Global predictive maintenance market is expected to experience substantial growth in revenue, increasing from US$ 5,934.2 million in 2022 to US$ 60,363.8 million by 2031, with a growth rate of 29.4% CAGR during the forecast period of 2023-2031.
Predictive maintenance utilizes real-time asset data collected through sensors, historical performance data, and advanced analytics to predict asset failure. This method evaluates the condition of equipment by periodically performing offline or continuously monitoring online equipment conditions. Advanced predictive maintenance techniques incorporate cutting-edge technologies such as machine learning and artificial intelligence (AI) to provide better results. Predictive maintenance can be applied in different industries, including manufacturing, healthcare, and transportation, where technology-driven systems are essential for efficient operation.
The predictive maintenance market’s significant drivers include rising urbanization, rampant digitalization, and increasing demand for reduced operation and maintenance costs. As urbanization continues to rise, consumer preferences are shifting towards technology.
Consequently, businesses are increasingly seeking operations with zero errors and aiming for less downtime, more work. Predictive maintenance, which relies on sensors to identify the need for maintenance, provides an advantage. Sensors are more accurate than human senses, detect internal wear that cannot be directly observed, and can inspect dangerous or inaccessible areas without shutting down equipment. Additionally, predictive maintenance allows companies in the global predictive maintenance market to trim operating costs as businesses can make operational predictions up to 20 times faster and more accurately than threshold-based surveillance systems. AI and IoT-based predictive maintenance technologies help enterprises predict equipment failures in advance.
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Market Dynamics
Drivers
Driver 1: Increasing Adoption of Industrial Automation
With the increasing demand for operational efficiency and cost savings, industrial automation is becoming increasingly popular. Industrial automation systems are designed to reduce manual intervention and provide real-time data on equipment performance. Predictive maintenance is an integral part of industrial automation, as it allows businesses to detect and address equipment failures before they occur. The rising adoption of industrial automation is therefore driving the growth of the predictive maintenance market.
Driver 2: Advancements in machine learning and AI technologies
The advancements in machine learning and AI technologies have opened up new opportunities for the predictive maintenance market. Predictive maintenance solutions powered by AI and machine learning algorithms can help organizations detect anomalies in real-time and predict equipment failures accurately. These solutions can also analyze large volumes of data and provide actionable insights to optimize equipment performance.
Restraints
Restraint 1: High Initial Investment
One of the major restraints of the predictive maintenance market is the high initial investment required to implement the technology. Predictive maintenance solutions often require specialized sensors, data analysis software, and other equipment, which can be costly. Additionally, businesses may need to train their employees or hire specialized personnel to manage the system, which can add to the cost.
Restraint 2: Limited Availability of Skilled Personnel
Another challenge in the predictive maintenance market is the limited availability of skilled personnel. Predictive maintenance solutions require specialized knowledge and expertise to implement and manage effectively. However, there is a shortage of skilled personnel with this knowledge, which can limit the adoption of predictive maintenance solutions.
Trend: Integration of IoT and Cloud-Based Solutions
The integration of IoT and cloud-based solutions is a growing trend in the predictive maintenance market. IoT-enabled devices can collect real-time data on equipment performance and transmit it to the cloud for analysis. Cloud-based solutions can then analyze the data and provide insights on equipment health and potential failures. This integration allows businesses to leverage the power of real-time data analytics and optimize their predictive maintenance strategies.
Segmental Analysis of Global Predictive Maintenance Market Report
By Component:
The solutions segment holds the highest share in the Global Predictive Maintenance Market in 2022 and is expected to continue its dominance over the forecast period. This is due to the increasing importance of solutions in identifying the cause of possible faults or failures of equipment before they occur. Predictive maintenance solutions offer real-time monitoring, data analysis, and predictive insights, allowing companies to proactively schedule maintenance and avoid costly downtime. Additionally, solutions are becoming more sophisticated, incorporating advanced technologies such as AI and machine learning to provide even more accurate predictions.
By Deployment Mode:
The on-premises segment has the highest share in the global predictive maintenance market in 2022. However, the cloud segment is expected to have the highest CAGR over the forecast period. Cloud-based predictive maintenance solutions offer significant business efficiencies, cost benefits, and competitive advantages. Cloud deployment eliminates the need for businesses to invest in costly hardware and infrastructure, while also providing scalability and flexibility to meet changing business needs. Moreover, cloud-based solutions can offer real-time access to data and analytics, allowing businesses to make informed decisions quickly.
By Technology:
The vibration monitoring technology held largest share of the global predictive maintenance market in 2022 as it helps to assess machine condition, evaluate and diagnose identified assets, and take proper action at the appropriate moment. Vibration monitoring uses sensors to detect and measure the vibrations produced by machinery, allowing businesses to identify any abnormal behavior or potential faults. This technology is widely used across various industries, including manufacturing, energy, and transportation, among others. Moreover, the technology is evolving, and the incorporation of AI and machine learning is expected to further enhance its capabilities.
By Organization Size:
Large enterprises dominated the global predictive maintenance market in 2022 and is expected to register the highest CAGR over the forecast period. Predictive maintenance provides easy access to specific information on product and usage habits and has the ability to provide cost-cutting solutions that reduce the need for additional maintenance. Large enterprises have a higher volume of assets to maintain, making predictive maintenance an ideal solution for reducing maintenance costs and minimizing downtime. Moreover, large enterprises can afford to invest in advanced predictive maintenance solutions, making them better equipped to manage their assets effectively.
