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 the closing episode of our 4-part series, we sat down with Peter O'Hara, a Customer Success Manager for Senseye who's spent close to a decade helping manufacturers actually adopt and scale predictive maintenance. We cover: → Why asset selection makes or breaks the first 90 days after go-live → What separates a real internal champion from someone who just has the time → How to turn resistors into advocates, often by solving the problem they actually have→ The KPIs that drive renewal and expansion (hint: downtime avoided is only part of it)→ Why Senseye at scale is beautifulIf you've missed Parts 1–3 with Richard Jeffers, Nat Ford, and Pontus Noren, now's the time to binge the full series:Listen to episode one: AI Is Ready. Are We? - with Richard Jeffers here.Listen to episode two: Why Change Management Makes or Breaks PdM — with Nat Ford hereListen to episode three: What Industrial AI Projects Get Wrong About Adoption — with Pontus Noren | Adoption Gap Part 3 hereYou 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 the third episode of our 4-part series, I sat down with Pontus Noren, founder of Ensemble AI, to unpack the patterns he's seen across industries —and why the same mistakes keep showing up in different factories.His perspective is especially valuable because it's from outside the Siemens world. The adoption gap isn't a PdM problem. It's an industrial AI problem.We cover: → The most common patterns behind failed industrial AI projects → Why "proof of concept" culture is one of the biggest blockers to scale → How misaligned KPIs create a gap between what AI delivers and what the business measures → What trust actually looks like in practice and how to build it incrementallyIf you missed Parts 1 and 2 you can listen below:Listen to episode one: AI Is Ready. Are We? - with Richard Jeffers here.Listen to episode two: Why Change Management Makes or Breaks PdM — with Nat Ford hereYou 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 the second episode of our 4-part "The Adoption Gap" series, we sat down with Nat Ford from Siemens to unpack why change management isn't a workstream you bolt on at the end of a PdM project. It's the project itself.His take? If you need formal "change management" by the time you go live, something has already gone wrong.We cover: Why change management is often a sign the project wasn't designed properly in the first placeThe difference between managing change and designing change in from day oneHow the Hawthorne Effect applies to PdM adoption — and why paying attention to people matters more than any frameworkWhy predictive maintenance changes how people decide, not just what they doPrevious episodes in the seriesListen to episode one, AI Is Ready. Are We? - with Richard Jeffers here.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-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 Nick Leeder to explore what sustainability really means for mid-sized manufacturers — and how moving beyond strategy into practical, measurable action can unlock both operational efficiency and improved profitability.What you’ll learn in this episode:Why sustainability must be treated as a core business initiative — with clear ownership, budget, and measurable outcomes, not just a strategy documentHow external pressures (regulation, supply chain requirements) and internal drivers (cost, competitiveness) are accelerating adoption for mid-sized manufacturersPractical starting points — using existing data like energy bills and focusing on simple, high-impact improvements such as reducing idle energy consumptionHow to build a business case by directly linking sustainability initiatives to financial outcomes, from cost savings to risk reductionThe biggest pitfalls to avoid — lack of governance, too many initiatives, and failure to embed sustainability into daily operations and decision-makingYou 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 Peter Schopf to unpack the latest industrial and AI trends coming out of Hannover Messe 26 — from the rise of humanoid robotics to the growing divide between operational and strategic AI, and why many organizations are still missing where the real value lies.What you’ll learn in this episode:What stood out at Hannover Messe — including the rise of humanoid (embodied) AI and the shift from hype to more tangible industrial use casesWhy IT/OT integration and industrial data access remain persistent challenges, despite years of investment and innovationPractical generative AI use cases on the shop floor — from documentation and troubleshooting to AI-powered assistants for operatorsWhy most organizations focus too heavily on operational AI — and are missing the much bigger opportunity at the strategic decision-making levelHow “strategic prompting” and better AI interaction can dramatically accelerate complex business decisions, and why context and experience matter more than technical skillYou 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 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

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 Tom Jacques, a Solutions Engineer for Senseye at Siemens, to break down what predictive maintenance looks like in the real world, from kickoff to daily use and scale.What we cover:What actually happens during the first 30–60 days of a predictive maintenance projectHow proper scoping, asset selection, and data availability set projects up for successWhere projects commonly slow down or stall, including resource constraints and misaligned expectationsHow pilots transition into day‑to‑day operational useWhat creates real “aha moments” for maintenance teamsWhy trust is the key factor in getting teams to act on insightsHow Senseye Copilot supports decision‑making without replacing human judgementWhat separates pilots that scale successfully from those that remain stuck in PoVsYou 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-maintenanceConnect with Tom on LinkedIn here:https://www.linkedin.com/in/thomas-jacques-22655585/

