Insights

Revenue per operating hour gauges how effectively a mining operation converts time into money. A higher value means that each hour of operation yields more output and income – crucial in capital-intensive mining where every hour of equipment time is costly. By optimizing everything from haul truck dispatch to mill throughput with AI, some mining companies have increased their revenue per operating hour by around 5%, which, given the scale of mining operations, can lead to significant financial gains over time.

An AI-first solution for boosting revenue per operating hour integrates real- time decision-making support into operations. For example, AI can prioritize higher-grade ore sections for extraction when market prices are high, ensuring that each hour of operation delivers the most value. Dynamic dispatch algorithms can assign trucks and shovels in a mine to minimize idle time – if one shovel finishes work, the AI immediately reroutes the nearest empty truck to that shovel, keeping both assets productive. In the processing plant, AI adjusts feed rates and energy usage to avoid bottlenecks, so each hour of plant operation produces as much metal as possible without downtime or quality loss. These capabilities can be built

using Microsoft’s advanced analytics. Azure Synapse Analytics can process large volumes of operational data (from fleet management systems, plant control systems, etc.) and feed insights to Azure Machine Learning models that optimize dispatch and processing schedules. Power BI provides a live KPI board, showing revenue per hour and highlighting when it dips (perhaps due to suboptimal resource allocation), triggering further analysis. With AI continuously fine-tuning operations, mines can extract maximum value from every working hour, significantly fueling Revenue per Operating Hour.

Contact us at info@acclero.ai for demos and discussions.