Industrial IoT Predictions for 2018

What are the biggest changes expected in Industrial Analytics for Predictive Maintenance in 2018?  What are the underlying trends within the industrial sector? As 2017 winds down, we present our analysis of the year ahead for the IIOT Predictive Maintenance category.  The following is an abstract from the Presenso blog on this topic. 

Trend No. 1—Slowdown in IIoT infrastructure investment

With GE’s announcement of a recent time out for its Predix platform, many industrial plants are sitting on the sidelines.  The issue is not whether IIOT Predictive Asset Maintenance is a fad. It is not. The question that plants are dealing with is whether to commit to a single IIOT infrastructure platform.  The wait and see approach may impact some of the large IIOT infrastructure vendors.

Trend No. 2—Momentum for unsupervised machine learning (ML)

Unsupervised Machine Learning uses advanced algorithms to analyze machine sensor data and detect anomalous behavior.  It is called “Unsupervised” because there is no need to “train” the data labels. Supervised ML requires the learning algorithm to be trained on the physical machine blueprints and mechanical processes.  Both analysts and industrial facility owners are paying more attention to this solution type.

Trend No. 3—Automotive industry advances in IIoT asset-maintenance

The automotive industry stands out as an early adopter of IIOT Predictive Maintenance. The industry is undergoing disruptive changes and has recognized its need to re-define business models and core offerings.  IIOT Predictive Asset Maintenance addresses a major industry weakness:  unscheduled factory downtime.

Trend No. 4—IIoT predictive maintenance will be seen as a solution for top-line growth

In the old days, traditional predictive maintenance (PdM) solutions were justified based on costs savings and other operational metrics. With Industry 4.0, executives are starting to consider the impact on top-line revenue from their big-data investments.  Executives are looking for solutions for improved uptime and higher-production yield rates.

Trend No. 5—Holistic view of predictive maintenance within IIoT asset maintenance

Forget the siloed, data-driven approaches to IIOT asset maintenance.  Today’s industrial plants need integrated processes that include automated repair scheduled, inventory management and inspection.  The holistic approach to asset maintenance is essential if companies are to gain all the value generated by Big Data.

Trend No. 6— Big Data Centers of Excellence less of a factor

It wasn’t long ago that industry executives were using a lack of qualified big data engineers and scientists as the excuse for not deploying IIOT for predictive asset maintenance.  The good news is that with advances in technology, third party vendors can analyze data in the cloud without the need for internal big data resources.

Trend No. 7— The growth of multi-asset predictive-maintenance solution offers

Forget predictive-maintenance solutions based on SCADA that were sensor- or machine-specific. With Machine Learning, industrial plants can take a holistic view of their operations and use solutions that span multiple assets regardless of sensor type, asset class or age.  Why is this important?  Solutions that analyze the behavior of multiple assets can generate Root Cause Analysis.

Trend No. 8—OEM’s changing their business model

Many traditional OEM’s are embracing the business model typically associated with software.  In the quest for recurring revenue, we are seeing hardware vendors moving to a software as a service (SaaS) licensing agreements. How does a hardware vendor achieve this?  By bundling a physical machine asset with a predictive maintenance solution.

Trend No. 9—New market entrants in the IIOT Predictive Analytics category

Investment funds are pouring into the IIoT asset-maintenance category.  With more M&A activity and new start-ups entering into the market, an evolving Predictive Analytics ecosystem is emerging.

Trend No. 10—Industry analysts diminishing credibility

Without referring to specific names, there are a number of high profile analysts that have attached their careers to the burgeoning IIOT market.  Some of these analysts have released unrealistic reports of market growth.  The closer we get to 2020, the more it becomes clear that the predictions of disruptive change within short time periods were exaggerated.

Do you have a prediction for 2018?  We’d love to hear your feedback!

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  • Thanks for sharing an insightful piece of content. Also, I came across some market survey reports which say that only 18% of the asset failure occurs due to the prolonged usage 82% occur due to poor maintenance and not being able to predict this prior. In order to overcome these factors, a predictive maintenance solution plays a major role.

    How Predictive Maintenance Helps to Scale Up The Manufacturing Industries
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