Senseye
Overview
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HQ Location
United Kingdom
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Year Founded
2014
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Company Type
Private
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Revenue
< $10m
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Employees
51 - 200
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Website
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Twitter Handle
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Company Description
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.
IoT Solutions
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.
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.
IoT Snapshot
Senseye is a provider of Industrial IoT application infrastructure and middleware, analytics and modeling, functional applications, sensors, and infrastructure as a service (iaas) technologies, and also active in the automotive, construction and infrastructure, electronics, oil and gas, and retail industries.
Technologies
Use Cases
Functional Areas
Industries
Services
Technology Stack
Senseye’s Technology Stack maps Senseye’s participation in the application infrastructure and middleware, analytics and modeling, functional applications, sensors, and infrastructure as a service (iaas) IoT Technology stack.
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Devices Layer
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Edge Layer
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Cloud Layer
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Application Layer
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Supporting Technologies
Technological Capability:
None
Minor
Moderate
Strong
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Case Studies.
Case Study
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.
Case Study
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.
Case Study
Nissan Manufactures Vehicles in 20 Countries
With an abundance of sensor data but insufficient skilled resources to perform manual analysis, Nissan was keen to expand the benefits of using data and machine learning to influence maintenance. In 2016, it decided to embark on a Predictive Maintenance program to reduce production downtime by up to 50% across thousands of diverse machines.It was attracted to Senseye by its deep domain experience and ability to scale across its sites, underpinned by its patented Artificial Intelligence technology.
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