Case study — Manufacturing · Industry 4.0
Sensor-driven predictive maintenance and production monitoring across three plants for an industrial equipment manufacturer.
01 — Challenge
Unplanned downtime was costing more each quarter than the maintenance program meant to prevent it.
Maintenance ran on fixed schedules, not machine condition — some equipment was serviced too often, some failed before its turn came up.
Three plants tracked machine health differently, so there was no single view of where the next failure was likely to happen.
02 — Approach
03 — Solution
Built for the workflow
Predictive maintenance models
Failure risk scored per machine, days ahead of the point where fixed schedules would have caught it.
MES integration
Maintenance signals flow directly into existing MES workflows — no new system for technicians to learn.
Live production monitoring
Real-time status across all three plants in one view.
IoT dashboards
Plant managers see risk and throughput side by side, not in separate reports.
04 — Outcome
“We stopped finding out about failures from the production line. We find out from the dashboard, days earlier.”