2017 was not as eventful a year as many had expected for IIoT asset-maintenance. Although almost every industrial manufacturer has included it as an operational or strategic priority, some are holding back on major investments until there is more clarity in the market. At the same time, others are rushing forward.
The technology outlook is rapidly developing. Market entrants battle entrenched vendors that are upgrading their solution-offerings to keep pace with the new entrants’ innovation.
Let’s look at 2018 and share insights from our commercial engagement with leaders (and laggards) in manufacturing.
Trend No. 1—Pause in IIoT infrastructure investment
In early 2017, there was no sign of letup in IIoT R&D investments on the part of industry behemoths such as GE and Siemens. However, GE has announced a strategic a shift to cost-cutting and a slowdown in investment and customer acquisition. What does this indicate for the overall predictive-analytics segment? GE has not achieved the growth within industrial plants that analysts had expected and this indicates a hesitancy on the part of the industrial sector to commit to a singular IIoT platform.
Trend No. 2—Momentum for unsupervised machine learning (ML)
With Unsupervised Machine Learning, advanced algorithms analyze machine sensor data without the need to “train” the data labels. Whereas supervised ML requires the learning algorithm to be trained on the physical machine blueprints and mechanical processes, unsupervised ML is agnostic to vendor, asset age or sensor type. With advances in unsupervised ML, industrial plants now have the opportunity for this low-touch analytics methodology.
Trend No. 3—Automotive industry will become the clear leader in IIoT asset-maintenance
More than any other sector, the automotive industry recognizes the potential value from IIoT predictive maintenance from both strategic and operational perspectives. The significant investments in R&D touch all aspects of the manufacturing process, including a serious commitment to reduce the industry’s Achilles heel—unscheduled downtime. The automotive companies that demonstrated a willingness to be early adopters of nascent technologies are expected to be the first to benefit financially from advances in AI and advanced predictive analytics.
Trend No. 4—IIoT predictive maintenance will be viewed as a source for top-line growth
The typical cost-justifications for traditional predictive maintenance (PdM) are based on increased operational efficiencies and savings. With Industry 4.0, executives are starting to consider the impact on top-line revenue from their big-data investments. With the shift from Industry 3.0 to Industry 4.0, metrics such as improved uptime and higher-production yield rates are replacing downtime as the driving force for investments in this technology category.