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 Natalie Kurgan, Head of Delivery for Senseye across the Americas at Siemens, who shares a delivery‑side view of when predictive maintenance is (and isn’t) the right fit—and what plants need in place before they start:Why predictive maintenance is a strategy, not a tool, and why success depends on people + process, not software alone.The readiness checklist that’s often missing: leadership support, a clear workflow, and a technically minded champion who drives action.Where projects go wrong in practice—from weak ownership to poor asset selection and low/limited data quality.What PdM can realistically deliver (planning spares, reducing unnecessary planned work, avoiding risky unplanned failures) vs. what it can’t.How AI copilots help and their limits: they need context and feedback; they don’t replace human judgement.If you’re not ready yet, how to get there: define KPIs, audit maintenance logs, identify problem assets, then assess what data/sensing you actually have.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-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 of the Trend Detection podcast, we’re joined by Christian Zillner, who leads global AI deployment for Digital Industries Automation at Siemens, to explore what it really takes to scale industrial AI from experiments to real shop‑floor impact.Drawing on hands‑on experience across industries, Christian shares practical lessons on what works, what doesn’t, and why many AI initiatives struggle to move beyond pilots, including:What industrial AI deployment really means—going beyond algorithms to include business cases, ownership, services, and organisational changeWhy many AI pilots fail to scale, from unrealistic expectations to non‑serviceable, custom architecturesThe human side of IT/OT convergence, and how unclear roles and ownership can derail progressHow to choose between cloud, edge, or hybrid AI based on latency, security, cost, and operational constraintsThe role of partners and ecosystems in taking AI from the lab to productionWhere AI delivers real value today—and where expectations still need groundingWhy standardising the deployment platform early is critical to long‑term scalabilityPractical advice for moving from experimentation to production with a small set of repeatable, high‑value use casesA refreshingly realistic discussion on industrial AI for anyone responsible for digitalisation, automation, or AI strategy in manufacturing.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-maintenanceConnect with Christian on LinkedInhttps://www.linkedin.com/in/christian-zillner/

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 Vlad Romanov, an industrial automation and data integration specialist with experience spanning plant‑floor engineering, systems integration, and enterprise strategy, who shares a practical view on how industrial data moves from machines to board‑level decisions:What industrial data really is, starting at sensors and control systems on the plant floor and evolving into decision‑ready information used across SCADA, MES, and enterprise systems.How data flows from machines to strategy, explaining the progression from standalone equipment, to production lines, to site‑wide and multi‑site performance insights.Why digitalisation has accelerated in recent years, particularly post‑COVID, as manufacturers needed remote visibility, faster decision‑making, and more resilient operations.The reality of IT/OT integration, including cultural differences, conflicting priorities, and why alignment and over‑communication matter more than technology alone.Where AI and machine learning add value today—and where they don’t yet, highlighting realistic use cases such as analysis support, infrastructure modernisation, and decision assistance rather than full autonomy.What separates successful data initiatives from failed ones, including mindset, patience, iterative improvement, and the willingness to modernise legacy infrastructure step by step.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-maintenanceConnect with Vlad on LinkedIn:https://www.linkedin.com/in/vladromanov/

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 Steve Lomax, an independent reliability and maintenance consultant with decades of experience in highly regulated pharmaceutical environments, who shares a practitioner’s perspective on predictive maintenance, reliability, and digital transformation.What predictive maintenance really means in regulated industries, focusing less on “magic AI” and more on reducing uncertainty, managing risk, and stabilising critical processes.Why reliability must be framed in business language, connecting maintenance decisions to availability, risk, patient impact, and CFO‑level financial outcomes.How global standards and local realities must coexist, with predictive maintenance deployed through a common framework but adapted site‑by‑site based on maturity, assets, and regulation.Why data quality, simplicity, and cultural readiness matter more than more sensors, starting with existing data and building trust in digital records and AI‑supported insights.How to introduce predictive maintenance without overwhelming teams, by focusing on asset criticality, bad actors, cross‑functional ownership, and a clear reliability roadmap.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-maintenanceConnect with Steve on LinkedIn:https://www.linkedin.com/in/steve-lomax-56730912/Learn more about Rheon Insights:https://www.rheoninsight.co.uk/

