Predictive maintenance that caught equipment failures before they stopped the line

Ardenmill's production lines were running on reactive maintenance — equipment got serviced after it broke, and unplanned downtime was costing entire shifts on their busiest lines.

We instrumented critical machinery with vibration and temperature sensors feeding a time-series pipeline, then trained predictive models on historical failure patterns to flag equipment showing early signs of wear. Maintenance teams now get prioritized alerts days before a failure would have occurred, with enough lead time to schedule repairs around production.

Unplanned downtime on the instrumented lines fell 44% in the first six months, and the maintenance team shifted from firefighting to scheduled, predictable work.