The factory of the future uses artificial intelligence (AI) to support maintenance and procurement teams — the humans on the floor — with their day-to-day tasks. This focused application of AI will lead to better maintenance, repair and operations (MRO) data, sophisticated inventory and spend optimization, and improved productivity and decision-making in MRO processes.
The outcome yields substantial advantages for the factory, including enhanced efficiency, reduced capital expenditures, and even a heightened competitive advantage. Notably, it also provides a tangible return on investment (ROI).
This blog post will explore the first step to making that future a reality: using AI to improve MRO data quality.
We’ll explore:
- The current data challenges facing MRO professional
- How AI can solve these challenges
- How AI-enhanced data will set factories up for more advanced AI applications like inventory and spend optimization
MRO data challenges
When an MRO professional needs to repair a production line, they look for spare parts data to assess what parts they need and in what quantity. The problem? The data is poor, with show-stopping problems like:
- Fragmentation: Records are buried in complex enterprise resource planning (ERP) systems and scattered across spreadsheets and lists.
- Lack of standards and cleanliness: Parts are mislabeled. Records include typos or other errors, are difficult to read, have poor formatting, or have duplicates.
- Incomplete records: Some spare parts data are missing important fields. Other records are simply missing.
- Incorrect records: Since many spare parts look identical to the human eye, it is not uncommon for them to be misidentified in the ERP system.
Such data problems make it difficult for maintenance professionals to find spare parts. When a crucial machine goes down, they waste time searching for records in the material master when they could be performing maintenance work to restore production and limit lost revenue.
What causes these data problems?
“The biggest issue is human error."
says SPARETECH CEO Martin Weber.
Imagine that you’re an MRO professional. You are under significant pressure to keep production lines running, as even a few minutes of downtime can be extremely costly for the factory. Procurement brings added pressure. You may receive over 10,000 parts a year and face the task of registering each in the ERP system according to your organization’s governance and industry standards.
As Weber explains, this would be much like entering 10,000 contacts into your smartphone.
Take a quick look at your phone now—you’ll get a sense of how this process plays out on a smaller, less stressful scale compared to the pressures MRO professionals face daily. Some of your contacts are probably complete, with full names, addresses, birthdays, and other details neatly filled in. But then there are likely those labeled “Cousin” with just a phone number or duplicate entries for the same person.
That’s what parts records look like inside a factory’s ERP system. Incomplete, dirty, and difficult to navigate.
“The reason for this poor data is how factories do data entry today. The human needs to key in every part themselves without making mistakes. At that scale, it makes sense that there are errors.”
Martin Weber says.
Another big problem is the lack of reference data. A catalog that includes pre-filled records for each possible original equipment manufacturer (OEM) part would be hugely helpful when filling out records. If an ERP system could automatically fill out a record for the MRO professional each time they create an entry, human error wouldn’t be a problem anymore.
In addition to human error and a lack of reference data, one more problem prevents factories from having complete data on their spare parts: Virtually every factory has parts in its inventory without corresponding records. This is a complex problem to fix because humans can't identify parts visually to create the necessary records.
The root of the challenge is that many parts may look the same to the human eye despite having completely different parts internally. This makes it nearly impossible for MRO professionals to identify what part they are looking at. Ultimately, this prevents factories from performing retroactive inventories to clean up their systems manually.
How AI can improve MRO data
According to SPARETECH Senior Account Executive Jack Reinke, the most realistic and useful application of AI in the next few years will be checking and enriching data sets.
That would help factories ensure that their maintenance professionals have comprehensive, organized, complete, and usable records for reference when they need spare parts quickly.
“AI can correct humans when they aren’t setting up the parts correctly. If someone sits down at a computer and begins to enter a part, AI can check that it’s not a duplicate. It can scan the record to ensure there are no typos or other errors. It can ensure good governance and check that every record aligns with industry standards. That’s the first step to better data.”
Jack Reinke says.
AI can also critically enhance existing records. The technology can automatically fill in missing fields with accurate and up-to-date information by leveraging data from verified spare parts catalogs.
Take, for example, a pen listed in your system with only the manufacturer’s name. Instead of leaving that record incomplete, AI can search trusted sources—such as the manufacturer’s website or other reliable databases—and enrich the record with all the necessary details. MRO software like SPARETECH provides tools to achieve this efficiently.
"The lack of good data quality extends beyond the factory floor. Many suppliers—component manufacturers, machine builders, and resellers—also struggle with inconsistent data due to multiple software systems and global operations. AI can help streamline and standardize this information globally, improving data quality and ultimately supporting MRO teams more effectively,"
says Simone Scheperle, Senior Partner Manager at SPARETECH.
AI may even be able to identify spare parts that have no record. As sensors improve, AI will better detect subtle differences between parts. As factories clean and enrich their data, AI will have better input for learning how to identify spare parts in text and photographs. That means AI may even be able to create clean, rich data for spare parts currently in inventory but without data attached.
ERP that works
What’s the big picture? AI enables a future where the human on the floor no longer needs to focus on data.
AI will help MRO professionals enter large numbers of parts quickly, automating the process for minimal pain and zero error. It will also ensure that all preexisting records are error-free and formatted according to the factory’s data governance rules and industry standards.
It will enrich existing data, creating more complete records that offer greater value in maintenance tasks. It will also help identify parts in inventory so MRO professionals can be certain about what they have on hand.
With AI's help, factories can improve their ERP systems so that maintenance professionals can find the data they need. This will help them find spare parts quickly and keep the production line running.
By using AI to help the human on the floor with high-quality data, factories can maximize uptime and minimize lost profit. They can sharpen their competitive edge and see real, measurable ROI.
What’s possible with high-quality MRO data?
A factory with good quality MRO data can begin using AI for other more sophisticated applications, such as: