The predictive maintenance market is sat on the precipice of a huge boom over the next five years, according to a report collated by Reports Intellect.
With more and more industries comprehending the benefits of predictive maintenance, the industry is only set to grow.
The benefits of predictive maintenance and the wider predictive analytics industry are well acknowledged. Machine maintenance was traditionally reactive, with machines being replaced or having key parts repaired when they break down. Some companies also combined this with preventive maintenance: maintaining and repairing their machines in order to minimise break downs in the first place.
This used to be considered best practice, but the problem with preventive maintenance is that you must have downtime on each machine, even if it doesn’t need it. This downtime will reduce productivity, potentially needlessly. However, in the wake of the development of Industry 4.0, companies such as Proekspert have revolutionised the maintenance industry by implementing predictive maintenance systems.
Predictive maintenance allows businesses to plan their maintenance more efficiently and predict when maintenance might be needed (scheduling it before it reaches the point of disrepair). This will minimise any machine downtime and therefore increasing efficiency and productivity. Predictive maintenance enables just-in-time workflows, where servicing can be executed only when it is needed.
So, when is predictive maintenance the right choice? If you have a rarely used machine, or one that is cheap and easy to replace, then predictive maintenance is unlikely to be worth the initial outlay.
But if you have a machine or asset which is integral to your business success, or which would lead to substantial financial losses if it had downtime, predictive maintenance strategies are strongly recommended. This will allow you to avoid system failure and save you money, both in the medium and long term.
A Rapidly Growing Industry
Predictive maintenance is an industry that is ideal for heavy equipment, such as engines, wind turbines, and manufacturing machines. In fact, it is one of the most funded use-cases of AI across all sectors of heavy industry, according to an AI opportunity landscape enquiry. But, whilst this industry is growing rapidly, and its benefits are undisputed, putting the concept into practice is not a simple process. Each piece of equipment or machinery has its own unique pattern of data.
This means its calibration and diagnostics will also prove challenging. Determining exactly which data should form the core part of the diagnostic data stream for a specific piece of equipment is likely to require expensive and time-consuming iteration. It’s important for industries to view this as a long-term investment, and one that will have ongoing benefits for their business.
Whilst it’s important to acknowledge that initial financial outlay is not insignificant, ultimately a good predictive maintenance system should save you considerably more money that you spend on establishing it. The business value that predictive maintenance could bring means that it is well worth consideration.