531 fragmentation quality, influences downstream comminution energy intensity, and affects the overall stability and cost structure of the operation. Apparent minor deviations in parameters such as penetration rate, pulldown force, or torque can cascade through subsequent stages, amplifying energy consumption, decreasing predictability, and generating compounding efficiency losses across the production chain. Consequently, leading mining operations worldwide are intensifying their focus on productivity optimization, cost discipline, environmental performance, and operational resilience. Digital transformation has emerged as a strategic enabler of these objectives. Among all production processes, drilling holds strategic relevance because it fundamentally determines: • Blast design effectiveness • Fragmentation distribution and quality • Loading and hauling efficiency • Crusher throughput and specific energy consumption Despite its importance, drilling operations in many large-scale mines still rely on partial automation and manual reporting. Operational data generated by modern drilling rigs often remain underutilized due to: • Heterogeneous equipment manufacturers • Proprietary communication protocols • Lack of unified connectivity infrastructure • Limited interoperability between control and enterprise systems This paper presents a replicable model that transforms operational data into structured intelligence, strengthening productivity, predictability, and accountability in the supply of critical minerals in the face of current global challenges that transforms drilling into a connected, data-driven process aligned with Mining 4.0 principles. 2. STATE OF THE ART IN DIGITAL DRILLING The adoption of digital technologies in mining has advanced rapidly over the past decade, driven by the need to improve productivity, safety, energy efficiency, and sustainability. Within the mining value chain, production drilling has become a focal point for digital transformation due to its direct influence on blasting outcomes, rock fragmentation, downstream comminution efficiency, and overall copper mine performance. The following review synthesizes the most relevant research developments, industrial implementations, and emerging trends in the digitalization of drilling operations. 2.1 Industrial IoT and Real-Time Data Acquisition The integration of Industrial Internet of Things (IIoT) technologies into drilling systems has enabled high-frequency acquisition of operational parameters such as penetration rate, torque, rotation speed, and pulldown force. These time-series datasets provide the foundation for real-time process monitoring and predictive analytics [1], [2]. Huamán and Gómez [3] demonstrated the feasibility of real-time monitoring systems for vibration control in drilling, highlighting measurable improvements in process visibility. Similarly, Petrick and Kaiser [4] describe the evolution of historical data access architectures in process industries, emphasizing their relevance for high-resolution industrial telemetry. 2.2 Industrial Connectivity and Private LTE Networks A central challenge in applying IoT to mining is the lack of robust connectivity in remote and harsh environments. Traditional Wi-Fi and cellular solutions often fail to provide coverage across large open-pit areas. To address this gap, mining operations have increasingly deployed private LTE and 5G networks to enable resilient, low-latency communication between rigs, edge devices, and central servers.
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