Global Predictive Maintenance Market to Reach $13.9 Billion by 2026

Predictive maintenance (PdM) is an advanced form of maintenance that offers benefits over and above condition-based maintenance (CbM) which utilizes real-time sensor measurements to identify if a machine or its components have reached a point of failure and then schedule maintenance. As a subset of asset management, predictive maintenance (PdM) is forecast to benefit from the growing manufacturer investments in asset management systems and corporate wide implementation of asset management regimes. Key factors driving demand for predictive analytics in smart factories include the growing need to drive value from big data, growing power of computing, increasing role of data in shaping equipment management decisions, growing cloud solution deployments, and better understanding of value proposition offered by the technology. In the post-COVID-19 scenario, anticipated resurgence in core end-use sectors especially in the developing Asia-Pacific and Latin America nations, would renew the demand for production machines in various end-use sectors. Increased activity in automotive, aerospace & defense, shipbuilding and electrical/electronics domains would in particular enhance the demand for sophisticated machine tools for use in the manufacture of machine components and in assembly of finished products. Furthermore, resurgence in oil & gas exploration and drilling activities, and mining & mineral sector activities would extend new growth opportunities for machine tools market. This in turn is expected to benefit growth prospects of predictive maintenance.

Amid the COVID-19 crisis, the global market for Predictive Maintenance estimated at US$3.8 Billion in the year 2020, is projected to reach a revised size of US$13.9 Billion by 2026, growing at a CAGR of 23.3% over the analysis period. Cloud, one of the segments analyzed in the report, is projected to grow at a 27.7% CAGR to reach US$8.4 Billion by the end of the analysis period. After a thorough analysis of the business implications of the pandemic and its induced economic crisis, growth in the On-Premise segment is readjusted to a revised 19.7% CAGR for the next 7-year period. This segment currently accounts for a 59.9% share of the global Predictive Maintenance market. Cloud-based predictive maintenance solutions harness the combined power of cloud computing and machine learning to allow for more complex processing and richer machine learning models. Machine learning accesses the historical data stored in the cloud and processes real-time data gathered through IoT sensors at the edge to seek failure patterns.

The U.S. Market is Estimated at $1.3 Billion in 2021, While China is Forecast to Reach $2.3 Billion by 2026

The Predictive Maintenance market in the U.S. is estimated at US$1.3 Billion in the year 2021. The country currently accounts for a 29.71% share in the global market. China, the world`s second largest economy, is forecast to reach an estimated market size of US$2.3 Billion in the year 2026 trailing a CAGR of 29.2% through the analysis period. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at 19.7% and 21.6% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 20.7% CAGR while Rest of European market (as defined in the study) will reach US$2.7 Billion by the end of the analysis period. Inclination towards automation remains more visible and profound in the developed regions including North America, Europe and Japan. Driven by manufacturers` appetite for sophisticated technologies that comfortably steer their manufacturing production towards efficiency and profitability, shift towards automation presents lucrative prospects for predictive maintenance in developed regions.