Barriers to Scalable IIoT Execution
By providing visibility into the assets and processes that historically had no data feedback loop, the Industrial Internet of Things (IIoT) has allowed manufacturing organizations to leverage their incoming data to implement crucial initiatives such as condition monitoring and predictive maintenance.
In addition, by establishing a constant and secure channel for data to flow from physical assets to digital tools, IIoT can provide organizations the ability to extend equipment lifetime, reduce equipment maintenance costs, and deliver more accurate data for production-quality improvements.
In a recent Software AG survey, 84% of automotive and heavy industry manufacturers agree that the most important area of IIoT is "monetization of product-as-a-service-revenue." Optimizing production is also viewed as a top priority, with 58% of heavy industry and 50% of automotive manufacturers agreeing with that statement.
Still, few manufacturing organizations are actually executing on their belief in the value of IIoT, primarily due to implementation and integration difficulties.
Case in point: The research shows Information Technology and Operations Technology (IT-OT) integration is considered one of the most difficult implementation tasks, with 57% of automotive manufacturers stating that this has prevented them from realizing a full return on their IIoT investments.
About 60% of manufacturers surveyed say that defining threshold-based rules is as difficult as leveraging predictive analytics. This is surprising because condition-based rules, at the lowest form, are simple if-then statements that any associate can create, while predictive analytics rely on complex algorithms that require a data scientist's expertise.
The data reveals that neither task is considered simple but each is rated as very difficult, with leveraging predictive analytics rated only slightly more difficult than condition-based rules.