How we'd approach predictive maintenance for a mid-sized manufacturer

The Problem

The client's maintenance teams only found out about equipment failures after they'd already happened. Unplanned downtime across 12 production lines was costing real money every year, and the maintenance schedule ran on fixed intervals, not actual equipment condition.

Our Approach

We combined the client's sensor data with a computer-vision model trained on their own equipment footage, built to flag failure risk up to 72 hours in advance. Bivoxo engineers spent the first two weeks on-site mapping every sensor feed before writing a line of code, then validated the model against 18 months of historical failure data before it touched a live line.

Typical target range for this type of engagement

Rolled out across all 12 production lines within a few weeks. Unplanned downtime dropped by 67%, and the maintenance team now schedules repairs around predicted risk instead of reacting to breakdowns.