systems. Results confirm that integrating Digital Twin methodologies with established tracking platforms creates a powerful paradigm for proactive industrial operations, allowing for anticipatory rather than reactive management strategies.