Problem
Frequent, unplanned production line stoppages caused by machine failures. Lack of data made it impossible to identify root causes, extending repair times and increasing costs.
Goal
Improving production line efficiency and minimizing unexpected downtime.
Story
A car parts manufacturer was struggling with frequent, unplanned machine downtimes. Repairs were expensive and time-consuming, and addressed only the symptoms – not the root causes. The company had no real-time machine monitoring in place, which made it impossible to analyze performance or detect early warning signs.
Together with the plant team, an IoT-based solution was implemented without interrupting production. Sensors were installed to monitor key machine parameters: temperature, vibrations, and energy consumption. Telemetry data was streamed in real time to a cloud platform that analyzed it for patterns indicating possible failures.
The system automatically notified the maintenance team whenever critical thresholds were exceeded – for example, in case of overheating. This enabled quicker reactions and allowed maintenance to be scheduled before a breakdown occurred.
Based on the collected data, machine settings were optimized to ensure smoother, more stable operation. The company not only solved the downtime issue, but also unlocked new opportunities to boost overall production efficiency.
Results (KPI)
Thanks to the implementation, the manufacturer:
✅ Increased machine availability by 20%, directly boosting production output
✅ Shortened failure response times by 25%, eliminating unnecessary downtime
✅ Reduced maintenance costs by 15% thanks to earlier issue detection
Project summary
Installation of sensors monitoring temperature, vibrations, and energy consumption Real-time data collection and transmission to a cloud platform Only one month in – data analysis revealed overload patterns leading to breakdowns Automatic alert and notification system triggered by risk signals Use of insights to optimize machine scheduling and preventive maintenance planning
Scope of implementation
- Data collection: IoT sensors monitoring key machine parameters
- Cloud analytics: Detection of patterns signaling potential failures
- Automated response: Alerts and recommendations for the maintenance team
- Optimization: Machine configuration adjustments to avoid overload
Technology stack – core and supporting
⚙️ Internet of Things (IoT) – real-time machine condition monitoring
⚙️ Big Data – live data analysis
⚙️ Predictive Maintenance – forecasting failures and planning service
