The global predictive maintenance market was valued at US$ 2.1 billion in 2018 and is projected to reach US$ 17.7 Billion by 2026, registering a CAGR of 30.3% over the forecast period (2018-2026), according to Predictive Maintenance Market Report, by Component (Solution & Services), by Deployment (On-premise and Cloud), by Application (Asset Maintenance, Operations Maintenance, and Health & Performance Optimization), by End-use Industry (Oil & Gas, Aerospace & Defense, Automotive, Power Generation, Hospitals/Clinics/Diagnostic Laboratories, Biotechnology/Pharmaceutical Manufacturers, Medical Device Manufacturers, and Shipping), and by Region (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa).
Increasing awareness of real-time streaming analytics is expected to offer significant growth opportunities for the predictive maintenance software and services providers over the forecast period. Real-time analytics is the ability, or the capacity to utilize data and related resources as soon as the data is received at the system. Real-time analytics offers various beneficial features such as it aids in detecting faults instantly, reduces the data processing time, and helps in effective decision-making. These features are very effective in predicting faults in real-time and avoiding incidents caused by machine component failure. Therefore, increasing awareness of real-time streaming analytics can be a major growth opportunity for key players in the market during the forecast period. Moreover, increasing need for ease of operations and economic & scalable predictive maintenance solutions is expected to have positive impact on growth of the predictive maintenance market over the forecast period.
All the currently available predictive maintenance solutions are expensive and very complex to implement. These factors are restraining small and medium-sized enterprises from implementing predictive maintenance solutions. Development of economic, easy to install & operate, and scalable solution is expected to drive the small and medium-sized enterprises to adopt predictive maintenance solutions. Therefore, key players could focus on launching such systems and solutions to increase their market share. However, high cost and complex installation and implementation of predictive maintenance solution is expected to hinder the market growth. Predictive maintenance solution architecture consists of a large number of connected IoT devices such as sensors, receivers, high-end computer systems, and skilled labors. This makes the system very costly and complex. Eco-system of predictive maintenance system includes players from different levels of value chain such as raw material suppliers, system integrators, and original system manufacturers (OEMs), which makes the setting up of the solution system a difficult task. Considering these two factors, the overall predictive maintenance set-up is costly and requires high capital investment, which in turn may restrain growth of the market.
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