AI-Powered Predictive Maintenance for a Large Industrial Manufacturer
Challenge
The client is a large industrial manufacturer in Central Europe that produces machinery components across multiple plants. A few years earlier, SumatoSoft had built an IoT system that streamed vibration, temperature, and pressure data from shop-floor equipment into dashboards and alerts.
As production scaled and new machine models arrived, the system grew less accurate and produced more false alarms, because it could not model how signals interact with ambient temperature and workload. The client wanted adaptive, context-aware predictions without disrupting production.
The client is a large industrial manufacturer in Central Europe that produces machinery components across multiple plants. A few years earlier, SumatoSoft had built an IoT system that streamed vibration, temperature, and pressure data from shop-floor equipment into dashboards and alerts.
As production scaled and new machine models arrived, the system grew less accurate and produced more false alarms, because it could not model how signals interact with ambient temperature and workload. The client wanted adaptive, context-aware predictions without disrupting production.
Solution
SumatoSoft upgraded the IoT platform with an explainable machine learning layer linked to the maintenance workflow.
A hybrid engine combines a supervised XGBoost model for risk scoring on known equipment with isolation-forest and autoencoder anomaly detection for machines with little failure history. The models were trained on about 20 TB of multi-year sensor data and recalibrate automatically as new data arrives. Each alert shows its key contributing factors. The rollout ran as an 8-week pilot followed by six phases, line by line, without stopping production.
SumatoSoft upgraded the IoT platform with an explainable machine learning layer linked to the maintenance workflow.
A hybrid engine combines a supervised XGBoost model for risk scoring on known equipment with isolation-forest and autoencoder anomaly detection for machines with little failure history. The models were trained on about 20 TB of multi-year sensor data and recalibrate automatically as new data arrives. Each alert shows its key contributing factors. The rollout ran as an 8-week pilot followed by six phases, line by line, without stopping production.
Results
- Unplanned downtime cut by nearly 50%
- 2.5x ROI within 8 months
- 40% fewer false alerts
- 35% improvement in early fault detection
- Rolled out line by line in six phases without interrupting production
- Delivered by a team of 7 specialists over 6+ months
- Unplanned downtime cut by nearly 50%
- 2.5x ROI within 8 months
- 40% fewer false alerts
- 35% improvement in early fault detection
- Rolled out line by line in six phases without interrupting production
- Delivered by a team of 7 specialists over 6+ months