Scalable Predictive Maintenance in Nissan

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.

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  • SUPPLIER
  • Senseye
    Senseye is the leading cloud-based software for Predictive Maintenance. It helps manufacturers avoid downtime and save money by automatically forecasting machine failure without the need for expert manual analysis. Its intelligent machine-learning algorithms allow it to be used on any machine from any manufacturer, taking information from existing Industrial IoT sensors and platforms to automatically diagnose failures and provide the remaining useful life of machinery.
  • TECHNOLOGIES
  • Analytics & Modeling - Machine Learning
    Analytics & Modeling - Predictive Analytics
    Functional Applications - Enterprise Asset Management Systems (EAM)
    Functional Applications - Remote Monitoring & Control Systems
  • INDUSTRIES
  • Automotive
  • FUNCTIONS
  • Maintenance
  • USE CASES
  • Predictive Maintenance
  • ABOUT 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.
  • CUSTOMER NAME
  • Nissan
  • CONNECTIVITY PROTOCOLS
  • 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. 

  • DATA COLLECTED
  • OPERATIONAL IMPACT
  • Impact #1
    [Cost Reduction - Production]

    Multi-million dollars of unplanned downtime saved to date

    Impact #2
    [Process Optimization - Predictive Maintenance]

    2 weeks to 6 months advance warning of asset failure

    Impact #3
    [Efficiency Improvement - OEE]

    Year-on-year OEE improvements

  • QUANTITATIVE BENEFIT
© 2022 IoT ONE

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