Problem
A food production plant faced repeated issues on its packaging line, where machine failures caused frequent downtime. High repair costs combined with reduced output resulted in double financial losses.
Goal
Minimize losses caused by machine failures and production stops. Increase efficiency by improving performance on the packaging line.
Story
The packaging line in the food factory suffered from frequent failures of key machines, causing an average of 4 hours of downtime per day across the year. Production was regularly disrupted by unexpected technical issues that required costly repairs and halted operations.
Together with the plant team, Predictive Maintenance was chosen as the implementation strategy. Sensors were installed to monitor machine parameters in real time – in this case, vibrations, temperature, and pressure. Machine learning–based analytics began recognizing equipment behavior patterns and predicting failures in advance.
Just three months into the implementation, the system identified a potential failure in a critical machine component. The maintenance team verified the alert and performed a scheduled service to replace worn parts during the night shift, avoiding any production downtime.
As a result, the factory reduced the number of failures by 40%, saving over €23,000 (100,000 PLN) in downtime and repair costs over the year.
Results (KPI)
Thanks to Predictive Maintenance, the factory:
✅ Reduced the number of failures by 40%, increasing machine availability
✅ Minimized downtime by 25%, improving production efficiency
✅ Saved over €23,000 (100,000 PLN) in repair and downtime costs
Project summary
Installation of sensors monitoring temperature, vibrations, and energy consumption on packaging machines.Real-time data collection and transmission to a cloud-based analytics platform Within three months – the system detected abnormal patterns indicating failure risk Automatic alerts regarding component wear allowed for planned maintenance Thanks to preventive action, repair was done overnight, minimizing production losses
Scope of implementation
- Data collection: IoT sensors monitoring key machine parameters
- Data analytics: Failure pattern recognition through machine learning algorithms
- Automated response: Notifications for maintenance and recommendations for the team
- Schedule optimization: Repairs planned at times least disruptive to production
Technology stack – core and supporting
⚙️ Internet of Things (IoT) – machine condition monitoring sensors
⚙️ Big Data – real-time data analytics
⚙️ Predictive Maintenance – forecasting failures and scheduling service
