Fighting Unplanned Downtime: GREIF-VELOX Increases Equipment Availability with Smart Technology

Our goal: sustainable improvements in Overall Equipment Effectiveness (OEE) through digital innovations and intelligent system design.
Downtime as a Major Cost Factor in Industry
The joint study reveals a sobering reality in German industry: 94% of surveyed companies experience at least one unplanned production stop per month, with an average duration of 3.3 hours. The consequences are severe – just one hour of downtime can cost millions, especially in highly automated processes. The economic fallout ranges from missed delivery deadlines and increased personnel requirements to reputational damage.
Common Causes of Downtime
• The study identifies key causes for unexpected production stops:
• Failure of machine components or IT systems
• Lack of transparency on equipment condition
• Delays or difficulties in spare parts procurement

The result: Inefficiencies, reactive maintenance, growing pressure on technical teams – and ultimately, a drop in Overall Equipment Effectiveness (OEE).

By combining intelligent sensor technology, real-time data analysis, and practical support tools, we help customers significantly reduce downtime and secure long-term process reliability.

Predictive Maintenance with VeloGuard
Our condition monitoring solution plays a central role in preventing unexpected breakdowns. It automatically detects typical wear patterns and continuously monitors the health of critical components through self-diagnostic tests. This eliminates the need for time-consuming troubleshooting and allows teams to address potential faults early and with precision – resulting in improved production reliability, predictable maintenance cycles, and drastically reduced downtimes.
Real-Time Insights with VeloInside
This digital real-time dashboard offers all key performance indicators at a glance. Availability, performance, and quality metrics are visualized within an integrated OEE dashboard, making weak points visible and enabling data-driven improvements.