Predictive Maintenance Pro
A manufacturer faced costly unplanned downtime across a fleet of high-value industrial assets. We built ML-driven predictive maintenance models that reduced unplanned downtime by 55%, cut maintenance costs by 30%, and achieved 92% prediction accuracy.
Key Outcomes
The Challenge
The manufacturer operated a fleet of high-value industrial assets across multiple sites, each with different sensor configurations and maintenance schedules. Unplanned downtime cost upwards of $500K per incident, yet the existing time-based maintenance programme either intervened too early (wasting parts and labour) or too late (causing failures). Maintenance teams had no visibility into real-time equipment health and relied on manual inspections.
Our Solution
We ingested sensor telemetry from across the fleet into a unified data lake, then trained anomaly-detection and remaining-useful-life models tailored to each asset class. A real-time scoring pipeline surfaced maintenance recommendations via mobile dashboards, enabling condition-based interventions. Unplanned downtime fell by 55%, maintenance costs dropped by 30%, and prediction accuracy reached 92%.
