Siemens is expanding its portfolio in the field of innovative predictive maintenance and asset intelligence with the acquisition of Senseye.
The global industrial analytics software company is headquartered in Southampton. Senseye is a provider of outcome-oriented predictive maintenance solutions for manufacturing and industrial companies.
Senseye’s predictive maintenance solution enables a reduction in unplanned machine downtimes by up to 50%, increased maintenance staff productivity by up to 30%.
Its solutions support an improvement in corporate sustainability through increased asset lifetime and waste reduction. Senseye is now a 100% subsidiary of Siemens in the UK. The company is assigned organisationally to Siemens Digital Industries and part of the Customer Services Business Unit.
Brian Holliday, managing director of Siemens Digital Industries UK and Ireland, said: “The acquisition of Senseye seriously bolsters our digital technology and service offer to industry.
“Downtime is business-critical and, in a world where data is increasingly intrinsic to success, leading companies are now benefitting from AI-driven insights to improve plant and system availability.
“Data makes a difference too when it comes to productivity and sustainability. Senseye’s PdM predictive maintenance solution is proven across multiple sectors and now enhances our Digital Enterprise approach such that we are excited about the opportunity to work in concert and add more value to our customers.”
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 purpose-built 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 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.