Launch of predictive maintenance solutions continues to gain massive momentum in the ICT industry

June 2, 2022 1:40 pm

Increasing use of innovative technologies such as advanced neural networks and artificial intelligence to deduce and raise necessary alerts.

Predictive maintenance has gone through transformational changes over the last couple of years, owing to the surge in need to propel asset uptime and reduce maintenance costs of the machineries. In the earlier days, companies used conventional systems, and hence they had to depend on the historical data about previous equipment breakdowns and performances. Predictive maintenance is an ideal technique adopted by numerous players of the market to effectively monitor the performance and condition of the equipment so as to appreciably minimize the risk of equipment failure under normal or uniform working conditions. On the other hand, modern predictive maintenance solutions repeatedly monitor the behaviour and performance of the equipment so as to collect the vital data in real time.

Increasing use of innovative technologies such as advanced neural networks and artificial intelligence (AI) to deduce and raise necessary alerts if any sort of potential equipment failure is bound to happen. In addition, various organizations across the globe are leveraging on various machine learning technologies for not only increasing the speed of processes over conventional tools, but also for providing enhanced accuracy to monitor crucial data about the equipment. These factors drive the demand for predictive maintenance in the coming years.

Apart from that, there has been a significant surge in the awareness about the importance of predictive maintenance among industrial customers. Predictive maintenance is extensively used in the industrial manufacturing sectors such as the food and beverage and oil & gas industry. They are also widely used in other industries including healthcare, consumer goods, transportation, and others. Thus, organizations tend to adopt utterly effective predictive maintenance solutions to notable make a prediction 20 times faster than conventional threshold-based monitoring systems.

Besides, downtime can be quite expensive, and hence can cost a lot of money for organizations dealing in heavy industries such as oil & gas. These factors are further expected to boost the demand for predictive maintenance in various countries around the world in the forthcoming years.

Moreover, increase in investments in predictive maintenance, surge in need to prolong the lifespan of ageing industrial equipment, rise in concerns over data privacy issues, and increase in adoption and integration of industrial internet of things (IIoT) are predicted to create tremendous opportunities for the growth of the global predictive maintenance market. According to the report published by Allied Market Research, the global predictive maintenance market is anticipated to reach $31.96 billion by 2027, growing at a CAGR of 28.8% from 2020 to 2027.

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