Predictive maintenance: a $28 billion industry in the making


Alexander Hill, Co-Founder at Senseye, looks at the evolution of predictive maintenance over the last five years, and identifies some of the key drivers behind its growth

An accelerating industry

The last five years has seen predictive maintenance well and truly come of age. Following a fairly steady 25-year evolution, which has seen CMMS systems make way for Enterprise Asset Management, before paving the way for more traditional Asset Performance Management tools, along with an entirely new generation of predictive maintenance-focused tools, asset reliability and performance has well and truly made its way up the agenda.

By way of comparison, if the last 25 years has seen an evolution of such systems, the last five years has witnessed a revolution.

From niche to mainstream

Five years ago, predictive maintenance was still in its relative infancy, worth less than $1.5 billion globally[1], with most systems operating on a standalone basis and being comprised of ‘DIY’ style data-science solutions, with benefits being held back by a lack of interoperability and understanding. In line with this, examples of return on investment (ROI) were scant.

Subsequent years however have seen advanced integration capabilities and the availability of APIs expedite use cases of predictive maintenance, with integration with existing enterprise systems, whether ERP, CMMS or EAM becoming more commonplace, and as a result, greater value being recognized.

In parallel, a rise in sensor availability, and reduction in cost, has supported increased demand ease of use when it comes to the deployment of wireless sensor networks, with

Gartner reporting that predictive maintenance is becoming a dominant aspect for Industry 4.0 use-cases in many business transformations.

This more integrated, sophisticated use of the software has unlocked huge ROI. Testament to this ROI is Senseye’s recently launched guarantee, ROI Lock®, which, in partnership with SCOR, a global tier one reinsurer, reimburses customers’ subscriptions costs should downtime reduction targets not be met. When you consider that, using Senseye’s innovative predictive maintenance platform, unplanned downtime is typically reduced by up to 50%, with productivity boosts of 55% and maintenance accuracy increases of 85%, it’s easy to see how the value is propelling market growth.

Data sources

Another driver of this evolution is data quality and availability. Access to data via a number of sources including existing control systems, not just via retrofitted sensors, has allowed organizations to embark upon innovative predictive maintenance programs using existing data, whether it measures cycle times, vibrations or motor current, with sensors often forming part of plans to enhance, rather than commence initiatives.

The next five years

Predictive maintenance in 2021/2022 sees the technology having evolved from a standalone, niche framework or DIY solution, into a fast-growing application that is truly delivering high ROI and value to organizations – it’s the killer-app of Industry 4.0.

With new entrants in the market like Amazon with their Monitron offering, it’s clear that this is an industry with serious scope for expansion.

Accelerated by Industry 4.0, IoT and AI, analysts predict that the market will continue to grow at a rapid pace, and is set to be worth $28.2 billion by 2026[2]. In the future, we anticipate that it will represent part of existing enterprise software, or companies will opt for best of breed and tightly integrate with existing systems. In fact, Gartner predicts that by 2026, 60% of IoT enabled predictive maintenance solutions will be delivered as part of enterprise asset management products, up from 15% in 2021.

Also, energy-intensive industries, such as manufacturing, will have a leading role in combating climate change and driving sustainability worldwide.

In order to achieve sustainable and safe industrial operations, it’s clear these organizations will have to use real-time data, analytics and predictive maintenance technologies to support improvements in the following key areas of sustainability:

  • Reducing energy consumption in manufacturing processes
  • Reducing materials and spares waste
  • Maximizing the lifespan of assets
  • Mitigating health and safety risks

Looking ahead, predictive maintenance will be as critical to industrial organizations as ERP or financial planning software, as it facilitates a level of equipment performance which is commensurate with demonstrating best practice, adhering to industry standards and driving competitive advantage.