maintenance log analytics convert historical repair records into usable data; and tire management analytics address one of the most cost-sensitive and safety-critical consumable systems on mobile equipment. The result is a shift from fragmented maintenance decision-making to an integrated, data-driven approach that improves equipment availability, reduces maintenance variability and improves spare parts planning. 5. PRODUCTION ANALYTICS: ORCHESTRATING THE UNDERGROUND VALUE CHAIN In the underground deployment, production analytics was built around the practical realities of a mine where connectivity, visibility, and coordination had historically been limited. The system first established a reliable data layer by collecting equipment telemetry from mobile assets and transmitting it through an underground connectivity network. This made it possible to track how machines were operating across the shift, including their movement patterns, utilization, idle time, fuel consumption, and cycle behavior. Once this data became available, the platform converted previously fragmented operating information into a real-time view of production performance. Supervisors and dispatchers could see which assets were active, where delays were emerging, how long equipment was spending in productive versus non-productive states, and how haulage cycles were changing throughout the shift. Rather than relying only on manual reporting or post-shift summaries, the team could identify operational constraints as they developed and make adjustments while there was still time to affect the outcome of the shift. A key function of the production analytics layer was the analysis of haulage performance. The system measured truck activity across repeated cycles, including travel time, loading and dumping patterns, queueing, idling, and route-level inefficiencies. By comparing performance across routes, machines, and shifts, the mine could identify where production losses were occurring and whether those losses were linked to congestion, underutilized equipment, excessive idle time, inconsistent cycle execution, or avoidable delays in the haulage process. The same data also supported short-interval control. Dispatchers and supervisors could use the platform to monitor whether production was progressing as planned and to respond to deviations during the shift. For example, if a route showed recurring delays or if certain assets were spending too much time idle, the team could adjust sequencing, reassign equipment, or investigate the specific cause of the bottleneck. This changed production management from a retrospective reporting process into a more active operating process. Over time, the platform created a continuous improvement loop. Historical data from previous shifts was used to benchmark normal operating patterns, identify recurring constraints, and evaluate the impact of operational changes. Real-time data then allowed the team to apply those lessons during current shifts. This helped improve truck utilization, increase the number of productive trips, reduce non-productive fuel use, and make underground production more predictable.
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