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.

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.

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.

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.

Seeks to improve operational decision-making, safety management and sustainability.A key element in these initiatives is asset maintenance, representing approximately 25% of Cameco’s overall operating costs at its mining operations. Improving asset management began with automating data collection.Cameco had struggled to analyze multiple regular condition monitoring data from assets in the past. As a result, the company found it hard to understand what went wrong in the event of asset failure.