Reporting from the ERP Today News Desk, live at the IFS Unleashed event in Orlando, Florida, Mark Vigoroso, Chief Content Officer, ERP Today/Wellesley Information Services and Stephanie Ball, Deputy Editor, ERP Today, sit down with Berend Booms, Head of EAM Insights, IFS Ultimo and Chris van den Belt, Head of Product Management, IFS Ultimo to discuss AI use cases for Enterprise Asset Management (EAM) and how Ultimo (which was acquired by IFS in 2022) is leading this change.
Enterprise Asset Management solutions have undergone a sea change over the past couple of years as artificial intelligence (AI) takes center stage. However, Booms says that most AI functionalities today tend to focus on preventive maintenance. Still, he notes, it is equally important to consider reactive maintenance in an AI plan for EAMs.
“We believe in AI for preventive maintenance and have a large network of partners we integrate with to service our customers who ask for it. But we have realized that reactive maintenance is here to stay for the foreseeable future,” Booms explains, “That is why we are focusing on adding AI value to reactive maintenance.”
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According to van den Belt, this route aims to cut down every per cent of time spent on reactive maintenance. He explains that 80% of the meant time to repair (MTTR) is spent on figuring out the root cause of the failure. “That is a lot of time, and it is expensive. It also affects the technicians’ productivity,” van den Belt says.
One of the reasons for this downtime is the lack of detail in a failure report, according to van den Belt, who gives the example of a field pump failure report, which could be as vague as ‘field pump failure, please fix it.’
“However, the technician who writes that report is on the field and might have seen, heard or felt issues that are not included. We have seen that this is usually because they simply would rather be on the field than spend time on a laptop or on a mobile phone,” he explains.
IFS Ultimo has built AI that uses a large language model (LLM) to suggest those sensory observations of the asset. The technician can simply click on the observations suggested by AI and attach them to the report. “As a result, we have more details for the technician to solve the issue right away rather than having to go to the asset to observe the situation himself,” van den Belt explains. These AI models use generally available GPTs, so organizations do not have to spend time or money to train their own models.
There’s a lot of value in this AI use case. “We have this gigantic figure of 80% MTTR,” says Booms. “Now take into consideration the price point at which this comes as the average manufacturing plant generally has around 15-20 hours of downtime a week. Even a 5-10% reduction means millions in monetary value apart from higher productivity.”
This AI use case can be extended to add details on how the issue was fixed to the failure report to strengthen the data on work order history and make it valuable for future occurrences of the same failure.
“Data is a huge asset in the EAM business, so if you work towards increasing the quality of your data, you are realizing the short-term benefits of AI and also leapfrogging your journey to achieving your long-term objectives,” Booms notes.
IFS Ultimo plans to release the AI models for getting a failure report and more details of a failure report in EAMs shortly. “Ultimo works on a continuous delivery model, which means we have a continuous improvement cycle, and anything that gets released into the Ultimo version today will be instantly available to our customers starting next week,” Booms concludes.
Note: This ERP Today TV content comes sponsored by IFS.
