Trend Detection Podcast

Siemens

Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – the platform, powered by Siemens, which enables predictive maintenance at scale across all of your assets, across all of your plants.Listen to gain insights from our bi-weekly live events and interviews with industry experts about all things predictive maintenance, IoT and digital transformation.Please subscribe via your selected podcast provider to be notified about future episodes.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-maintenanceDISCLAIMER: Unnecessary maintenance," "wasteful activities," or "over-maintenance" only exist when they are unrelated to safety and safety of personnel. Always verify if the maintenance intervals are safety-related; if so, please contact your manufacturer or consult your operating manual.

  1. From AI Lab to Shopfloor: What It Really Takes to Deploy Industrial AI - with Christian Zillner

    1 DAY AGO

    From AI Lab to Shopfloor: What It Really Takes to Deploy Industrial AI - with 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 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/

    25 min
  2. The Evolution of Industrial Data: From Sensors to Strategy - with Vlad Romanov

    1 APR

    The Evolution of Industrial Data: From Sensors to Strategy - with Vlad Romanov

    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/

    42 min
  3. Reliability in Regulated Plants: 5 Rules That Actually Scale - with Steve Lomax

    24 MAR

    Reliability in Regulated Plants: 5 Rules That Actually Scale - with Steve Lomax

    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/

    35 min
  4. From Student to Senseye Educator: Predictive Maintenance in the Classroom with Jasleen Kaur

    18 MAR

    From Student to Senseye Educator: Predictive Maintenance in the Classroom with Jasleen Kaur

    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

    29 min
  5. AI-based Predictive Maintenance from the factory floor to the cloud - live from SPS

    11 MAR

    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

    23 min
  6. Finding the Right Predictive Maintenance Partner - with Kelli Case

    3 MAR

    Finding the Right Predictive Maintenance Partner - with Kelli Case

    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

    52 min
  7. From Copilots to Agentic AI in Manufacturing — with Lina Huertas

    25 FEB

    From Copilots to Agentic AI in Manufacturing — with Lina Huertas

    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/

    35 min
  8. Beyond the AI Hype: What Actually Works in Manufacturing - with Nick Leeder

    18 FEB

    Beyond the AI Hype: What Actually Works in Manufacturing - with Nick Leeder

    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/

    38 min
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About

Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – the platform, powered by Siemens, which enables predictive maintenance at scale across all of your assets, across all of your plants.Listen to gain insights from our bi-weekly live events and interviews with industry experts about all things predictive maintenance, IoT and digital transformation.Please subscribe via your selected podcast provider to be notified about future episodes.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-maintenanceDISCLAIMER: Unnecessary maintenance," "wasteful activities," or "over-maintenance" only exist when they are unrelated to safety and safety of personnel. Always verify if the maintenance intervals are safety-related; if so, please contact your manufacturer or consult your operating manual.

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