Siemens acquires predictive maintenance software specialist
Siemens has expanded its portfolio in the field of predictive maintenance and asset intelligence with the acquisition of Senseye, a UK based provider of AI-powered solutions for industrial machine performance and reliability.
Senseye’s predictive maintenance software has been shown to enable a reduction in unplanned machine downtime of up to 50%, and increase maintenance staff productivity by up to 30%.
Since 1 June, the company is a 100% subsidiary of Siemens in the UK. It is assigned organisationally to Siemens Digital Industries and part of the Customer Services Business Unit.
“Senseye’s AI based solutions complement our digital services portfolio driving efficient and scalable predictive maintenance,” said Margherita Adragna, CEO of Customer Services for Digital Industries at Siemens AG.
“This will allow us to offer highly flexible solutions to help our customers across many industries to determine the future condition of their machinery and hence, increase their overall equipment effectiveness.”
Commenting on the acquisition, Senseye CEO Simon Kampa added: “Together we can multiply the full potential of Senseye’s innovative predictive technology and deep expertise. Siemens’ global presence and extensive industrial knowledge will ensure that our current and future customers benefit from innovative, seamlessly integrated Industry 4.0 solutions to drive measurable business outcomes.”
Since its inception in 2014, Senseye has focused on asset intelligence Software-as-a-Service solutions. It uses purpose-built machine learning and artificial intelligence to provide a scalable solution that enables predictive maintenance, helping to reduce unplanned downtime and improve sustainability.
This integrates seamlessly with existing and new infrastructure investments, using machine, maintenance, and maintenance operator behaviour data to understand the future health of machinery and what requires human attention.
The solution is designed for maintenance operators and requires no previous background in data science or traditional condition monitoring.
