Mining
Leading Mining Producer — 23% OEE Improvement
An independently audited 23% improvement in Overall Equipment Effectiveness, achieved through multi-agent operations across predictive maintenance, alarm response, and shift handover.
Outcome
23% OEE improvement (independently audited)
The Challenge
Plants operating at 70–85% of theoretical optimum is not unusual in mining. The gap between current OEE and optimal performance is widely understood, frequently measured, and persistently difficult to close.
The gap is not a data problem. Modern mining operations generate more data than any operations team can act on. The historians are full. The dashboards exist. The problem is a decision problem: the right data exists, but the capacity to act on it — at the right moment, in the right sequence, with the right trade-offs — consistently doesn’t.
Unplanned downtime accumulates from equipment failures that were detectable in advance. Alarm response is slow because operators are managing volume, not analysing signal. Shift handovers lose institutional knowledge — the context that experienced operators carry in their heads doesn’t transfer reliably when a shift changes.
Each of these is a decision gap, not a data gap. And each one compounds.
The Approach
MAGS agents were deployed to operate continuously across asset health monitoring, alarm triage, and maintenance coordination. Agents connected to the operational data that already existed — historians, maintenance systems, alarm logs — and ran continuously against it.
Rather than surfacing more data to operators, the agents took on specific, bounded operational tasks: monitoring asset health parameters and identifying degradation patterns before failure modes develop; triaging alarm floods to surface the signals that require human attention; raising proactive work orders coordinated against production schedules; and generating automated shift briefings that preserve the operational context that would otherwise be lost at handover.
The Outcome
The result was a 23% improvement in Overall Equipment Effectiveness — independently audited, not a projection or self-reported figure. The deployment also delivered a 733% ROI.
The Mechanism
The OEE improvement came from three compounding levers:
Reduced unplanned downtime. Predictive maintenance agents identified equipment degradation patterns before failure events occurred, allowing maintenance to be scheduled rather than reactive. The shift from reactive to proactive maintenance is where the largest OEE gains came from.
Reduced alarm response time. Triage agents operating continuously against the alarm log reduced the time from alarm event to qualified human response. In high-alarm-volume environments, this is not a marginal improvement — it is the difference between a managed fault and a production stoppage.
Improved shift handover quality. Automated shift briefings generated from operational data preserved the institutional knowledge that experienced operators carry between shifts. The briefings captured not just what happened, but the context — the developing patterns, the assets being watched, the decisions pending — that makes a handover effective.
Validated
The 23% OEE improvement is independently audited. When evaluating expected returns from an Agentic Operations deployment, this result is the appropriate reference — but every operation is different, and first deployments are scoped for a specific asset class or process area, not the full operation.
The 733% ROI reflects a production deployment at scale. Early deployments are structured to generate auditable proof before expanding scope. The path to that outcome is measured, not assumed.