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How can machine learning help with maintenance, repair and overhaul (MRO) processes?

Answer
By Justin Stoltzfus | Last updated: September 10, 2018

How can machine learning help with maintenance, repair and overhaul (MRO) processes?

Machine learning can help with both predictive and regular maintenance, and the general maintenance, repair and overhaul (MRO) processes that companies use to support and preserve their assets, such as vehicles, equipment and other useful items.

In general, structured maintenance, repair and overhaul plans benefit from all sorts of data aggregation and analysis practices. Machine learning is driving many of the new tools and platforms that work on specific MRO problems to help companies to innovate and make overall maintenance more efficient and effective.


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One key way that machine learning is helping with MRO is in building predictive accuracy.

A Forbes article, "10 Ways Machine Learning Is Revolutionizing Manufacturing," talks about improving maintenance through more predictive precision in regard to parts and components. The idea is that by integrating data from databases and other sources, machine learning systems can offer companies more business intelligence in the maintenance arena. That in turn will add capability to maintenance, repair and overhaul processes, and foster more proactive predictive maintenance, as well as better regular scheduled maintenance and operational efficiencies – for instance, having the right processes in place to do the scheduled maintenance, and having a more robust reporting system for what's already been done.

Machine learning can also be applied to a maintenance, repair and overhaul inventory. MRO processes rely on inventories of parts and products that will support effective maintenance. For example, companies will keep certain amounts and numbers of parts and pieces on hand for a vehicle fleet, such as bulk orders of brake pads and brake shoes, oil filters, or anything else that's commonly applied to regular or predictive maintenance.

Handling these inventories is, as anyone could imagine, a complex affair. Where the inventories are, how they are labeled, and when they are applied to a maintenance, repair and overhaul system makes a difference. So does the application of machine learning processes that can enhance the handling of MRO inventories or solve problems related to those inventories. Missing data can throw a wrench in a business process. Machine learning can seek to secure that data and bring more consistent analysis and processes to the table. It can also help to determine factors such as labor costs, or add intelligence on mean time between failures, or work with any number of other metrics, benchmarks and indicators to streamline a maintenance, repair and overhaul process and make it work better.

At a very basic and fundamental level, a machine learning approach adds certain advantages – the advantage of handling larger numbers of predictive variables to create better business intelligence. Its strength is in its agility and the capability of handling the complex data that provides transparency on all sorts of maintenance elements, from parts inventories to labor management to long-term design and engineering analysis.

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Written by Justin Stoltzfus | Contributor, Reviewer

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Justin Stoltzfus is a freelance writer for various Web and print publications. His work has appeared in online magazines including Preservation Online, a project of the National Historic Trust, and many other venues.

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