Predictive Maintenance Analytics: Improve Efficiency and Reduce Unplanned Downtime

A notebook showing the advantages of predictive maintenance analytics, including asset availability, improved workforce, lower risk, decreased service loss, IoT support, decreased planned maintenance, revenue recovery, and smart replacement.

Predictive maintenance analytics is a field of data analytics that assists organizations with their asset maintenance. Through predictive maintenance, organizations can determine when their assets are most likely to fail, optimizing their maintenance tasks and improving their operational efficiency.

Predictive maintenance analytics is used throughout industries such as utilities and power, food and beverage, oil and gas, and industrial manufacturing.

A screenshot of the predictive maintenance analytics dashboard in LLumin’s CMMS+

What Is Predictive Maintenance Analytics?

By analyzing data collected from machine-level sensors, predictive maintenance analytics identifies patterns that indicate when a machine is likely to experience a failure. This information can then be used to schedule repairs or replacements before the failure occurs, preventing unplanned downtime and minimizing the associated costs.

Below we will discuss how using predictive maintenance analytics will help:

Improve Efficiency

Prevents production halts and safety risks in factories and power plants by increasing system reliability.

Minimize Unplanned Downtime

Reduces operational disruptions and associated costs by scheduling maintenance conveniently.

Reduced Costs of Asset

Enables better planning for asset failure and maintenance.


Reducing emergency costs and improving resource allocation.

Improve Efficiency

In a factory, the unexpected downtime of a single asset can cause production to grind to a halt, resulting in lost revenue and frustrated employees. In a power plant, an unplanned outage can lead to rolling blackouts—and even endanger public safety.

Predictive maintenance analytics improve efficiency by increasing reliability. And the more efficient your system, the less it costs to operate and the less strain it puts on your employees.

Minimize Unplanned Downtime

What happens when your systems go down? When a system goes down, it causes cascading disruption. Employees and operations can’t do their job. Customers don’t get the parts or services they need. And maintenance technicians are pulled from their regularly scheduled tasks to put out fires.

In construction, a critical machine going down could mean that an entire day of labor is lost, setting back the entirety of the project. In healthcare, the effects could be even more devastating. Unplanned downtime could mean patients need to be moved from facility to facility.

Unplanned downtime has the potential to cost millions of dollars per hour. And while some downtime is unavoidable, predictive maintenance can help reduce downtime or (at least) schedule it when most convenient.

Reduce Cost of Asset Ownership

A pump goes down in the oil field. Parts have to be sourced and expedited last minute—at a premium cost. Meanwhile, maintenance technicians and/or outside contractors work overtime to bring the asset back online. It is difficult to make an accurate plan for the future when you can’t rely on consistent resources due to an inability to predict system failure rates.

Predictive maintenance empowers your organization to plan ahead for asset failure and when asset maintenance is truly needed. It allows you to know precisely which parts you need and when you need them, so you can accurately source them at the lowest possible costs. Conducting maintenance on your schedule will improve your mean time to repair, lower your cost of maintenance, and increase ROA.

 a screenshot from LLumin’s CMMS+ of real-time predictive maintenance status.

Building a Predictive Maintenance Analytics Based System

Predictive maintenance analytics gives your organization the data it needs to anticipate what potential problems may occur and when. But how does it work? It begins with data collection. A predictive maintenance analytics system is only as good as the data it collects.

Collecting Predictive Maintenance Data

Today, predictive maintenance data is best collected through machine-level sensors or industrial internet of things (IIoT) enabled control or monitoring systems. Machines report their statuses, ongoing performance, and condition levels of all components.

Examples may include usage or utilization levels, critical parameters such as temperature, pressure, or any number of flow levels and rates. Along with machine data, a thorough analytics-driven system also collects historical data regarding maintenance actions, machine environment and application factors, operator input, and historical failure rates. This data is then sent to the platform, where it is analyzed.

Improve the Reliability With Real-Time Readings

As stated above, predictive maintenance analytics is only as reliable as the data collected. And this means not only what data but the quantity and granularity of the data collected. Now, with today’s level of computing power readily available, large volumes of data can be collected and then analyzed with machine learning algorithms.

Machine learning algorithms look at data sets for patterns. In a simplistic example, a food and beverage facility might have a packaging machine that tends to fail after 2,000,000 units. But a machine learning algorithm might notice that the tendency to fail doesn’t occur specifically after 2,000,000 units. Instead, failure occurs after a machine has spent a certain number of working hours operating at the peak level of uninterruption or in conjunction wither other external factors that are found to be relevant to machine operations reliability—the 2,000,000 unit number is only one factor.

Creating a Predictive Maintenance Analytics System

To create a predictive maintenance analytics system, you need two parts:

  • Sensors or controls to collect machine-level data in real-time – This data will be sent to your analytics software for analysis and review.
  • Software to analyze maintenance and operations data –  Historical data will continue to influence the system, and the system will also learn over time.

Together, collected data and software create a predictive maintenance analytics system.

LLumin’s CMMS+ for Intelligent, Sensor and IIoT-Driven Predictive Maintenance

LLumin’s CMMS+ predictive maintenance software collects data directly from machine-level sensors and IIoT-enabled control systems to give you the most accurate insights possible. Data is automatically collected and consolidated, allowing the machine learning system to become smarter and more accurate over time.

Most organizations know that a predictive maintenance enabled CMMS is right for them. But they fear that a transition will be disruptive. To counter this, we provide complete implementation support to guide you through the process. We will create a roadmap for your organization’s digital transformation. You can reap the benefits without having to manage a large transition.

So if you are looking for a cutting-edge CMMS to handle predictive analytics and so much more, LLumin is the perfect fit.

Getting Started With LLumin

LLumin develops innovative CMMS software to manage and track assets for industrial plants, municipalities, utilities, fleets, and facilities. To learn more about predictive maintenance analytics, we encourage you to schedule a free demo or contact the experts at LLumin and see how our CMMS+ software can help you reach your efficiency and cost-cutting goals.

Take a Free Tour