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.When predictive maintenance prevents a breakdown, the operational benefit may be clear. Proving its financial value is harder. Because the failure never happened, manufacturers need a credible way to estimate what was avoided without exaggerating the impact.In this episode of the Trend Detection podcast, we were joined by Rafael Iadocicco, Customer Success Manager at Siemens, to explore how manufacturers can turn avoided downtime into a fair, transparent and repeatable estimate of financial value.In this episode, we discussed:Why calculating avoided downtime begins with the operational story, not the financial formulaHow early warnings, inspections and maintenance actions create a credible chain of evidenceWhy maintenance, operations and finance must agree on the affected scope, avoided hours and financial basisWhen a simple cost-per-hour calculation is sufficient and when a detailed breakdown is neededHow to account for lost production, scrap, restart waste, emergency labour and spare partsWhy conservative assumptions build more trust than pursuing the largest possible numberHow to avoid exaggeration, double counting and confusion between gross impact and net valueHow a consistent process can be replicated across assets, plants and industriesYou 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, Niall Sullivan is joined once again by Dr. Peter Schopf, an AI and organizational development expert with a background spanning industry, leadership and information systems.Drawing on his latest projects and research, Peter explores the shift from individual AI towards team-based AI, where shared knowledge, context and intelligent agents could help people collaborate, make decisions and solve problems more effectively.In this episode, we discuss:The shift from individual AI to team AI and what it could mean for organizationsAI agents on the factory floor, from shift handovers and troubleshooting to onboarding and knowledge sharingAI as a leadership assistant, including better meetings, decision-making and conflict resolutionWhy context matters, and how organizations can build and continuously improve shared AI knowledgeThe rapid evolution of AI engineering, from prompt and context engineering to skills, harnesses and multi-agent systemsKeeping humans in the loop, particularly as AI becomes more proactive and autonomousAdoption, security and trust, and why understanding how people interact with AI will be critical to scaling it successfullyPeter also introduces his work on European Verified Agents, a proposed research initiative looking at how team-based AI could be adopted effectively in industrial and organizational environments.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 Peter on LinkedIn here.

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

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 Trusio 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

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