When Predictive Maintenance Is (and Isn’t) the Right Tool for Your Plant - with Natalie Kurgan

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 Natalie Kurgan, Head of Delivery for Senseye across
the Americas at Siemens, who shares a delivery‑side view of when
predictive maintenance is (and isn’t) the right fit—and what plants
need in place before they start:Why predictive maintenance is a
strategy, not a tool, and why success depends on people + process,
not software alone.The readiness checklist that’s often missing:
leadership support, a clear workflow, and a technically minded
champion who drives action.Where projects go wrong in practice—from
weak ownership to poor asset selection and low/limited data
quality.What PdM can realistically deliver (planning spares,
reducing unnecessary planned work, avoiding risky unplanned
failures) vs. what it can’t.How AI copilots help and their limits:
they need context and feedback; they don’t replace human
judgement.If you’re not ready yet, how to get there: define KPIs,
audit maintenance logs, identify problem assets, then assess what
data/sensing you actually have.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