In our last few blogs, we’ve taken a look at the rise in predictive maintenance, and identified some of the main drivers for its deployment, and indeed some of the most common obstacles, to getting it right. In this post, we examine shifts in the market and do some future gazing on where the technology is heading.
The rise and rise of predictive maintenance
Predictive maintenance is, without doubt, coming of age. Representing a means of preventing asset failure by analysing industrial production insights to identify patterns, trends and anomalies to subsequently predict changes, issues and failures before they become problems, its growth is being driven by a need to reduce downtime and costs, and boost both capacity and profitability. Predictive maintenance has been earmarked as the “killer app” of IoT and, and holds its own in commanding hefty ROI across multiple parameters.
When you consider that 82% of companies have had had unplanned downtime in the past three years, at a cost of up to $260K / hour, with outages lasting an average of 4 hours, (Aberdeen), the potential for predictive maintenance as a means of addressing this is vast.
Testament to the stark benefits it brings, is that the global predictive maintenance market is expected to secure a value worth US$ 45.5 Billion while displaying a compound annual growth rate of 21.9% during the forecast period from 2022 to 2032. To put this into context just five years ago, the predictive maintenance market was still in its relative infancy, worth less than $1.5 billion globally.
As well as being driven by increased pressure to extend the lifespan of machinery and equipment, increased investment in Artificial Intelligence (AI) and Machine Learning (ML) to minimise asset-specific downtime and maintenance costs, adoption of IoT is also key to the rise of predictive maintenance.
Drivers for investment
While core drivers such as ageing assets which are more prone to failure, and new assets which are increasingly complex and in some cases, sensitive to disruption, prevail, other shifts are expediting demand.
Sustainability is clearly dominating the priority list for manufacturers currently, with a recent Make UK report stating 50% manufacturers are making headway with sustainability programmes, and a further 70% of those reporting that they have benefited from a reduction in costs. Effective, efficient equipment performance, and minimising waste are clearly crucial pillars in any such strategy.
Staffing and skills shortages in the wake of both Covid and Brexit are also converging to drive a need for more efficient, remote means of monitoring assets, with greater reliance on remote diagnostics and even remote fixes. In turn this also supports safety agendas by minimising headcount on the shop floor.
Advances in digital
However, the rapid development of the market is largely down to accessibility and advances in digital. Until recently, technological obstacles proved prohibitive to widespread deployment. Now, a combination of cloud, easy integration, and ongoing advances in machine-to-machine communication, artificial intelligence, and big data have paved the way forward.
In fact, the use of Artificial Intelligence for predictive maintenance has a potential savings of $500B – $700B annually (McKinsey). Leveraging AI, predictive maintenance can evaluate the numerous factors that determine and affect an asset’s condition, informing maintenance managers of potential equipment failures in advance accordingly. For example, is there an actual problem with an asset or is the data merely highlighting an anomaly due to higher usage? We’ll see more businesses looking to replace traditional BI tools with AI and machine learning tech to more quickly, more accurately and more precisely analyse the abundance of data that’s now available.
Predictive maintenance software itself has evolved and matured, with the quality of data that can be harnessed from assets improving dramatically over the last few years. A combination of traditional methods and sensors are not only able to tell us if there’s a problem with a particular asset, but more specifically, which component of the asset is responsible for that problem. This better quality of insight is enabling more businesses to use their existing data to form the basis of comprehensive predictive maintenance plans, with ever-increasing interoperability and integration capabilities also helping to expedite the adoption of predictive maintenance capabilities across businesses of all sizes too.
Refining a predictive maintenance-led culture
As with any new technology, culture remains one of the biggest challenges. It’s important to remember that, while predictive maintenance delivers efficiency, visibility and cost-savings in its own right, it really comes into its own as part of an organization’s wider Industry 4.0 framework. Connected devices lie at the very heart of all things Industry 4.0, therefore predictive maintenance will increasingly become standard best practice as part of strategies. In fact, Gartner predicts that by 2026, 60% of IoT enabled predictive maintenance solutions will be delivered as part of enterprise asset management products.
This connected approach to asset performance necessitates a shift in mindset and often, a shift in responsibility and ownership away from OT to IT teams instead. Cloud will continue to gain traction, facilitating scale and flexibility, with on-premise systems gradually becoming obsolete, or used in more sensitive areas.
In the future, through the lens of Industry 4.0, predictive maintenance will elegantly integrate all of these features, and their nuances, into a single product which proactively and pre-emptively manages asset maintenance, improves uptime, and optimises productivity in an increasingly connected manufacturing ecosystem.
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