From planned to predictive: Reshaping preventive maintenance

Preventive maintenance: employee at a bottling plant smiling at a tablet in his hands

In today’s industrial landscape, unplanned downtime continues to be one of the most significant threats to operational efficiency. Unexpected outages can erode more than 10% of a company’s annual revenue, according to a Siemens report.1 At the same time, nearly half of all manufacturers now have dedicated predictive maintenance teams—twice as many as five years ago.

A combination of low-cost industrial internet of things (IIoT) sensors, scalable cloud analytics, and modern operational technology (OT) management systems are making preventive maintenance attainable, helping teams respond to asset conditions before failures occur. Organizations are embracing this shift to increase asset uptime, operational efficiency, and margins.

The evolution of maintenance

To understand the opportunity, it’s helpful to consider three common maintenance strategies used in manufacturing today:

  1. Planned maintenance  is the most basic level. Maintenance tasks are scheduled at fixed intervals, regardless of whether equipment actually needs attention. It’s widely used but inefficient.
  2. Proactive maintenance  uses simple, condition-based triggers. When a sensor detects that a parameter has exceeded a set threshold, such as excessive vibration or temperature, maintenance is scheduled.
  3. Predictive maintenance  goes a step further. By analyzing patterns across multiple data points, predictive models can forecast when a failure is likely to happen, giving teams a chance to intervene precisely when it matters most.

This is what predictive maintenance looks like when it works: no downtime, no overtime, and a closed feedback loop that helps the system get smarter over time.

While traditional asset management or production management systems are good at planning and record-keeping, they often struggle to keep up with the data demands and responsiveness required for predictive approaches. Even advanced preventive maintenance solutions often fail to deliver real-world results because they lack the capabilities to assign, route, and track the work that needs to be done.

Unifying shop floor data with AI and automated workflows can make a difference.

Powering predictive maintenance

Modern condition monitoring systems have made it possible to continuously observe machines using sensors that track vibration, heat, noise, pressure, and other variables. These sensors provide a steady stream of performance data. But raw data alone doesn’t create value. The key is context—being able to connect that data to the right operational workflows.

This is where solutions such as AWS IoT SiteWise and ServiceNow Operational Technology Management play a critical role.

AWS IoT SiteWise handles the heavy lifting when it comes to ingesting and modeling industrial data from sensors, historians, supervisory control and data acquisition (SCADA) systems, and programmable logic controllers (PLCs). It helps teams organize and analyze data in real time or near-real time.

Once anomalies are detected, ServiceNow Operational Technology Management, built on the ServiceNow AI Platform, acts as the system of action—creating incidents, triggering work orders, and helping to ensure issues are tracked and resolved before they cause downtime.

Planned maintenance got us through the last century. Predictive maintenance will define this one. By combining IIoT data streams with intelligent, automated workflows, manufacturers are turning every asset into a source of insight—and every anomaly into a chance to act before failure strikes.

Operator efficiency in action

Let’s consider a practical use case. An operator on a bottling line notices no visible issues, but the IIoT sensors are telling a different story. An AWS-based monitoring solution detects abnormal vibration from a pump—well above the normal range. That data is run through a predictive model, which estimates a 72% chance the pump will fail within the next 36 hours.

Rather than wait for a failure to happen, the system automatically creates a maintenance event in ServiceNow. That triggers an OT incident, which flows into a planned work order. The task is bundled into a weekend maintenance window, minimizing disruption. Once the repair is done, the operator uses a mobile app to confirm the line is running normally again, and that feedback is sent back into the model to help refine future predictions.

This is what predictive maintenance looks like when it works: no downtime, no overtime, and a closed feedback loop that helps the system get smarter over time.

A foundation for the data-driven factory

Predictive maintenance is often the first step toward building a truly digital factory. Once your machines are instrumented and your data pipelines are working, you can layer on additional capabilities, such as energy optimization, quality control analytics, and even AI-based digital assistants.

Organizations such as Siemens are already integrating conversational AI into their maintenance platforms, allowing operators to interact with the system naturally using voice or chat interfaces.

In this context, predictive maintenance isn’t just about fixing machines. It’s about transforming the way your entire operation runs.

Planned maintenance got us through the last century. Predictive maintenance will define this one. By combining IIoT data streams with intelligent, automated workflows, manufacturers are turning every asset into a source of insight—and every anomaly into a chance to act before failure strikes.

The future belongs to organizations that can anticipate it. With the right tools, that future is already within reach.

Find out how ServiceNow can help you put AI to work in manufacturing.

1 Siemens, The True Cost of Downtime 2024