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 projectHow proper scoping, asset selection,
and data availability set projects up for successWhere projects
commonly slow down or stall, including resource constraints and
misaligned expectationsHow pilots transition into day‑to‑day
operational useWhat creates real “aha moments” for maintenance
teamsWhy trust is the key factor in getting teams to act on
insightsHow Senseye Copilot supports decision‑making without
replacing human judgementWhat separates pilots that scale
successfully from those that remain stuck in PoVsYou 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 Tom on
LinkedIn here:https://www.linkedin.com/in/thomas-jacques-22655585/