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