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By Industry:
The manufacturing industry is the highest end-user of predictive maintenance market. Predictive maintenance is widely used in the manufacturing industry to minimize downtime, reduce maintenance costs, and improve overall productivity. The energy and utilities segment has the highest CAGR over the forecast period. This is due to the increasing demand for energy and utilities, which has led to the development of predictive maintenance solutions for assets such as wind turbines, solar panels, and power grids. Predictive maintenance can help energy and utility companies ensure the reliability of their assets and minimize the risk of downtime.
Regional Analysis
North America is currently the largest market for predictive maintenance, owing to the presence of a large number of companies with advanced technological capabilities and strong financial resources. The region is home to some of the major players in the predictive maintenance market such as IBM Corporation, General Electric, and Honeywell International, among others. Additionally, the adoption of advanced technologies such as machine learning, artificial intelligence, and IoT has been relatively high in North America, contributing to the growth of the predictive maintenance market in the region.
On the other hand, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, owing to the rapid digital transformation, increasing demand for automation in various industries, and the presence of a large number of small and medium-sized enterprises. Countries such as China, Japan, South Korea, and India are major contributors to the growth of the predictive maintenance market in the region. The growth in the manufacturing sector in these countries is driving the demand for predictive maintenance solutions, as it helps to optimize operational efficiency and reduce maintenance costs.
Moreover, the increasing expenditure by the public and private sectors in the region to improve their maintenance solutions is further driving the demand for predictive maintenance solutions. Governments in countries such as China and India are investing heavily in Industry 4.0 initiatives, which are expected to drive the growth of the predictive maintenance market in the region. The Asia Pacific region is also witnessing a rise in the adoption of cloud-based predictive maintenance solutions, owing to the benefits such as cost-effectiveness, scalability, and ease of deployment.
Competitive Landscape
The global predictive maintenance market is highly competitive, with several established players such as IBM, SAP, SIEMENS, Microsoft, GE, and Intel, among others. These players are continuously focusing on research and development activities to launch innovative products and solutions, which can cater to the diverse needs of end-users. Moreover, the leading companies are adopting various competitive strategies such as mergers and acquisitions to strengthen their market position and expand their geographical boundaries.
For instance, in August 2021, IBM acquired Turbonomic, a leading provider of Application Resource Management (ARM) and Network Performance Management (NPM) software. The acquisition is expected to help IBM to deliver a comprehensive AIOps solution that can help customers optimize their IT environments and deliver better business outcomes.
Similarly, in June 2021, Microsoft announced the acquisition of ReFirm Labs, a provider of IoT security solutions. The acquisition is expected to help Microsoft to enhance its Azure Defender for IoT offering, which is a cloud-based security solution that provides comprehensive protection to IoT devices.
List of Key Companies Profiled:
Segmentation Overview
The following are the different segments of the Global Predictive Maintenance Market:
By Component:
By Deployment Mode:
By Technology:
By Organization Size:
By Industry:
By Region:
| Report Attribute | Details |
|---|---|
| Market Size Value in 2022 | US$ 5,934.2 Mn |
| Expected Revenue in 2031 | US$ 60,363.83 Mn |
| Historic Data | 2018-2021 |
| Base Year | 2022 |
| Forecast Period | 2023-2031 |
| Unit | Value (USD Mn) |
| CAGR | 29.4% |
| Segments covered | By Component, By Deployment Mode, By Technology, By Organization Size, By Region |
| Key Companies | Fujitsu Limited, Hitachi, Ltd., Toshiba Corporation, Mitsubishi Electric Corporation, Google Llc, IBM Corporation, Microsoft Corporation, Oracle Corporation, SAP Se, Software Ag, Onyx Insight, Amazon Web Services, Inc., SAS Institute, Hakunamatata Solutions, Other Prominent Players |
| Customization Scope | Get your customized report as per your preference. Ask for customization |
Predictive maintenance helps in scheduling the maintenance work and performing it before an asset is expected to fail with minimal downtime.
Predictive maintenance is used in various industries such as manufacturing, healthcare, energy & utilities, oil & gas and transport.
Predictive maintenance relies on sensors to identify the need for maintenance. Not only are sensors more accurate than human senses, but they can detect internal wear that cannot be directly observed, is too dangerous for humans to inspect, or would otherwise require equipment to be shut down and opened up.
The Global Predictive Maintenance Market was valued at US$ 5,934.2 Mn in 2022.
Global Predictive Maintenance Market is projected to expand at a CAGR of 29.4% over the forecast period.
The market is majorly driven by factors such as rising urbanization, rampant digitalization and increasing demand to decrease operation & maintenance cost.
Lack of skilled workforce is hindering the market growth.
Vibration monitoring segment has the highest share in the Global Predictive Maintenance Market.
Large enterprises have highest share of the predictive maintenance market in 2022.
Manufacturing industry is the highest end user in the predictive maintenance market.
North America dominates Global Predictive Maintenance Market in 2022.
Asia Pacific has the highest CAGR in the market over the forecast period.
The key players in the global predictive maintenance market are IBM, SAP, SIEMENS, Microsoft, GE and Intel among others.
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