Predictive maintenance on a plant-floor data platform

Illustrative case study. Written to show the page structure and the level of detail that performs well in search. Replace with a real engagement from the CMS before launch.

The challenge

Equipment failures halted production lines without warning. Sensor data existed but was written to local logs nobody read, and maintenance ran on a fixed calendar regardless of actual equipment condition.

Our approach

We built the data pipeline before the model — getting 2.4 million daily readings off the plant floor reliably was the harder problem. Once a clean historical record existed, forecasting which components were trending toward failure became tractable. Alerts route into the maintenance team’s existing scheduling tool.

The outcome

The platform now flags likely failures around 72 hours ahead across fourteen production lines. Unplanned downtime fell 18%, and maintenance shifted from calendar-driven to condition-driven.