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Scalable Predictive Maintenance in Nissan - Senseye Industrial IoT 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.
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Predictive maintenance in Schneider Electric - Senseye Industrial IoT 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.
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Scalable Predictive Maintenance in INSEE - Senseye Industrial IoT Case Study
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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