Case Study
Industrial IoT Predictive Maintenance
Machine learning for equipment failure prediction
Eliama Agency — Research Study
“The research showed us how to shift from reactive firefighting to data-driven predictive operations.”
Problem
Manufacturing facilities experience unpredictable equipment failures causing €50K+ downtime incidents. Traditional preventive maintenance is inefficient—replacing parts too early wastes resources, too late causes failures. With 300+ machines, optimization is complex.
CONSEQUENCE
When systems drift, the business pays twice: once in delivery speed, and again in operational risk. Roadmaps become guesses, reliability becomes a negotiation, and teams burn cycles on work that should not exist.
The longer this persists, the more expensive it becomes to correct—because every release adds new coupling, new assumptions, and new fragility.
Solution
We researched an IoT sensor deployment across machine fleets with real-time monitoring dashboards and ML models predicting failures 48-72 hours in advance. Analysis focused on vibration patterns, temperature anomalies, and performance degradation to trigger proactive maintenance.
Outcome
Research suggests potential 70% reduction in unplanned downtime, 30% decrease in maintenance costs through optimized scheduling, and 10-15% production throughput improvement. Payback period estimated at 14-16 months.
Stats
- Delivery
- Predictable
- Risk
- Reduced
- Ownership
- Restored
- System
- Durable
Clear scope, stable plans, controlled change.
Operational failure modes identified and removed.
Teams can operate and extend safely post-handover.
Built to survive change, not just ship once.