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Predictive Maintenance AI Cut Unplanned Downtime for a Manufacturer

IndustryManufacturing
ServiceManufacturing
ManufacturingManufacturing
40%Less unplanned downtime
18%Higher OEE
FasterMean time to repair

About the Client

The client is a mid-size discrete manufacturer running several production lines across two plants. Reliable output depended on ageing equipment, but maintenance was largely reactive and machine data sat unused in isolated OT and IoT systems.

Business Challenges

Business Challenges

Unplanned downtime was the client's biggest source of lost output. Maintenance ran on fixed schedules and gut feel, failures surfaced only after a line had already stopped, and telemetry from PLCs, sensors, and the MES never came together in one place to warn teams early.

Webuters’ Solution

<p>Webuters connected the client's OT and IoT data into one layer and added a predictive-maintenance model on top, so degradation was caught while the line was still running.</p>

OT & IoT data integration

Unified telemetry from PLCs, sensors, and the MES into a single, AI-ready operational layer.

Anomaly detection

Modelled normal machine behaviour and flagged early drift long before a hard failure.

Failure prediction

Predicted likely failure windows per asset so maintenance could be planned, not reactive.

Maintenance scheduling

Turned predictions into prioritized work orders aligned to production schedules.

Webuters’ Solution

The Impact

Measurable business impact
Less unplanned downtime
40%
Early warnings let teams intervene before failures stopped the line.
Higher OEE
18%
Fewer surprise stoppages and better-planned maintenance lifted overall equipment effectiveness.
Mean time to repair
Faster
Knowing what was failing and why shortened diagnosis and repair time.
Connected machine data
One layer
OT, IoT, and MES data finally shared one intelligence layer.

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