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Predictive Maintenance - Real-world phases + what actually happens after kickoff) - with Tom Jacques
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 Tom Jacques, a Solutions Engineer for Senseye at Siemens, to break down what predictive maintenance looks like in the real world, from kickoff to daily use and scale.
What we cover:
What actually happens during the first 30–60 days of a predictive maintenance project
How proper scoping, asset selection, and data availability set projects up for success
Where projects commonly slow down or stall, including resource constraints and misaligned expectations
How pilots transition into day‑to‑day operational use
What creates real “aha moments” for maintenance teams
Why trust is the key factor in getting teams to act on insights
How Senseye Copilot supports decision‑making without replacing human judgement
What separates pilots that scale successfully from those that remain stuck in PoVs
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 Tom on LinkedIn here:
https://www.linkedin.com/in/thomas-jacques-22655585/
View all episodes
By SiemensWelcome 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 Tom Jacques, a Solutions Engineer for Senseye at Siemens, to break down what predictive maintenance looks like in the real world, from kickoff to daily use and scale.
What we cover:
What actually happens during the first 30–60 days of a predictive maintenance project
How proper scoping, asset selection, and data availability set projects up for success
Where projects commonly slow down or stall, including resource constraints and misaligned expectations
How pilots transition into day‑to‑day operational use
What creates real “aha moments” for maintenance teams
Why trust is the key factor in getting teams to act on insights
How Senseye Copilot supports decision‑making without replacing human judgement
What separates pilots that scale successfully from those that remain stuck in PoVs
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 Tom on LinkedIn here:
https://www.linkedin.com/in/thomas-jacques-22655585/