According to a recent report titled, "Predictive Maintenance Market by Component, Technique, Deployment Type, Stakeholder, and Industry Vertical: Global Opportunity Analysis and Industry Forecast, 2019-2026 published by AMR(Ample Market Research)" the states that the global predictive maintenance market size is projected to reach $23,014.7 million by 2026 and register a CAGR of 30.20% from 2019 to 2026.
AMR recognizes the following companies as the key players in the Global Predictive Maintenance Market:IBM, Microsoft, SAP, General Electric, Schneider Electric, Hitachi, PTC, Software AG, SAS, Engineering Consultants Group, Inc., Expert Microsystems, Inc., SparkCognition, C3 IoT, Uptake Technologies Inc., Fiix Inc., Operational Excellence (Opex) Group Ltd, TIBCO Software Inc., Asystom, and Sigma Industrial Precision.
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Predictive maintenance facilitates multiple advantages such as reduction in downtime, prolonging equipment life, improvement in plant safety, optimizing spare parts inventory, reduction of maintenance costs, better yield rate, optimization of maintenance schedule, and enhancement of asset availability. Rise in need of improving uptime of the equipment, reduction of costs & quality risks, and extending life of assets are the factors that majorly drive the predictive maintenance market growth. In addition, rise in investment on prediction maintenance due to adoption of IoT also drives the market growth. Use of machine learning for predictive maintenance 4.0 offerings is expected to offer lucrative opportunities for predictive maintenance market size during the forecast period.
The on-premise segment dominated the predictive maintenance industry in 2018 and is projected to maintain its dominance during the forecast period, as it is considered more secure and reliable, as the entire software is installed on the organization’s premises. Furthermore, the cloud-based segment is expected to grow at a significant CAGR during the forecast period, owing to key benefits for businesses such as low cost, ease of implementation, and unlimited accessibility. The application of the Industrial Internet of Things (IIoT), AI and machine learning to predictive maintenance 4.0 is an essential part of Industry 4.0. Manufacturers are adopting advanced analytics, big data, and the cloud owing to the need to complete their transformational journey to Industry 4.0. This is further anticipated to boost the growth of this segment during the forecast period
The global predictive maintenance market was led by the manufacturing segment in 2018 and is projected to maintain its dominance during the forecast period. However, the healthcare segment is expected to witness the highest growth rate, as recent trends in healthcare are enforcing healthcare providers to maintain a high degree of profitability and innovation due to which medical technology and medical device manufacturers have started to adopt predictive maintenance approaches.
Based on region, the global predictive maintenance market was dominated by North America in 2018 and is expected to maintain this trend during the forecast period. The major factors driving the growth of the market in this region include high technological investments in maintenance, and presence of large number of key players in the region. However, Asia-Pacific is expected to witness the highest growth, owing to growing manufacturing and oil & gas industry, and increase in ICT spending across the developing countries such as China, India, and Japan, in the region.
Key Findings of the Predictive Maintenance Market:
1. Based on component, the software segment led the predictive maintenance market size in terms of revenue in 2018.
2. By technique, the vibration monitoring accounted for the highest predictive maintenance market share in 2018.
3. Based on region, North America generated the highest revenue in 2018.
4. Depending on industry vertical, the healthcare segment is anticipated to exhibit substantial growth during the forecast period.
5. Based on region, Asia-Pacific is anticipated to exhibit substantial growth during the forecast period.