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 Jasleen Kaur, a graduate electrical and electronics engineer at Siemens Digital Industries, who shares her firsthand experience delivering industry‑led education through the Connected Curriculum initiative.How industry and academia can work together effectively through initiatives like Connected Curriculum to close the skills gap between university education and real-world engineering roles.The practical difference between condition monitoring and predictive maintenance, and why predictive maintenance adds real value by anticipating failures and reducing unplanned downtime.How AI-powered tools like Senseye (and its Copilot) help beginners and professionals alike interpret machine data, troubleshoot issues, and make informed maintenance decisions using natural language.What a real-world, hands-on predictive maintenance course looks like, including the use of synthetic data, staged learning over several weeks, and practical platform experience rather than theory alone.Why human judgment still matters in an AI-driven workplace, and how students are taught to combine critical thinking with AI insights rather than relying blindly on automated recommendations.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-maintenanceConnect with Jasleen on LinkedIn:https://www.linkedin.com/in/jasleen-kaur-907391268/Previous episodes about Connected Curriculumhttps://podcasts.apple.com/ca/podcast/hands-on-with-ai-bringing-senseye-to-the-classroom/id1589803102?i=1000724963690

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

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, Liz McGinn is joined by Kelli Case, a Business Development Director for Senseye at Siemens, who shares practical guidance drawn from her experience working with organizations adopting predictive maintenance.Why choosing a predictive maintenance partner is a strategic, long‑term decision, not just a software purchase—covering culture change, transformation, and sustained value.How to assess your organization’s readiness for predictive maintenance, including maintenance maturity, data access, internal capabilities, and willingness to change.What separates a strong PDM partner from a weak one, such as listening skills, adaptability, domain experience, global support, and the ability to scale with your business.Key technology and architecture considerations to look for, including openness and vendor agnosticism, data ownership, security, configurability vs. customization, and integration across systems.How to avoid common pitfalls and measure success, from unrealistic promises and long time‑to‑value to proving ROI quickly, enabling user adoption, and planning for future evolution toward prescriptive maintenance.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-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 Lina Huertas, Industry Executive for Manufacturing at Microsoft UK, to explore how generative AI, copilots, and agentic AI are reshaping digital manufacturing — not just speeding up tasks, but changing how work is designed, delivered, and governed.We unpack the difference between copilots (which assist and enhance human work) and AI agents (which can complete tasks end‑to‑end within defined boundaries), and what this shift could mean across the shop floor, engineering, and back office.You’ll learn:How copilots and agentic AI differ — and why that matters for manufacturing workflows and roles.How organisations are thinking about moving from assistance to more end‑to‑end task execution (with human oversight and clear boundaries). Why human–AI collaboration is becoming a core capability, with work shifting toward supervision, decision‑making, leadership, and critical thinking.The key barriers to scaling AI in manufacturing: data silos, fragmented systems, shadow IT, and organisational structure.The skills manufacturers (and individuals) need next: hands‑on AI literacy, “learning how to learn,” and leading in a workforce that increasingly includes AI systems.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-maintenanceConnect with Lina on LinkedIn:https://www.linkedin.com/in/linaahuertas/

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.What you will learn in this episode:Why many industrial AI initiatives fail to move beyond pilots, despite heavy investment and executive attention.How AI hype and “fear of missing out” often lead companies to start with the technology rather than the business or process problem.Why process maturity, data relevance, and context are essential prerequisites before applying AI at scale.How to identify repeatable, scalable AI use cases—with predictive maintenance highlighted as a strong example.What to measure to prove success, including operational impact, financial value, and real improvements to frontline workers’ day‑to‑day work.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-maintenanceConnect with Nick on LinkedIn:https://www.linkedin.com/in/redeel/

