“In the spring of 2016, we attended an industry-leading maintenance conference in Dortmund, Germany, full of hopes to discuss how companies were embracing the new AI-enabled paradigm of predictive maintenance.
In 2016, the global predictive maintenance market was valued at $1.5 billion, considerably smaller than the condition-based maintenance market.
Predictive maintenance in 2021 is a $6.9 billion market, according to our latest analysis on the topic, titled Predictive Maintenance Market Report 2021-2026.
Going forward, we estimate that the predictive maintenance market will see a strong recovery post-COVID-19 alongside a heightened and broad interest in digital topics in general .
The growth is fueled by a large number of start-ups that have recently entered the topic, notably in the analytics part of the technology stack, helping users make sense of their ever-growing and scattered data.
With the market expected to expand quickly and companies seeking support in making accurate predictions using their numerous data sources and in connecting predictive maintenance solutions to their core business systems, there is a lot of room for more vendors to enter the market.
Two years later, in 2018, tech vendors had not made much progress in addressing the latter two, but it had become clear that IoT projects could deliver definite ROI.
An IoT Analytics survey of ~100 senior IT and OT managers from the industrial sector observed that predictive maintenance implementations yielded a positive ROI in 83% of the cases and that 45% of those reported amortization in less than a year.
In the future, as PdM solution sophistication and ease of implementation increase, we expect predictive maintenance ROI to increase even further.
Most projects still only use a small selection of those sources, but as more sources are tapped, the average precision of the predictions increases.
In September 2020, AspenTech revealed that it has been working with over 60 companies to develop and test Aspen Hybrid Models that combine physics-based models with ML data science knowledge to enhance the prediction accuracy of PdM solutions.
In the future, as more machine and process data accumulate and data lakes/libraries of highly contextualized machine datasets are created, we expect that companies will have an even richer pool of data to tap into to deploy solutions.
IoT Analytics expects that sensing devices will also enable algorithms to run at the point where data is collected, thereby reducing response latency and increasing the benefits offered by a solution.
An IoT Analytics survey of technology providers found that interoperability between the different system components was regarded as the main challenge for most IoT-based industrial analytics projects.
A 2020 survey that is discussed in depth in the report shows that the clear majority of predictive maintenance solutions are integrated with other business systems, such as enterprise resource planning solutions.
In the last couple of years, there has been a movement towards a.) the increased use of automated predictive maintenance software and b.) the development of “low-code/no-code” solutions.
At IoT Analytics, we estimate that in the next five years, the current proliferation of low-code/no-code and automated PdM software solutions will advance, enabling non-tech experts to realize PdM without significant domain expertise or programming skills.