Client: Confidential (Industrial manufacturer, UK)
Problem:
Frequent machinery downtime and reactive maintenance cycles caused significant production delays and financial losses. The client lacked a data-driven way to predict and prevent failures.
Solution:
BBI AI Labs designed a predictive analytics platform that leveraged real-time sensor data and machine-learning algorithms to forecast potential equipment failures before they occurred.
Implementation:
- Collected live data from temperature, vibration, and pressure sensors across factory lines.
- Built predictive models to detect anomalies and estimate time-to-failure.
- Developed a dashboard displaying machine health scores and maintenance alerts.
- Integrated automated email and Teams notifications for proactive servicing.
- Continuous model refinement using technician feedback and service logs.
Results:
- 50% reduction in unplanned downtime.
- 30% lower maintenance costs.
- 20% longer average equipment life.
- Improved production scheduling and workforce efficiency.
