A globally renowned food and animal care company were looking for ascalable predictive maintenance solution to play a key role in the ambitious digital transformation programme. Despite extensive experience in the industry, challenges in maintaining a cohesive approach to condition monitoring across operations emerged. Manual analysis, disjointed maintenance analytics, and struggles to align with asset reliability and performance objectives hindered efficiency. Previously, reliance on a specialized condition monitoring contractor for monthly machine inspections provided only intermittent insights into equipment conditions, leading to delays in identifying potential issues. To tackle these challenges, an innovation center in North America dedicated to exploring digital technology solutions for predictive maintenance was established. The aim was toleverage production and retrofitted sensor data to predict machine failures accurately.

Recognizing the need for a highly automated solution that could seamlessly integrate with existing infrastructure and reduce dependency on digital maintenance teams, Senseye Predictive Maintenance was chosen. This solution offered a holistic view of factories and assets, coupled with scalability and ease of deployment. Leveraging insights from one of Senseye Predictive Maintenances largest customers, a global automotive manufacturer, confidence in the solution's simplicity and effectiveness was gained. Senseye Predictive Maintenance empowered maintenance teams to transition from reactive to proactive and prescriptive maintenance, enabling early detection of machine health issues and process anomalies. This shift significantly improved maintenance precision, reduced costs, and mitigated risks associated with manual inspections, while also advancing sustainability and quality objectives.