Senseye is a provider of predictive maintenance solutions for manufacturing and industrial companies. Source: Siemens
Siemens is further expanding its portfolio in the field of innovative predictive maintenance and asset intelligence with the acquisition of Senseye, an industrial analytics software company headquartered in Southampton, in the United Kingdom. Senseye is a provider of outcome-oriented predictive maintenance solutions for manufacturing and industrial companies.
Furthermore, Senseye solutions support an improvement in corporate sustainability through increased asset lifetime and waste reduction. Since June 1, 2022, Senseye is a 100% subsidiary of Siemens holdings plc in the United Kingdom. The company is assigned organizationally 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. 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,” said Margherita Adragna, CEO of customer services for Digital Industries, Siemens AG.
Simon Kampa, CEO of Senseye, 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 scalable and sustainable asset intelligence Software-as-a-Service (“SaaS”) solutions. Senseye uses machine learning and artificial intelligence to provide a globally-scalable solution that enables predictive maintenance, helping to reduce unplanned downtime and improve sustainability. It integrates with existing and new infrastructure investments, using machine, maintenance, and maintenance operator behavior 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.
