Under Internet of things, predictive maintenance is made possible through the storage of data collected from sensors and environmental conditions. It applies rules and history to predict performance issues. The following article explains why most companies using the technology are not enjoying all the benefits from it in the short run
Predictive maintenance is, arguably, the most hyped application of IoT technology currently available to the enterprise user, and it’s easy to understand why: Getting greater insight into industrial machinery, fleets of vehicles or anything else that can be digitally instrumented seems to offer a fairly direct path to savings through lower maintenance costs and less downtime.
But it’s not as simple as just grafting sensors onto existing equipment, according to experts, and reaping the benefits of predictive maintenance isn’t an automatic win for the asset-heavy businesses that can profit most from this IoT implementation.
The challenges, according to ABI Research, can be seen clearly in the track record of IoT usage in the oil-and-gas industry. Offshore oil spillage is still relatively common, despite the widespread use of IoT services, and a big reason for that is that the AI/ML piece of IoT just isn’t that well implemented as yet.
“While top oil players market themselves as pro-tech, with predictive analytics being the key to their investment,” ABI analyst Kateryna Dubrova wrote last month, “consulting firms and the hiring of a few experts is not making the technology work and subsequently not making a difference in preventive measures.”
Not having a top-to-bottom plan for getting real value out of the oceans of data an IoT project can generate is the biggest reason that companies don’t see measurable results from predictive maintenance, said Forrester analyst Frank Gillett. Businesses sometimes get excited, place sensors everywhere they can, and then expect the payoff to develop on its own.
“There’s lots of examples of people looking at sensor data and then trying to build a business case, rather than trying to build a business case first,” he said. “It’s like walking around with a hammer and not finding any nails.”
Much of that has to do with the fact that making AI and machine learning work correctly is difficult. Companies need plenty of data science expertise – whether in-house or from their vendors – to make sure that training data is teaching the model the correct lessons. Moreover, moving data around freely is tricky in certain industries, where companies might be reluctant to hand over operational information to a third party. For example, a manufacturer might not want to release performance info on factory equipment if that info could provide outsiders with an insight into confidential processes at work.
Users also need a much more holistic understanding of how predictive maintenance actually drives business value, according to Cambashi principal consultant Alan Griffiths, who also noted that institutional expertise in IoT is invaluable to make everything work.
“When you look at the technology required, it’s quite sophisticated,” he said. “Each [component of IoT] is fairly well understood, but it can be complicated to implement, especially with old-fashioned IT departments.”
Yet it’s easy to understand why companies are in such a hurry to adopt the technology – there are simply too many potential benefits to ignore. Tracking maintenance information offers businesses additional surety that the money they’re spending on replacements and repairs is being spent in the right places, and lets them cut down on unnecessary outlay.
451 Research vice president Christian Renaud said that the possible upsides are hard to overstate.
“There’s a bunch of different ROI things, production, asset value, worker safety, and then all the fluffy benefits where you collect all the data from these things and glean insight from historical trends,” he said. “This is something that has been the ultimate use case, long before we started calling this IoT.”
And, despite some hiccups and a knowledge gap where the data analysis piece of the puzzle is concerned, there are plenty of users out there making predictive maintenance work for them. A recent survey from 451 shows that predictive maintenance is the most-used application of IoT among operational technology companies, and that, of those, the vast majority report at least “somewhat positive” ROI.
“There are so many benefits to getting telemetry off these machines,” said Renaud.