Senseye > Case Studies > Scalable Predictive Maintenance in Nissan

Scalable Predictive Maintenance in Nissan

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 Scalable Predictive Maintenance in Nissan - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
  • Analytics & Modeling - Predictive Analytics
  • Functional Applications - Enterprise Asset Management Systems (EAM)
  • Functional Applications - Remote Monitoring & Control Systems
Applicable Industries
  • Automotive
Applicable Functions
  • Maintenance
Use Cases
  • Predictive Maintenance
The Customer
About The Customer
Nissan manufactures vehicles in 20 countries and areas around the world, including Japan, USA, Russia and the UK. Its global vehicle production volume exceeded 5.6 million in 2016, with products and services provided in more than 160 countries.
The Challenge

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.

The Solution

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. 

Operational Impact
  • [Cost Reduction - Production]

    Multi-million dollars of unplanned downtime saved to date

  • [Process Optimization - Predictive Maintenance]

    2 weeks to 6 months advance warning of asset failure

  • [Efficiency Improvement - OEE]

    Year-on-year OEE improvements

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