While industrial IoT held much promise, even one of its first general use cases involvng Predictive maintenance have failed to gain a lot of traction.
From its earliest stages of inception a few years ago, Predictive maintenance – employing data analytics to proactively predict equipment stresses and failures – appeared to be the killer use case of the Industrial Internet of Things.
In theory, with data streaming in from everything from trucks to trains to engines to elevators, the industrial IoT would dramatically increase the efficiency of machines and open up a new generation of service businesses.
However, predictive maintenance – along with other aspects of industrial IoT – has been slower to catch on than originally thought. V in this space have fully developed their solutions for predictive maintenance, but enterprise customers haven’t quite been going along yet.
That’s the word from a recent survey of 600 high-tech executives by Michael Schallehn and Christopher Schorling, both with Bain & Company. The Bain study found that industrial customers were less enthused about the potential of predictive maintenance in 2018 than they were two years earlier. “Conversations with many customers reveal that implementing predictive maintenance solutions has been more difficult than anticipated, and it has proven more challenging to extract valuable insights from the data, Schallehn and Schorling state. The problem has been integrating such capabilities into existing operational technology.
Instead, enterprises are eager to try out augmented reality and virtual reality for maintenance and training, the survey shows. The catch is, most vendors aren’t quite ready.
Predictive maintenance isn’t the only trouble spot in today’s IoT implementations, the Bain study finds. “Many have found IoT implementation more challenging than they anticipated. Because of this, customer expectations have dampened somewhat. While long-term predictions for industrial IoT implementations remain positive, for the near term, “customers expect implementation to be a bit slower than they did in 2016,” Schallehn and Schorling find.
