SUPPLIER MANAGED

Senseye

United Kingdom
Southampton
2014
Private
< $10m
51 - 200
Open website
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.
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Senseye PdM is an industrial operations software tool, designed to be used on the shop-floor by the maintenance and operations people who need to keep things running smoothly and ensure that unplanned downtime stays down.

Like all good Industry 4.0 / Industrial IoT software, Senseye PdM is designed to integrate seamlessly and provide maximum value by leveraging your existing investments. It focuses on automatically delivering advanced Predictive Maintenance insights in an easily understandable manner.
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Hardware
Software
Software that ingests data and visualises it to generate insights to answer specific questions
Services
Number of Case Studies3
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.
Predictive maintenance in Schneider Electric
Schneider Electric Le Vaudreuil factory in France is recognized by the World Economic Forum as one of the world’s top nine most advanced “lighthouse” sites, applying Fourth Industrial Revolution technologies at large scale. It was experiencing machine-health and unplanned downtime issues on a critical machine within their manufacturing process. They were looking for a solution that could easily leverage existing machine data feeds, be used by machine operators without requiring complex setup or extensive training, and with a fast return on investment.
Scalable Predictive Maintenance in INSEE
SCCC had committed to running a showcase Digital Factory for the ASEAN region and had already invested heavily in smart factory equipment and sensors. They required a predictive maintenance system that would leverage their existing investments and integrate with their SAP PM maintenance system.
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