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4 Ways to Leverage Predictive Maintenance Data Analytics in Your Venue Maintenance Strategy

| May 3, 2023 | By

Predictive maintenance is a branch of data analytics that aims to make predictions about the future of assets by using historical data and analytics techniques.

It’s an increasingly important strategy for facility management leaders seeking ways to save time and money, maximize efficiency, prolong equipment life, reduce maintenance costs, and improve safety and reliability.

If you’re ready to avoid the headache and massive bill that comes with restoring or repairing your facility equipment, read on to learn why a predictive maintenance strategy is necessary for success.


What is predictive maintenance data analytics?

At its core, predictive maintenance data analytics is about avoiding major repairs or replacements by integrating real-time data to evaluate equipment conditions and assess the need for maintenance. Rather than performing maintenance after a failure occurs—known as reactive maintenance—predictive maintenance is about catching minor issues before they become major problems.

Predictive maintenance is used in many major industries, including the aerospace, automotive, energy, manufacturing, and retail industries. In each of these industries, the same principles apply: data collection, advanced analytics, and the integration of cloud-based analytics with machinery bring opportunities for huge productivity gains.

The exploding market is a significant indicator of the growing need for better predictive maintenance across industries. According to research conducted by The Insight Partners, the predictive maintenance data analytics market is projected to grow at a CAGR of 20.4 percent from 2022 to 2028.


How does predictive maintenance data analytics work?

To better predict the need for equipment repairs, successful predictive maintenance depends on many digital assets that connect to a platform that then collects and organizes data. These assets fall under two umbrella categories:

  • The Internet of Things (IoT): IoT embeds equipment with sensors to allow for a seamless exchange of data between equipment and systems over the internet.
  • Condition monitoring equipment: This equipment monitors the health of an asset and includes sensors, gauges, and meters that measure temperature, vibrations, sounds, and other factors.

By gathering information and sending it to the cloud where it is then synced to evaluate the status of equipment, failures can be anticipated and avoided.


What are the benefits of predictive maintenance tools?

With real-time data syncing between your condition monitoring equipment and maintenance management system, you’re able to prevent equipment from breaking down. Doing so leads to many other benefits, including:

Money Saved

With a successful predictive maintenance plan in place, downtime is decreased and the number of major repairs is reduced. Your team spends less time and effort making major repairs, uses far fewer spare parts and supplies, and incurs fewer breakdowns and equipment failures. That ultimately puts more money in your pocket.

Maximized Efficiency

You can experience better efficiency for both your assets and your maintenance teams. When a piece of equipment fails, your maintenance team has to dedicate a significant amount of time to repairing or replacing it. When equipment is monitored and reported, issues can quickly be addressed and resolved.

More Available Equipment

Also known as uptime, the availability of your equipment is how often the equipment is in full operation during normal circumstances. By implementing a predictive maintenance plan, you can ensure that your equipment is more reliably up and running when you need it.


How are predictive maintenance data analytics implemented?

Predictive maintenance relies on the use of equipment to assess the performance and condition of your assets. When you’re ready to implement a predictive maintenance plan, a number of different pieces of equipment that all connect to a single platform.

Here’s what you’ll need to do:

1. Analyze Historical Data

As you prepare to implement a predictive maintenance plan, you’ll first need to analyze your current equipment to review instances of defects, downtime, and any frequent issues.

Be selective about which assets take priority because you most likely won’t be able to implement a plan for every piece of equipment. The assets with high and frequent maintenance costs are imperative to the success of your operations. After those are taken care of, move on to planning for the remaining assets.

2. Install IoT Equipment

IoT equipment is the foundation for the success of your predictive maintenance plan. Without it, you’re only relying on your scheduled preventive maintenance plan. Sensors, gauges, and meters that measure temperature, vibrations, sounds, and other factors are all examples of IoT equipment that connects to your system to analyze equipment health and performance.

3. Establish Equipment Parameters

As your IoT equipment begins collecting information on your assets, your system will begin organizing patterns and trends in the data. It will then creates a prediction model for your equipment and establish parameters for when an asset is expected to fail. Over time, the parameters become more and more accurate and will trigger an alert when an asset needs immediate inspection and maintenance.

Once you begin receiving alerts, you will need a plan in place that details who will perform maintenance.

4. Use a CMMS to Connect All of the Pieces Together

All of the data collected on your assets needs to lead to one place—your computerized maintenance management system (CMMS). Here, all of the information gathered by your IoT equipment can be used to create, assign, and manage work orders. This streamlines your entire process and ensures the right people are where they need to be to successfully fix equipment.


Connect to our CMMS for a seamless process!

Predictive maintenance continues to increase in popularity because of the cost savings and better reliability of equipment. If you’re considering a predictive maintenance plan, you first need to start with a CMMS that not only gathers data from your assets but also turns it into actionable tasks. Check out the benefits you’ll receive when using our CMMS by requesting a free demo.

Read the MLSE Case study about advanced analytics