Scalable Predictive Maintenance in Nissan
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
- Functional Applications - Enterprise Asset Management Systems (EAM)
- Functional Applications - Remote Monitoring & Control Systems
- Predictive Maintenance
With an abundance of data and insufficient skilled resources to perform analysis, Nissan were keen to expand the benefits of using data to influence maintenance. It decided to embark on a Condition Based maintenance programme to reduce production downtime by up to 50% across thousands of diverse assets. It was attracted to Senseye by its strong prognostics offering underpinned by machine learning.
Senseye is providing Predictive Maintenance capability across multiple global Nissan production sites where models such as the Qashqai, X-Trail, Leaf and Infiniti are produced. 9,000 connected assets and more than 30 different machine types including robots, conveyors, drop lifters, pumps, motors and press/stamping machines are remotely monitored using Senseye’s proprietary machine learning algorithms. More than 400 maintenance users actively use Senseye to optimize maintenance activities and make repairs months before predicted machine failure.