『Trend Detection Podcast』のカバーアート

Trend Detection Podcast

Trend Detection Podcast

著者： Siemens
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今ならプレミアムプランが3カ月 月額99円

2026年5月12日まで。4か月目以降は月額1,500円で自動更新します。

概要

 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.Siemens AG
エピソード
  • When Predictive Maintenance Is (and Isn’t) the Right Tool for Your Plant - with Natalie Kurgan

    When Predictive Maintenance Is (and Isn’t) the Right Tool for Your Plant - with Natalie Kurgan
    2026/04/15
    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
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    26 分
  • From AI Lab to Shopfloor: What It Really Takes to Deploy Industrial AI - with Christian Zillner

    From AI Lab to Shopfloor: What It Really Takes to Deploy Industrial AI - with Christian Zillner
    2026/04/08
    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/
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    25 分
  • The Evolution of Industrial Data: From Sensors to Strategy - with Vlad Romanov

    The Evolution of Industrial Data: From Sensors to Strategy - with Vlad Romanov
    2026/04/01
    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/
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    42 分
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