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.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

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 third episode in Rethinking Predictive Maintenance, a Trend Detection podcast series exploring the people, data and decisions behind successful predictive maintenance.We're joined by Ryan Falcini to examine why some predictive maintenance evaluations demonstrate meaningful value while others struggle to progress beyond the pilot stage.Ryan explains why a successful evaluation must begin with a clearly defined business problem, the right assets and realistic measures of success. The conversation explores the limitations of free proof-of-concept projects, the importance of good operational context and why maintenance teams need to be involved from the beginning.In this episode, you’ll learn:Why predictive maintenance evaluations should start with a business objective, not the technologyHow choosing the wrong assets can create a misleading impression of valueWhy machine data needs operational context to produce useful insightsHow maintenance-team involvement builds trust and supports adoptionWhy predicting failures should not be the only measure of successWhat needs to be in place before an evaluation can progress to wider deploymentListen to discover how manufacturers can move beyond simply testing predictive maintenance and build the foundations for a scalable programme.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 is the second 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 Tom Jacques to explore how an International Tech Talents project turned existing PLC status lights into a new source of machine data. Using a camera, the team monitored the lights controlling automated railway points and combined the information with Senseye Predictive Maintenance to identify changing behaviour, without adding sensors or altering the existing control logic.Tom discusses what the demonstrator could mean for manufacturers whose machines may already display useful information that is not currently being captured as data. He also explains where non-intrusive monitoring could be valuable, what further testing would be required for operational use and how collaboration across Siemens helped transform the original idea.In this episode, you’ll learn:How a camera turned existing PLC status lights into usable dataHow the demonstrator identified changes in machine behaviourWhy non-intrusive monitoring could help when systems are difficult to modifyWhy manufacturers should consider the information their machines already displayHow cross-business collaboration changed the direction of the projectListen to discover why some of the machine signals manufacturers need may already be hiding in plain sight.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 launches Rethinking Predictive Maintenance, a new Trend Detection podcast series exploring how manufacturers can retain expertise, capture new forms of machine information, evaluate predictive maintenance and expand successful approaches across multiple sites.In this first episode, host Niall Sullivan is joined by Rachel Green to explore what happens when experienced manufacturing and maintenance professionals leave the workforce, taking decades of knowledge with them.Rachel explains why technical information alone cannot replace the experience, judgement and credibility built over a long career.She discusses how manufacturers can reduce their dependence on a small number of experts by capturing maintenance history, adding context to machine data and using predictive maintenance and AI to help people access the right knowledge when they need it.In this episode, you’ll learn:Why critical maintenance knowledge is so difficult to replaceHow reliance on a small number of experts creates operational riskWhy standard procedures cannot capture every real-world maintenance scenarioHow predictive maintenance and AI can make experience more accessibleWhy technology should support experts and develop the next generation, rather than replace 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

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.From the Trend Detection archive - a factory-floor perspective on AI-based predictive maintenance.Tobias, Head of Maintenance and Improvement at Siemens, shares the predictive maintenance journey of Siemens’ highly automated factory in Bavaria.The discussion explores how smart hardware, OT modernisation and AI-driven analytics were brought together in a live brownfield production environment. It also examines how Senseye Predictive Maintenance helped maintenance teams focus on critical assets, act on emerging equipment issues and reduce reactive firefighting.This episode was recorded previously, so references to the project and product reflect the position at the time of recording.