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 special episode with David Humphrey, Director of Research, ARC Europe, we discuss:How predictive maintenance has evolved from scheduled inspections to data‑driven decision‑making using connected machine data.What Senseye Predictive Maintenance is, how it works as a cloud‑based analytics application, and where it fits within Siemens’ broader asset and maintenance portfolio.How machine learning and generative AI are used to detect abnormal asset behavior and translate complex analytics into actionable maintenance guidance.How historical machine data, maintenance records, and technical documentation are leveraged to speed diagnosis and reduce dependency on individual expert knowledge.Why scalability, usability, and organizational adoption are critical success factors for predictive maintenance programs operating at hundreds or thousands of assets.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-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.This episode covers:How the SITRANS MS200 wireless multisensor measures vibration, temperature, and magnetic fields to enable condition monitoring on rotating and vibrating assets.Why retrofittable, battery-powered sensors are key to bringing predictive maintenance to assets that previously had no condition data.How the CC220 gateway integrates seamlessly with Senseye Predictive Maintenance, supporting both stand‑alone deployments and existing IT infrastructures via MQTT.What makes the solution highly flexible and scalable, from single gateways to plant‑wide sensor networks without complex IT overhead.Where the MS200 roadmap is heading, including configurable measurement cycles, on‑sensor KPI calculation, improved battery life, and expanded frequency ranges for future applications.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-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.This episode covers:The Essence of Creativity in Engineering: How creative thinking is crucial for innovating solutions to complex problems in predictive maintenance (PM), moving beyond established methods to develop bespoke approaches for each customer.Unconventional Problem-Solving with Existing Tools: Discover how seemingly limited data, like temperature readings from electric car charger pins, can be creatively manipulated using features like "derived measures" to detect degradation, even when traditional sensor deployment isn't feasible.Bridging the Gap: From Industrial Practice to Education: Learn about the "Connected Curriculum" initiative, which brings Senseye to university students, and the creative adaptations needed to teach real-world data challenges (like noisy or incomplete data) and PM principles in an academic setting.Debunking Misconceptions about AI and Data: Understand that perfect data is a myth and that effective AI in PM, like Senseye, thrives on curated, clean data focused on specific condition indicators, rather than a "big data" dump, to provide nuanced and accurate insights.AI as an Enabler for Human Creativity: Explore how AI serves as a powerful tool to support and amplify human ingenuity in engineering, emphasizing the importance of asking questions, providing context, and fostering a collaborative environment to drive innovation and personal growth in the field.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-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.This episode covers:The Evolution of Vibration Analysis: Discover how this crucial predictive maintenance technique has evolved from manual listening with a screwdriver to sophisticated instrumentation, enabling early detection of equipment issues weeks in advance.Best Practices for Implementation: Understand the importance of careful planning, selecting the right sensors for specific assets, and conducting criticality assessments to avoid common pitfalls like "whack-a-mole" problems and ensure a strong return on investment.Why Vibration Analysis is a Foundational PDM Technology: Explore its broad applicability across various industrial equipment and environments, making it one of the most comprehensive methods for identifying a wide range of fault modes compared to more niche predictive maintenance technologies.The Game-Changing Impact of AI and Cloud: Learn how these advanced technologies have revolutionized vibration analysis by enabling rapid data interpretation, providing remote access to expert insights, offering scalable monitoring solutions for any facility size, and ensuring continuous software updates.Achieving Actionable, Contextualized Insights: Find out why cross-departmental cooperation is vital for successful implementation and how AI-driven platforms like Senseye provide tailored recommendations by understanding a facility's unique operational context, maintenance history, and risk tolerance.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-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.This episode covers:The Realistic Path to AI Adoption: Peter clarifies that while autonomous AI agents are exciting, they are still in early research. Companies should prioritize mastering foundational stages like strategic prompting and AI assistance before diving into complex agent workflows.Unlocking the Power of Strategic Prompting: Discover the difference between "operative prompting" (for direct outputs) and the often-underutilized "strategic prompting," where AI helps structure complex problems and create frameworks for future solutions, offering significant benefits.Debunking AI Myths and Misconceptions: Learn to navigate the "fake news" around AI, including exaggerated project failure rates and the notion of AI "getting dumber," by understanding the context behind such claims and focusing on reliable insights.Treating AI Like a "Working Student": Gain insights into effectively interacting with generative AI by treating it as a collaborative "working student," emphasizing the critical role of providing clear context, examples, and iterative feedback for optimal results.Navigating the "Build vs. Buy" Dilemma for AI/IoT: Explore the strategic considerations for companies deciding whether to develop their own AI and IoT solutions or integrate existing market offerings, and how this decision impacts differentiation and resource allocation.