levels. Enabling broader organizational access to structured information fosters alignment between drilling, planning, and downstream operations, contributing to a more integrated production ecosystem. The framework presented in this study is inherently replicable in other large-scale mining environments facing similar interoperability and connectivity challenges. By prioritizing open standards, modular scalability, and energy-oriented analytics, the architecture supports the transition from isolated automation to integrated digital ecosystems. In summary, advancing interoperable digital infrastructures in drilling operations is not solely a technological improvement but a strategic step toward building more efficient, transparent, and accountable mining systems. Such evolution is essential for sustaining responsible mineral supply under the increasing pressures of the global energy transition. 8. CONCLUSIONS This study demonstrates the technical feasibility and operational value of implementing a vendor-neutral, interoperable digital architecture for production drilling in large-scale mining environments. By integrating heterogeneous control systems into a unified data governance framework supported by resilient edge computing and secure wireless transport, the proposed model enables continuous acquisition, structured consolidation, and real-time accessibility of drilling data. The implementation of standardized data structures across multi-vendor fleets significantly improved traceability, reduced reporting inconsistencies, and enhanced comparability between equipment units. The integration of specific energy calculation at the individual hole level provided practical analytical insight into rock–tool interaction dynamics and geological variability, supporting more informed and energy-aware operational decisions. Beyond localized process optimization, architecture establishes a scalable foundation for broader digital integration across the mining value chain. The combination of interoperability, resilience under variable connectivity, and AI-assisted data interaction transforms raw control signals into structured operational intelligence accessible across supervisory levels. In the context of increasing global demand for critical minerals, improving predictability, reducing operational variability, and strengthening data transparency are essential components of responsible mineral supply. The framework presented in this work contributes to this objective by enabling more consistent, traceable, and analytically supported drilling operations. Its modular and scalable design makes it adaptable to other large-scale mining environments facing similar technological fragmentation challenges.
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