← All case studies
Clean Energy

Process Intelligence & Predictive Maintenance

An IoT-driven process intelligence and predictive maintenance platform — connecting the operational process to the data that predicts where it will break.

Azure IoT HubFabric RTIPredictive MLPower BI
Clean Energy illustration

The challenge

Plant downtime was being managed reactively, and OEE was a monthly retrospective — by the time the team saw the dip it was already last quarter's problem.

Our approach

How we structured the work, end to end.

01
Mapped the operational process to identify the failure modes that actually drive downtime.
02
Streamed IoT sensor data into Fabric Real-Time Intelligence.
03
Built predictive models for the failure modes most worth catching early.
04
Delivered live OEE and maintenance dashboards the plant team works from daily.

The architecture

From source to insight, in one governed flow.

01
IoT Sensors
02
Event Stream
03
Predictive Model
04
OEE Dashboard

Outcome

What changed
Maintenance is now driven by signals, not schedules. The plant team see OEE move as it moves — and act before downtime hits.

Related work

Other engagements with a similar shape.

Process × Intelligence

Have a similar problem?

Start with a conversation. We'll map the process, pressure-test the goal, and come back with a plan.

Start a Conversation →