Senseye Case Studies Scalable Predictive Maintenance in Nissan

	Scalable Predictive Maintenance in Nissan Senseye

Scalable Predictive Maintenance in Nissan

Senseye

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
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. Read More
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. Read More
Customer Name	Nissan Read More
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. Read More Log in to view content
Contents

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

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.

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

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.

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

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

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Overview

Scalable Predictive Maintenance in Nissan

Operational Impact