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-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.This episode covers:Tom's unique career journey: Discover how his path from hands-on agricultural maintenance and shop floor experience led him to become a solution engineer at Siemens, showcasing a fascinating blend of mechanical and digital expertise.The transformative power of diverse experience: Understand how having a broad perspective, including practical insights into various industries and machine failure modes, is crucial for developing effective, customer-centric solutions.Insights into the evolution of Predictive Maintenance (PM): Explore how AI and intelligent software are moving PM beyond basic condition monitoring, making it more accessible, accurate, and capable of anticipating failures across all types of assets.How Siemens' Senseye tackles real-world industrial challenges: Learn about its agnostic solution that quickly baselines machines, digitalizes expert knowledge, and provides tangible insights to optimize operations, reduce downtime, and enhance safety.A glimpse into the future of industrial maintenance and valuable career advice: Hear about exciting upcoming trends like simpler wireless sensors and advanced AI integration, along with practical tips on becoming an "expert generalist" and fostering continuous learning in a rapidly evolving field.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-maintenance

Levin Burkhart wird Ihnen mehr über Senseye Predictive Maintenance erzählen und wie Sie damit Maschinenausfälle erkennen können, bevor sie auftreten. Erfahren Sie, wie die KI-gestützte Lösung vorhandene Datenquellen nutzt, um Anomalien frühzeitig zu erkennen und Ausfälle vorherzusagen. So können Sie ungeplante Ausfallzeiten reduzieren, auf Echtzeitdaten zu Ihren Anlagen zugreifen, Kosten senken und Ihre Produktion zukunftssicher machen.Machen Sie mit uns den Schritt in Richtung intelligente Produktion und erfahren Sie, wie Sie Ihre Daten nutzen können, um Ihre Effizienz zu steigern.

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 you will learn about:The Hidden Cost of Poor Spare Parts Management: Discover how inadequately managed spare parts lead to significant capital tie-up, increased downtime, and often overlooked financial burdens in industrial settings.Why Spare Parts Are an Underserved Area: Understand the historical reasons behind the neglect of spare parts, from complex naming conventions and lack of transparency to accounting treatments that obscure their true impact.The Transformative Power of AI and LLMs: Learn how emerging technologies like Large Language Models (LLMs) can revolutionize spare parts management by accelerating RCM studies, standardizing inventory data, and enabling more strategic stocking decisions.Integrating Spare Parts into Your Digital Strategy: Explore how effective spare parts management is a crucial, yet often underestimated, component of a comprehensive digital transformation and predictive maintenance strategy.Gaining Executive Buy-in for Optimization: Get insights into how to justify investment in spare parts management by demonstrating clear return on investment through reduced working capital, minimized downtime, and enhanced operational efficiency.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-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.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-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.This episode covers: Cut Through the Noise: Discover the true, underlying challenges customers face in maintaining their assets, moving beyond buzzwords to focus on what genuinely impacts production and downtime.Experience Rapid Intelligence: Learn how Senseye's software can "learn" an asset's normal operation in just 2-5 days, providing insights that might seem too good to be true, and how customer feedback directly shapes its evolution.Debunk Predictive Maintenance Myths: Understand that predictive maintenance isn't about eliminating all failures or replacing human expertise, but about optimizing maintenance timing and acting as a powerful decision-support tool.Witness Industry 4.0 in Action: Hear how digital transformation is delivering real, measurable success for companies across various sectors, making advanced asset monitoring more accessible and impactful than ever before.Strategize for Maximum Impact: Gain insight into how identifying critical pain points and understanding the ripple effects of asset failure are key to selecting the right machines for monitoring, ensuring significant return on investment.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-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, you will learn about:Defining the Role: You'll discover that a predictive maintenance champion is a key individual responsible for integrating new technologies like Senseye, ensuring its full utilization, keeping colleagues engaged, and effectively communicating its value across all levels of the organization.Why a Champion is Essential: Learn why having a dedicated champion is critical for the success of predictive maintenance initiatives, preventing them from being sidelined by other priorities and ensuring the technology's potential is fully realized.Overcoming Challenges: Understand the common hurdles champions face, such as resistance from experienced staff to new technologies and the administrative tasks involved. You'll hear how demonstrating tangible results is key to building trust and gaining acceptance.Measuring Impact: Explore how the success of a predictive maintenance program and the champion's contribution are measured, primarily through avoided downtime and documented cost savings, which ultimately makes maintenance teams' jobs easier.Becoming and Supporting a Champion: Get advice on how to become a champion by demonstrating technical aptitude and proactivity, and learn what leadership needs to do to empower and support these champions to ensure the long-term success of predictive maintenance efforts.