Trend Detection Podcast

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 of the Trend Detection podcast, we’re joined by Emily Trott from BlueScope Steel to unpack a real-world predictive maintenance success case, where a single vibration sensor helped prevent a critical failure before it happened.It’s a practical, end-to-end story of how AI, engineering expertise, and process come together to move from early signal to real intervention — and how that translates into avoided downtime and operational impact.What you’ll learn in this episode:How a small vibration signal led to the discovery of a hidden failure on a connected assetWhy predictive maintenance is not one alert → one fix, but a multi-step investigation involving both AI and human expertiseThe gap between traditional monitoring and predictive maintenance and why most failures are only detected when it’s already too lateHow combining Senseye insights with on-site expertise changes the outcome from reactive to controlled interventionWhy sharing success cases internally is key to driving adoption, scaling, and new use cases across sitesYou 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-maintenanceTo find out more about Bluescope Steel's approach to asset intelligence, please watch the video below:https://www.youtube.com/watch?v=0dnDST5B1V4

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, we're joined by three Senseye deliver experts to share real-world lessons from Senseye deployments.They will discuss what actually works, what doesn’t, and what separates successful projects from the rest.What you’ll learn in this episode:Why choosing the right assets early is critical to proving value and building momentumHow data quality and context (not volume) determine success in predictive maintenanceWhy early wins are essential to drive trust, adoption, and scaling across teamsThe common pitfalls in implementations, from wrong failure assumptions to poor asset selectionHow successful deployments depend on combining AI with real-world expertise and customer ownershipYou 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-maintenance

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, we’re joined by experts from Siemens live from Hannover Messe 26 to explore how Arca Continental, one of the world’s largest Coca-Cola bottlers, is driving a data-led manufacturing transformation across its global operations, and how combining transparency, predictive maintenance, and a strong digital foundation is unlocking measurable operational impact at scale.What you’ll learn in this episode:How Arca Continental is scaling digital transformation across 45 sites by starting with transparency (OEE, cost, loss drivers) before moving to advanced use casesWhy predictive maintenance success depends on strong cultural alignment and early digital maturity — not just technologyA real-world example delivering impact in days — saving 13 hours of downtime on a critical production assetHow Insights Hub provides a unified data foundation to connect use cases, systems, and workflows at scaleWhat’s next: expanding with AI, contextualized data, and prescriptive insights to drive continuous operational improvementYou 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-maintenance

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, we’re joined by Louis Mahlau, Product & Portfolio Manager - IoT & Analytics at Siemens, who explores how digital drivetrains are transforming the way industrial assets are monitored and maintained and how combining IoT, AI, and domain expertise is unlocking a new generation of predictive maintenance.What a digital drivetrain is and why it underpins so much of modern industrial operationsHow predictive maintenance shifts organizations from reactive and preventive approaches to truly predictive insights How sensors, connectivity, cloud computing, and digital twins come together to turn raw machine data into actionable intelligence A real-world example of how connecting a single motor enabled early detection of issues before production downtime occurred Where the true value lies beyond technology — including data quality, scalability, and change management Why many pilots fail to scale, and what successful organizations do differently from the start How industrial AI and copilots are making complex machine data easier to understand and act on What the future looks like — from prescriptive maintenance to autonomous, self-optimizing systemsYou 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-maintenance

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, we’re joined by Richard Ella, who takes a step back in time to show how some of the most powerful ideas in predictive maintenance aren’t new at all and why understanding their origins is key to explaining, adopting, and trusting AI today.What you’ll learn in this episode:Why modern AI‑driven predictive maintenance follows the same principles as earlier mechanical and electrical innovationsHow the strobe light was originally invented for maintenance and what it teaches us about “seeing” machines differentlyA simple, practical way to explain AI and Senseye without buzzwords or hypeHow AI mirrors the instincts of experienced plant operators by detecting subtle changes before failureWhy curiosity, trust, and change management matter more than the technology itselfHow early warnings become real value only when teams act on themYou 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-maintenance