AI-based Predictive Maintenance from the factory floor to the cloud - live from SPS

Welcome to the Trend Detection podcast, brought to you by Senseye
Predictive Maintenance – which gives you visibility and insights
into all your assets, from single machines to full plants to help
you reduce downtime, increase knowledge sharing and accelerate
digital transformation across your organization.In this episode,
the host is joined by Tobias, Head of Maintenance and Improvement
at Siemens, alongside Pablo and Anya, to share a real‑world
predictive maintenance journey from Siemens’ highly automated Cham
factory in Bavaria.They explore how unplanned downtime drives lost
output, rising costs, and customer impact—and why predictive
maintenance starts with shop‑floor visibility, not just
software.The conversation walks through how Siemens combined smart
hardware, OT modernisation, and AI‑driven analytics to predict
failures before they happen, even in a brownfield environment with
live production.Using Senseye Predictive Maintenance, maintenance
teams gain clear insights, explanations, and recommended
actions—helping them focus on critical assets and avoid
firefighting.With early results already preventing multiple
breakdowns, the episode also looks at how Siemens plans to scale
the approach across factories and embed predictive maintenance
earlier in the machine lifecycle.A practical, experience‑led look
at how predictive maintenance delivers value on the factory
floor.You can find out more about how Senseye Predictive
Maintenance can reduce unplanned downtime and contribute towards
improved sustainability within your manufacturing plants, by
visiting: www.siemens.com/senseye-predictive-maintenanceRead the
reference in full below:Siemens Cham, Germany - Reduced unplanned
downtime with Senseye Predictive Maintenance