予測保全市場規模は、2023年の308億米ドルから2030年には増加する見込み

The global predictive maintenance market is undergoing rapid transformation due to the growing need for technological innovation and operational efficiency. The predictive maintenance market is valued at approximately USD 4.6 billion in 2023 and is projected to reach USD 30.8 billion by 2030 , expanding at a staggering CAGR of 31.2% during the forecast period  . Organizations across various industries are increasingly adopting predictive maintenance to reduce equipment failures, minimize downtime, and optimize maintenance strategies.

For more industry insights, read: https://www.fairfieldmarketresearch.com/report/predictive-maintenance-market

Why predictive maintenance is gaining attention

Predictive maintenance is reshaping how industries approach asset management. Unlike traditional time-based maintenance, this approach leverages IoT sensors, machine learning, and real-time data analytics to predict equipment failure before it happens. By proactively addressing potential issues, companies can reduce costs, improve asset reliability, and ensure uninterrupted operations.

Key drivers of adoption include:

• New technologies  enable real-time data acquisition and analysis .
• Condition monitoring systems  that help detect anomalies quickly .
• There is a growing need to reduce costs  associated with downtime and reactive repairs .

This ability to balance efficiency and reliability has made predictive maintenance extremely important across sectors such as manufacturing, energy, automotive and transportation.

Key Market Insights

Deployment Model

On-premise deployments continue to dominate due to industries requiring strict data management and regulatory compliance , and they also offer seamless integration with legacy systems, making them the preferred choice in established sectors.

Solution

Our integrated solutions lead the market by providing end-to-end capabilities, from data collection to decision-making, which streamline maintenance processes, reduce operational complexity, and deliver significant cost savings.

application

Manufacturing remains the largest application segment due to its high reliance on industrial machinery. Predictive maintenance can help manufacturers reduce equipment downtime , improve productivity, and align with Industry 4.0 initiatives.

Regional Dynamics

North America : Market leader

North America accounts for the largest revenue share, driven by an advanced industrial ecosystem, strict compliance standards, and widespread adoption of technologies such as AI and IoT. Industries such as automotive, aerospace, and energy rely heavily on predictive maintenance to ensure operational continuity.

Asia Pacific : The fastest growing market

Asia Pacific is expected to register the highest CAGR through 2030 , driven by the rapid industrialization of China and India . The expansion of the automotive sector and manufacturing industry, along with strong government-backed digitalization initiatives, are accelerating adoption across the region.

Issues to be addressed

Despite strong growth prospects, the market faces hurdles. A shortage of skilled professionals with expertise in IoT, AI, and data analytics is limiting the pace of adoption. Additionally, data ownership and privacy issues complicate the use of collected information, highlighting the importance of a robust governance framework.

Key trends and opportunities

• IoT sensors : These are important for monitoring temperature, vibration, and performance metrics in real time.
• Edge computing : Processing data closer to the source reduces latency and enables faster responses.
• Cloud computing : Facilitates scalable data storage and advanced analytics for predictive insights.

Taken together, these technologies enable companies to create a smarter, more reliable, and cost-effective maintenance ecosystem.

Competitive environment

The market is highly competitive, with global technology giants and industry solution providers leading the innovation. Key players include:

• IBM
• sap
• Microsoft
• General Electric
• Siemens
• Honeywell
• Schneider Electric
• ABB
• Bosch
• Rockwell Automation
• PTC
• Oracle
• SAS
• Import
• artificial intelligence

These companies are focused on integrating AI, IoT, and cloud-based solutions into their predictive maintenance platforms to deliver enhanced capabilities and expand their global reach.

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