The biggest challenge was that Ferrolux Manufacturing was still relying on scheduled and reactive maintenance across its plants. Too much of the equipment monitoring was still based on fixed calendars rather than actual machine condition, which meant that failures were often caught only after a line had already stopped. Live health signals from the machines were also not being used systematically, and maintenance decisions could only be made after a breakdown had already occurred. This was holding Ferrolux back; they knew they could protect far more output if they had the ability to predict failures before they happened. The work addressed three critical issues for Ferrolux Manufacturing:

Improve equipment health monitoring across plants:
The teams focused their efforts on a few of the highest-value sensor signals in order to review the current maintenance process, identify gaps in the monitoring coverage and analytically understand failure patterns across machine types.
Determine the right predictive maintenance model:
With thousands of machines across twelve plants, Ferrolux needed a proper method to predict which equipment was at risk of failure. Using historical failure and sensor data, the teams defined appropriate risk-scoring models by mapping actual versus predicted failures on the most critical production lines.
Optimize maintenance scheduling for perfect uptime:
The diagnostic determined the stressors that affected production continuity and maintenance cost. The teams focused on resolving issues related to higher-than-normal unplanned stoppages and reactive repairs, which stressed the entire maintenance operation and led to lost production time.

The solution ALETERIS came up with combined a predictive maintenance model with a multi-tenant SaaS platform built for real-world factory-floor practicality. Everyone knew that maintenance had to become proactive, the real challenge was doing it without requiring a full hardware overhaul across every plant. The solution was to deploy a SaaS platform that ingests existing sensor data, scores equipment risk continuously, and alerts maintenance teams before a failure occurs, while giving plant managers a live risk dashboard across all twelve sites.

This allowed maintenance teams to be involved in planned, proactive repairs instead of reacting to line-stopping breakdowns.