Resource operators improving asset uptime and worker safety with AI.
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VHMS telemetry streams 1,400+ sensor signals per truck per minute into OSIsoft PI Historian, but maintenance planners review only PDF shift reports generated by a Crystal Reports batch job — meaning actionable fault precursors are invisible until a truck stops moving, causing an average 11.3-hour unplanned downtime per event. Naive AI adoption fails here because a generic anomaly-detection model trained on fleet averages will generate false-positive alert storms (estimated 300+ alerts/day per site), causing alert fatigue that makes planners ignore the system entirely within weeks.
The median lag between a hazard occurring and it appearing as an actionable record in SAP EHS is 6.3 hours, meaning shift supervisors make crew deployment decisions on stale safety data. Naive AI adoption fails here because pit-floor workers wear PPE, operate in 110dB+ noise environments, and have no hands-free input method, so any solution requiring a screen or keyboard will be ignored and compliance will collapse within weeks.
Vibration, temperature, and amperage sensors on 38 critical assets stream to a Wonderware SCADA historian but no predictive model exists, so technicians only act on threshold alarms—catching failures after damage has begun, not before; 61% of corrective work orders in the last year were classified post-failure rather than preventive. Naive AI adoption fails because sensor data is noisy (underground EMI interference), SAP PM work orders are free-text in three languages (English, Afrikaans, Zulu), and Mine Health and Safety Act Section 54 stop-orders mean any false-positive that halts a conveyor triggers a mandatory inspector visit costing 8–12 hours of production.
The 31% out-of-band rate costs Vargfors approximately €4.2M annually in contract penalties and reprocessing, driven by planners manually reconciling grade data from the Surpac block model against real-time conveyor assay readings with a 6-hour lag. Naive AI adoption fails here because a generic LLM hallucinating blend ratios or ignoring shift-change constraints could push the plant further out of band, escalating penalties and triggering a force majeure review by the steelmaker.
Kalderas loses an estimated $210M/year in production value from reactive maintenance and manual ore-grade routing decisions, yet 74% of the operational data lives in PDFs, handwritten shift reports, and siloed historian databases that no single team can query. Naive AI adoption fails here because a generic LLM deployed on top of raw CMMS exports hallucinates equipment IDs, cannot reconcile conflicting sensor timestamps across sites, and produces recommendations that violate ISO 55001 asset-management compliance requirements enforced by Chile's Superintendencia de Minería.