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/