Predict and Prepare
Unexpected events happen regularly at mine sites, but they don’t have to catch you off guard. If your managers feel like they’re constantly putting out fires, there may be hidden patterns driving these disruptions. Advanced analytics, using statistics, machine learning, and AI, can reveal these patterns, allowing you to anticipate problems before they arise and make proactive decisions.
Forecasting Cost and Production Impacts
Adverse events like truck flips, equipment failures, or unexpected delays can have ripple effects on cost and production. By using predictive models, you can estimate how these disruptions impact operations, helping you plan responses that minimize downtime and financial losses.
Predicting Drill and Blast Fragmentation
The size of rock fragments after a blast affects everything down the line. Machine learning models analyze blast parameters, geology, and past fragmentation results to predict outcomes, allowing you to fine-tune drilling and blasting for better downstream performance.
Analyzing Consumable Usage and Deviations
Excessive or unexpected use of consumables can signal inefficiencies or operational issues. By tracking usage patterns and comparing them to planned operations, data-driven insights can highlight areas for cost savings and process improvements.
