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.
When Predictive Maintenance Isn't the Answer - with Jordan Walters
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.Predictive maintenance can transform how manufacturers approach equipment reliability, but that doesn’t mean it’s the right solution for every asset, failure mode or situation.In this episode of the Trend Detection podcast, we're joined by Jordan Walters, Delivery Lead at Siemens, to explore when predictive maintenance makes sense, and when a different maintenance approach might be the better choice.In this episode, they discuss:Why the most critical assets aren’t always the best place to start with predictive maintenanceThe importance of having the right data, not simply more dataHow asset selection, failure frequency and available lead time can determine whether predictive maintenance will deliver valueWhy context is essential for AI to interpret machine behaviour effectivelyThe difference between not being ready for predictive maintenance and predictive maintenance not being right for a particular use caseWhy predictive maintenance should complement, rather than automatically replace, other maintenance strategiesThe role of maintenance teams and human expertise in validating insights and turning predictions into actionThe three foundations for success: the right data, the right machines and the right peopleYou 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
22 Oct 2026
Predictive Maintenance Is Not the Strategy - A panel discussion
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.Moving from reactive maintenance to a more predictive approach sounds straightforward. In reality, technology is only one part of the equation.In this episode of Trend Detection, Niall Sullivan is joined by maintenance and asset management experts Kevin Clark, Corey Dickens, Rachel Green and Marc Trucio to explore what it really takes to build a more proactive maintenance strategy.They discuss:Why predictive maintenance should be part of a broader asset strategy, rather than the strategy itselftHow manufacturers can move beyond reactive maintenance and escape “pilot purgatory”Why engaging maintenance teams early is critical to adoption and long-term successThe value of predictive approaches beyond reducing downtime, from workforce confidence and knowledge capture to better planning and decision-makingWhy having more asset data doesn't automatically translate into better insightsHow manufacturers can make use of data from older and less connected equipmentWhere industrial AI can support maintenance teams, including identifying anomalies, capturing expertise and making knowledge more accessibleWhy people, culture and change management can ultimately determine whether new maintenance technologies succeed or stallWhether you're still largely reactive or already expanding predictive maintenance across your operations, this episode explores how to take the next step towards a more proactive, data-driven approach to 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
15 Oct 2026
Rethinking Predictive Maintenance: How Do We Roll It Out Across All Our Sites? - with Peter O'Hara
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 is the fourth and final episode in Rethinking Predictive Maintenance, a Trend Detection podcast series exploring the people, data and decisions behind successful predictive maintenance.Host Niall Sullivan is joined by Peter O’Hara to explore what it takes to expand predictive maintenance from one site to a wider, multi-site programme.Peter explains why scaling is not simply about copying the same deployment across every factory. Different sites may have different reliability challenges, infrastructure, assets and maintenance priorities. The conversation explores how manufacturers can share lessons and standardise where appropriate, while still adapting the approach to deliver value at each location.In this episode, you’ll learn:Why predictive maintenance often demonstrates more value at scaleWhy a successful deployment cannot always be copied directly to another siteHow local engagement and global knowledge-sharing support adoptionWhy each site needs clearly defined problems and measures of successHow documentation helps preserve knowledge as programmes expandWhat successful multi-site adoption can look like over timeListen to discover how manufacturers can build momentum, share successful practices and scale predictive maintenance without ignoring the needs of individual sites.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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James Loach
Edgar
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Marco Saviano
Keith Ritchie
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