Manufacturing · AurvikAI

Predictive Maintenance System

£2M in prevented downtime

A manufacturing group was experiencing 180+ hours of unplanned downtime annually across its production facilities — each hour costing approximately £11,000 in lost production.

The challenge.

A manufacturing group was experiencing 180+ hours of unplanned downtime annually across its production facilities — each hour costing approximately £11,000 in lost production.

Manufacturing engineer reviewing predictive maintenance alerts on a screen

2,000 machines monitored. Failure predicted 72 hours ahead.

The approach.

AurvikAI built an ML predictive maintenance system using sensor data from 2,000+ pieces of equipment. The system detects failure signatures 72 hours before failure occurs — giving the maintenance team time to schedule planned interventions.

The results.

87% reduction in unplanned downtime. £2M annual saving in prevented downtime costs. System now monitoring 2,000+ machines across 4 production facilities. Predictive accuracy of 91% — false alarm rate below 3%.

87%

Reduction in unplanned downtime

£2M

Annual savings

91%

Predictive accuracy

<3%

False alarm rate

What we used.

Machine Learning · IoT · Manufacturing

Python
TensorFlow
IoT Sensors
Time-Series DB
AWS
Edge Computing

Build something like this.

Tell us about your challenge. We'll tell you how we'd approach it.

See more work →