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-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.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-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.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-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, you will:Discover real-world examples of how a modest investment in industrial IoT sensors can prevent hundreds of thousands of euros in unforeseen repair costs and scale asset monitoring exponentially.Learn the practical strategies for deploying predictive maintenance across diverse factory environments, including navigating outdated corporate guidelines and integrating with legacy systems.Understand the seamless journey of data from robust wireless sensors to intelligent, AI-driven insights that predict equipment failures and suggest precise next steps.Find out how advanced predictive maintenance technology is becoming accessible to businesses of all sizes, driving value and efficiency beyond just cost savings.Get an inside look at the unique synergy between Siemens, EL-Watch's cutting-edge sensor technology, and Senseye's AI platform, and how this collaboration is shaping the future of industrial reliability.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-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.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-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.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-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.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-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.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-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.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-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.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-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 of Trend Detection, we explore why data is the single most important factor in the success of any predictive maintenance (PdM) strategy.  You'll learn which types of data matter most, how to overcome common challenges like silos and poor quality, and why you don’t need perfect data to get started. Whether you're just beginning your PdM journey or looking to scale, this episode will help you build a strong, data-driven foundation for long-term reliability and ROI. 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-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.This episode discusses: Evolving Conversations: Discover why "IoT Time" is expanding its focus to embrace the cutting edge of AI and Generative AI, and how the arrival of GPT-5 is reshaping the landscape of technological discussion.Beyond the Hype: Uncover the critical difference between true AI agents capable of complex problem-solving and the marketing buzz that often mislabels technology.The Power of GPT-5: Learn how the widespread accessibility of GPT-5 is poised to revolutionize strategic planning and problem-solving, making advanced reasoning available to a much broader audience.Smart AI Adoption: Get practical insights into a successful, phased approach for integrating AI into your organization, and find out why jumping straight to complex "agentic" projects might be a costly mistake.The Cost of Inaction: Hear a powerful call to action on why embracing AI now is non-negotiable, and how to navigate common internal hurdles like data security fears and managerial skepticism.Connect with Peter: https://www.linkedin.com/in/peter-schopf/

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.We are joined by Dr Aris Alexoulis from Manchester Metropolitan University and Jordan Walters and Steve Jones from Siemens to discuss:How Siemens’ Connected Curriculum equips students with real-world Industry 4.0 skills by integrating Senseye Predictive Maintenance into university teaching.The structure of the three-week pilot at MMU: from ingesting data to configuring anomalies, trends, and thresholds, giving students hands-on PdM experience.The role of Senseye Copilot in supporting learning—using natural language to explain alerts, provide context, and extend teaching beyond the classroom.The impact on students’ employability and career pathways, with predictive maintenance skills shaping projects and future opportunities.How academic feedback drives product improvements—such as simplifying derived measures—and helps refine training for both students and industry users.Next steps for the program: embedding the module at MMU, scaling to other universities, and building a distributed network of Festo labs for collaborative PdM monitoring.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-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 of Trend Detection, we sit down with Vahid Vahid Baharvand to explore how visualization tools are helping small and medium-sized enterprises (SMEs) turn complex data into clear, actionable insights. From real-time dashboards to predictive service assistance, we discuss how visualization streamlines operations, supports smarter decision-making, and drives measurable impact.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-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.