Digital Twin in Open-Pit Mining: A Systematic Review and Maturity-Based Assessment of Deployment and Operational Reality

Résumé fourni par la source

Digital twin (DT) technology is increasingly positioned as a transformative framework for cyber-physical integration in open-pit mining; however, its actual level of maturity and operational deployment remains unclear. This study presents a PRISMA-compliant systematic review of 120 publications (2016–2026) to critically evaluate DT applications across exploration, extraction, safety management, and autonomous operations. A key contribution is the development of a five-level Digital Twin Maturity Model (DT-M²), which classifies systems as Digital Models (L0), Digital Shadows (L1), Basic Digital Twins (L2), Intelligent Digital Twins (L3), and Autonomous Digital Twins (L4). Application of this framework reveals that 56.7% of studies meet the minimum criteria for digital twins (L2–L4), although most operate at early-stage functionality, and only 10.8% demonstrate fully autonomous capabilities. Quantitative evidence from case-specific deployments indicates substantial but context-dependent performance improvements, including up to 44% increases in excavator output, 98.64% resource utilisation, and 40% reductions in slope risk management costs. However, these outcomes are not generalisable across the sector and are concentrated in highly instrumented operations. The review further identifies systematic overstatement in the literature, including misclassification of digital models and shadows as digital twins (43.3% of studies) and the extrapolation of site-specific performance metrics to industry-wide expectations. To bridge the gap between research and practice, a Digital Twin Adoption Roadmap (DT-AR) is proposed, outlining phased implementation pathways, capability milestones, and indicative investment requirements. Critical limitations are also identified, including geological data constraints, lack of standardised architectures, cybersecurity vulnerabilities, and workforce skill gaps. This study provides a structured, evidence-based assessment of the current state of digital twins in open-pit mining, offering both a rigorous classification framework and practical guidance for future research, investment, and deployment.

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