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.

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 organizations

• AI agents on the factory floor, from shift handovers and troubleshooting to onboarding and knowledge sharing

• AI as a leadership assistant, including better meetings, decision-making and conflict resolution

• Why context matters, and how organizations can build and continuously improve shared AI knowledge

• The rapid evolution of AI engineering, from prompt and context engineering to skills, harnesses and multi-agent systems

• Keeping humans in the loop, particularly as AI becomes more proactive and autonomous

• Adoption, security and trust, and why understanding how people interact with AI will be critical to scaling it successfully

Peter 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-maintenance

Connect 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 maintenance

• The importance of having the right data, not simply more data

• How asset selection, failure frequency and available lead time can determine whether predictive maintenance will deliver value

• Why context is essential for AI to interpret machine behaviour effectively

• The difference between not being ready for predictive maintenance and predictive maintenance not being right for a particular use case

• Why predictive maintenance should complement, rather than automatically replace, other maintenance strategies

• The role of maintenance teams and human expertise in validating insights and turning predictions into action

• The three foundations for success: the right data, the right machines and the right people

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.

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 itselft

• How manufacturers can move beyond reactive maintenance and escape “pilot purgatory”

• Why engaging maintenance teams early is critical to adoption and long-term success

• The value of predictive approaches beyond reducing downtime, from workforce confidence and knowledge capture to better planning and decision-making

• Why having more asset data doesn't automatically translate into better insights

• How manufacturers can make use of data from older and less connected equipment

• Where industrial AI can support maintenance teams, including identifying anomalies, capturing expertise and making knowledge more accessible

• Why people, culture and change management can ultimately determine whether new maintenance technologies succeed or stall

Whether 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 scale

• Why a successful deployment cannot always be copied directly to another site

• How local engagement and global knowledge-sharing support adoption

• Why each site needs clearly defined problems and measures of success

• How documentation helps preserve knowledge as programmes expand

• What successful multi-site adoption can look like over time

Listen 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 technology

• How choosing the wrong assets can create a misleading impression of value

• Why machine data needs operational context to produce useful insights

• How maintenance-team involvement builds trust and supports adoption

• Why predicting failures should not be the only measure of success

• What needs to be in place before an evaluation can progress to wider deployment

Listen 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 data

• How the demonstrator identified changes in machine behaviour

• Why non-intrusive monitoring could help when systems are difficult to modify

• Why manufacturers should consider the information their machines already display

• How cross-business collaboration changed the direction of the project

Listen 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 replace

• How reliance on a small number of experts creates operational risk

• Why standard procedures cannot capture every real-world maintenance scenario

• How predictive maintenance and AI can make experience more accessible

• Why technology should support experts and develop the next generation, rather than replace people

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.

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.

Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – the platform which enables predictive maintenance at scale across all of your assets, across all of your plants.

This week, Trend Detection revisits a standout episode from the archive.

In this conversation, Chris Wonson shares the story behind the deployment of Senseye Predictive Maintenance at BlueScope Steel and explores how predictive maintenance was being applied across its operations.

The episode covers:

• How Senseye Predictive Maintenance was deployed at BlueScope Steel

• A success case that helped avoid 24 hours of unplanned downtime

• How Senseye Copilot was supporting the way teams worked at the time of recording

• Practical advice for manufacturers implementing predictive maintenance

This archive episode provides a valuable customer perspective on scaling predictive maintenance and turning asset data into practical maintenance action.

Find out more about how Senseye Predictive Maintenance can help manufacturers reduce unplanned downtime and improve maintenance efficiency across their plants by visiting: www.siemens.com/senseye-predictive-maintenance

Please note that this conversation was originally recorded and published previously. Some product details may have evolved since the original recording.

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 Andrew Rehm, Director of Reliability and Planning at Highland Pellets, to explore how predictive maintenance is transforming industrial operations.

Andrew shares Highland Pellets' journey from traditional inspection-based maintenance to a more proactive, data-driven approach powered by AI. We discuss how the company increased plant uptime from 60% to 89%, uncovered critical issues before they became failures, and built trust in predictive maintenance across operations, maintenance, and reliability teams.

The conversation goes beyond technology, covering change management, workforce adoption, maintenance planning, and why predictive maintenance is becoming a core part of Highland Pellets' long-term strategy.

In this episode you will learn:

• Why predictive maintenance looks very different today than it did ten years ago

• How Highland Pellets identified the right assets to monitor first

• The story behind a critical failure that was detected before it shut down production

• Moving from scheduled inspections to condition-based maintenance

• Building trust in AI-driven insights among maintenance teams

• Connecting predictive maintenance with CMMS workflows and planning processes

• Lessons learned from the first deployment and plans to scale further

• Why the future of maintenance is data-driven, proactive, and predictive

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