• Why predictive maintenance is crucial in heavy industry environments, and how it differs from traditional maintenance approaches - from preventing catastrophic failures to optimizing machine performance• The real value of existing data in heavy industrial settings, and why organizations often underestimate what they already have while overestimating what they need• How predictive maintenance directly impacts workplace safety in heavy industry, with real examples of how monitoring and early detection can prevent dangerous situations• The rapid return on investment (ROI) potential in heavy industry applications, including examples of companies achieving ROI within weeks and saving millions through single preventive actions• The role of AI and copilot technology in democratizing maintenance knowledge, breaking down language barriers, and preserving crucial expert knowledge as experienced workers retire. 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-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.This covers:Why long-standing manufacturers like Kotányi are embracing digital transformation—and what it takes to turn tradition into innovation with predictive maintenance.How starting with a single bottleneck machine helped Kotányi reduce downtime, prove value fast, and lay the foundation for future AI-driven improvements.Why success isn’t just about installing new tech—but about aligning with business goals, involving shopfloor teams, and making insights actionable.The critical importance of choosing the right partner—offering end-to-end solutions, domain expertise, and a collaborative approach that fits existing operations.Lessons learned from the frontlines: Avoiding change management pitfalls, breaking down silos, and scaling predictive maintenance with strategy, simplicity, and speed.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-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, you will learn:• Why combining both on-premise and cloud solutions creates more value than choosing just one approach• How on-premise systems excel at mission-critical control and high-speed analytics while cloud enables scalability and long-term analysis• The key security considerations when transitioning from operational technology (OT) to information technology (IT)• Real-world examples of hybrid approaches in predictive maintenance, including vibration monitoring and process data analysis• Why future-proofing your operations requires flexibility between on-premise and cloud solutions rather than a rigid single approachYou 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.Why it’s time to move beyond the AI buzzwords—and how to focus on solving real industrial problems with technology that delivers value.How starting small with predictive maintenance or AI projects can build trust, prove ROI, and create momentum for wider adoption.Why simplicity and usability matter more than complex data science—and how making insights accessible drives real operational impact.The essential role of high-quality, contextualized data in the success (or failure) of any AI-driven initiative.How to avoid common pitfalls by linking AI projects to business KPIs, building scalable plans, and being honest about what’s working and what’s not.Real-world advice for industrial leaders on scaling AI and predictive maintenance across global operations—without getting lost in the innovation theatre.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-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: Learn how New Zealand Steel, as the country's only steel maker, implemented predictive maintenance technology to enhance their unique iron sand-to-steel manufacturing processDiscover how a strategic pilot program and robust data foundation helped build trust and drive successful adoption of predictive maintenance across the organizationUnderstand how early detection of equipment issues, like loose gearbox mounting bolts, helped avoid 12 hours of critical downtime and potential catastrophic failuresExplore how automation and standardization of configurations helped scale the implementation from 300 to a target of 3,000-5,000 assets while maintaining qualityLearn about the multiple benefits beyond just downtime prevention, including quality improvements, yield optimization, and how proper data governance enables future digital transformation initiatives.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-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.Why Predictive Maintenance is the Gateway to Digital Transformation Discover how starting with predictive maintenance (PdM) helps organizations shift from reactive to proactive operations—building trust across departments and unlocking long-term digital value.How PdM Breaks Down Silos Across Teams and Technologies Learn how implementing PdM brings IT and OT together, fosters collaboration between operations, maintenance, and finance, and uncovers valuable insights from both new and existing data.Real-World Success Stories from Automotive and F&B Industries Hear examples of PdM in action—from saving over £200k at a stamping press to digitizing workflows and creating digital twins in global food and beverage plants.Scaling PdM Without Overhauling Everything Get practical advice on starting small, using existing infrastructure and sensor data, and building scalable, future-proof PdM strategies that don’t require massive upfront investment.Debunking Common Misconceptions About PdM Understand why digital transformation doesn’t need to be fully complete before starting PdM—and how this approach actually accelerates integration, ROI, and long-term growth.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-maintenance