Rethinking Predictive Maintenance: Evaluating Predictive Maintenance Properly - with Ryan Falcini

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