A major global manufacturer produces vehicles in 20 countries and areas around the world, including Japan, USA, Russia, and the UK. Its global vehicle production volume exceeded 4.7 million in 2020, with products and services provided in more than 190 countries. 

With an abundance of sensor data but insufficient skilled resources to perform manual analysis, they were keen to expand the benefits of using data and machine learning to influence maintenance. In 2016, it decided to embark on a Predictive Maintenance program to reduce production downtime by up to 50% across thousands of diverse machines. 

Senseye Predictive Maintenance’s industry experts support the global automotive mainufacturer for more than five years now. The manufacturer has expanded its predictive maintenance capability across their global production sites where a range of models are produced. 

Over time, they have become autonomous in its adpoption and scaling of their predictive maintenance journey. Together with Senseye Predictive Maintenance Omniverse to upskill their users, their engineers can now on-board new machines and integrate with other enterprise software independent of Senseye Predictive Maintenance. 

Currently, more than 10,000 machines and 100 different machine types are remotely monitored using Senseye Predictive Maintenance proprietary machine learning algorithms, including robots, conveyers, drop lifters, pumps, motor fans, and press/stamping machines. 

Over 500 concurrent users are actively using Senseye Predictive Maintenance to optimize maintenance activities and make repairs months before machine failure.