The challenge
On a high-volume line, every unplanned stoppage is expensive, and the warning usually arrived too late. Equipment data, supply status and quality signals lived in separate systems, so problems only became visible once they had already become incidents. Resilience depended on the experience of individuals, not on a system everyone could rely on.
Our approach
We treated resilience as something to engineer, not hope for. We mapped the failure modes that actually stopped the line, identified the signals that preceded them, and brought those signals into one place so risk could be seen early and acted on with a clear plan.
- The critical failure modes that cause real downtime, made visible
- Equipment, supply and quality signals unified in one view
- AI surfacing the early indicators a human would miss
- Clear playbooks so a warning leads to a response, not a debate
What we built
A resilience layer over the existing plant: it watches the signals that matter, predicts where disruption is building, and gives the operations team early warning with a recommended action. Routine interventions are scheduled before failure rather than after it, and supply risk is visible alongside equipment risk in a single picture.
The results
The team moved from reacting to incidents to heading them off. Maintenance shifted from firefighting to planned intervention, and disruption that would once have halted production is now absorbed because it was seen coming.
- Unplanned downtime reduced through early intervention
- Maintenance shifted from reactive to predictive
- Supply and equipment risk visible in one place
- Resilience built into the operation, not held in people's heads
How it runs
We run it as a managed service: monitoring the signals, tuning the models, and continuously improving the playbooks as the operation changes, with uptime and response measured against agreed SLAs. The plant keeps running, and one partner is accountable for keeping it that way.