Key Result
45% reduction in downtime
A mid-size manufacturing facility was losing 12% of production capacity to unplanned equipment downtime. Reactive maintenance was costing the company $1.8M per year in emergency repairs, lost production, and expedited parts procurement.
The maintenance team had no visibility into equipment health trends. Failures occurred without warning, forcing costly emergency shutdowns and overtime labour. Production schedules were regularly disrupted, impacting downstream delivery commitments.
The facility needed a data-driven approach to predict equipment failures before they happened, enabling planned maintenance windows that minimise production impact.
AINinza deployed an IoT sensor pipeline collecting vibration, temperature, and pressure data from 200+ machines across the facility. The data feeds into an ML model that predicts equipment failure 72 hours in advance with 92% accuracy.
The solution includes a real-time monitoring dashboard with configurable alerting thresholds, enabling the maintenance team to schedule repairs during planned downtime windows. The system continuously learns from new data, improving prediction accuracy over time.
45%
Reduction in Downtime
$800K
Annual Savings
3 mo
Payback Period
4 weeks
5 weeks
3 weeks
2 weeks