Case Study: Nissan reduces downtime and improves OEE with Senseye Predictive Maintenance

Nissan - Customer Case Study

Nissan, a global vehicle manufacturer operating in 20 countries and producing more than 5.6 million vehicles annually, wanted to better use its abundant data to improve maintenance and reduce production downtime across thousands of diverse assets. With limited skilled resources for analysis, Nissan turned to Senseye and its machine learning-based predictive maintenance and prognostics capability.

Senseye implemented predictive maintenance across multiple Nissan production sites, remotely monitoring 9,000 connected assets and more than 30 machine types, including robots, conveyors, pumps, motors, and press/stamping machines. The solution has helped Nissan save millions in unplanned downtime, achieve a return on investment in under 3 months, gain 2 weeks to 6 months of advance warning for asset failures, and improve OEE year over year.

Nissan

Damian Wheeler

Engineering Director