Unplanned machine downtime is a huge headache for the business sector. In 2016, the Aberdeen Group found that these events cost companies an average of $260,000 per hour – a 60% increase over its 2014 survey. While Aberdeen hasn’t published any further updates, it seems likely that ongoing problems – including pain points like aging infrastructure, skilled worker shortage, and recent societal disruptions – are continuing to drive up operational costs by reducing equipment availability.

In manufacturing, plant operators are tackling unplanned machine downtime by migrating from preventive maintenance to predictive maintenance. Unlike preventive maintenance, which is performed according to a schedule that reflects historical events, predictive maintenance monitors asset condition in real time and prompts interventions before failures disrupt production. Advances in sensors, analytics, and communication technologies are making predictive maintenance increasingly practical and affordable for small, medium, and large manufacturing companies.

This white paper discusses the ways in which a solid predictive maintenance strategy backed by state-of-theart technologies can help manufacturers stay ahead of equipment problems and minimize downtime. Four use cases are provided to help guide decision makers and define work efforts and budgets.