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Why combining an EAM system with IoT means faster insight

IoT technology is the next big thing in enterprise asset management and can help companies get richer insight more quickly. Here's a look at the promise -- and the challenges.

To operate efficiently, enterprises are required to track, assess and guarantee the reliability and performance of their assets. And for years, organizations have been using enterprise asset management systems to keep their assets operating at peak efficiency.

In fact, ensuring the health of their assets is a top priority for best-in-class companies, which are 37% more likely than all other companies to run EAM systems, according to Aberdeen Group's "Maintaining the Health of Your Most Critical Assets with EAM and IoT" report.

An EAM system "allows organizations to ensure asset health by listening to, and learning from, enterprise assets," the report noted. "It also improves operational efficiency, through enhanced analytical insights and recommendations, for preventive and predictive maintenance."

By combining the intelligence of the internet of things (IoT) with the functionality of an EAM system, companies may be able to improve the way they manage their assets across the enterprise.

While next-generation EAM systems allow companies to "listen" to high-risk equipment wherever the data comes from -- for example, a data historian system -- these systems are incorporating IoT to give enterprises a new level of real-time visibility into their assets, Aberdeen noted.

"IoT lets your assets 'talk' to you, providing streaming data and real-time visibility to assets," according to the report. "This visibility imparts the ability to monitor serviceable assets, with an eye toward predictive analytics and predictive maintenance."

EAM plus IoT for faster insight

Integrating an EAM system and IoT may enable companies to identify problems with their assets so they can quickly take the necessary actions to prevent problems.

"The IIoT [industrial internet of things], which is the hot topic in asset management, is the next generation," said Tracy Smith, president of SwainSmith Inc., a consulting firm in Franklin, Tenn. "IIoT is laid on top of an EAM system, and they work together to enable companies to be able to make faster decisions regarding their assets."

IIoT -- the use of IoT technologies in manufacturing -- is there to help companies facilitate, expedite and capture information that they might not otherwise be capturing, he said.

A common misconception about IoT is that it is powered solely by data generated from connected equipment. However, that could not be further from the truth, said Dave McCarthy, senior director of products at Bsquare Corp. in Bellevue, Wash. The most desirable IIoT use cases, such as predictive or condition-based maintenance, can only be achieved by combining asset-generated data with information stored in ERP and EAM software.

In fact, IoT is not intended to replace those existing systems but rather make them more intelligent by providing context and visibility into real-time operations, he said.

Over the last several years, McCarthy has had the opportunity to work with companies in a lot of industrial contexts -- for example, manufacturing, oil and gas, and transportation -- and they are all trying to figure out how to take advantage of IoT.

"And all of them have existing investments in either ERP, EAM solutions or a combination of other things that feed their existing business processes today," he said. "They understand that it's only when you can combine that new source of data with those existing enterprise systems that you can get to some level of value."

IIoT tops in next-gen EAM

From an EAM standpoint, manufacturing is probably going to be the biggest source of integration implementation of IoT technologies, said Richard Henderson, global security strategist at Absolute Software Corp., based in Vancouver, B.C.

"That's because it's expensive to deploy technicians in the field to be able to go and look at things, do preventive maintenance or regular quarterly or annual checks of things in the field," he said. "If [companies] can just park a sensor there and be able to monitor the core functionality and telemetry from these systems, why wouldn't they do it? So, we're starting to see that now, and I think it's only going to get bigger as companies like IBM start to develop much larger solutions for companies."

Connectivity challenges of EAM, IoT

There are some challenges that companies have to face when they integrate IoT and EAM systems, including connectivity, particularly for companies in remote areas, said Gina Murphy, senior vice president and general manager of Rackspace Application Services in San Antonio, Texas.

"One of our customers, a department of transportation, is in the middle of nowhere in the Midwest, where there just is no connectivity," she said. "They have a device that goes into the trucks, but it's not connecting back to them just yet."

"They're still working on the networking part of it -- the wireless capability to be able to have it call home and provide that data. So, there's still the connectivity part," she added.

Although the agency does get the data from the trucks eventually, it doesn't get it in real time yet, Murphy said. And because the transportation agency is unable to react to the data immediately, it takes a little longer to make decisions about the assets.

"But we do have customers that do have the connectivity that it is automatic and coming through and putting it into the data warehouse immediately as it happens," she added.

Making ERP and an EAM system work together

To be of use, IoT technology, the EAM system and the ERP system all need to work together. That can be a challenge.

A difficulty of using an EAM system and IoT is getting the data into the ERP system and making it actionable, said Seth Lippincott, an analyst at Boston-based Nucleus Research.

"ERP systems and ERP vendors are seeking to make a sort of way in which that can be streamlined and a way in which an ERP system can absorb that kind of information and digest it more easily," he said.

You can have an amazing machine learning algorithm, but if you're not asking it the right questions, it's not going to tell you anything.
Seth LippincottAnalyst, Nucleus Research

But one of the biggest challenges around working with large data sets and large amounts of information is that the value you get from it is really only as good as the questions you ask it, according to Lippincott.

"You can have an amazing machine learning algorithm, but if you're not asking it the right questions, it's not going to tell you anything," he said. "So, I think, that's still a place where vendors need to make it easier for customers to get to that point, either by standardized queries, standardized use cases or giving them the tools to figure out what questions they need to ask for their own enterprise."

However, in some cases, depending on the age and types of equipment, companies may not even have the right data points needed to get the information they need to make the right decisions, according to McCarthy. And that could be because they're using legacy data systems or other methods that were really expensive at the time.

"Then, when those companies go back to the vendors and say they want to collect more data points, trying to go back to those preexisting automation systems can be cost-prohibitive," he said. "And this is where I think IoT helps solve that problem because, in some cases, we see people continuing to use those legacy systems for what they were intended but creating almost a second stream of data using more low-cost sensors and connectivity to be able to augment that existing information."

That also leads to some challenges because, now, companies have different data formats coming in or they might have different sample rates of data -- that is, the newer sensors are reporting data at a much faster rate than some of the older systems.

"And then, you start talking about the challenges of combining what feels like structured and unstructured data," McCarthy said.

The good news is that industry has been trying to figure out ways to do this. It's a combination of being able to align data in different ways and applying technology, such as machine learning, not just to add structure to the unstructured data but to be able to combine it with the new asset information to pick out the meaningful patterns, according to McCarthy.

This is one of the challenges that any company that has gone from an unconnected or mildly connected state of its equipment to a much higher-fidelity state has completely underestimated -- or has been ill-prepared for -- the volume of the data that's now coming in and how difficult it can be to make sense of it, he said.

"The reason you're seeing the popularity of IoT platforms in those areas is that those companies have been focusing on how to bring together all those data types and different data sources and different frequency rates and put them into a common format so companies can then run data analytics and machine learning and drive the insights automatically back into those existing systems," McCarthy said.

This was last published in December 2017

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