⚙️ Predictive maintenance success: moneo at Nissha [Use-Case]

Nissha Metallizing Solutions, a global leader in high-quality metallised paper for premium packaging and labelling, leverages predictive maintenance to significantly increase production efficiency. By combining intelligent ifm sensor technology with AI-powered analytics in moneo, the company gains continuous transparency into machine health and minimises unexpected downtime. At the Italian site, real-time monitoring of vibration, temperature, pressure and humidity provides the data foundation for early anomaly detection. This predictive approach allows the maintenance team to identify wear patterns, address issues before failures occur and keep critical assets running reliably. What you will learn in the video: • How predictive maintenance boosts uptime and operational stability • How ifm sensors deliver accurate, real-time condition data • How moneo’s AI identifies anomalies before they become failures • How data-driven insights enable proactive maintenance planning Why ifm? • Complete transparency: sensors + connectivity + analytics • Early detection prevents costly, unplanned stops • Scalable predictive maintenance across multiple production sites 💡Find out more about our success stories: https://www.ifm.com/cnt/application-reports