Predictive Maintenance in the Energy Market Size
| Study Period | 2019 - 2029 |
| Market Size (2024) | USD 1.79 Billion |
| Market Size (2029) | USD 5.62 Billion |
| CAGR (2024 - 2029) | 25.77 % |
| Fastest Growing Market | Asia-Pacific |
| Largest Market | North America |
| Market Concentration | Medium |
Major Players
*Disclaimer: Major Players sorted in no particular order |
Predictive Maintenance in the Energy Market Analysis
The Predictive Maintenance in the Energy Market size is estimated at USD 1.79 billion in 2024, and is expected to reach USD 5.62 billion by 2029, growing at a CAGR of 25.77% during the forecast period (2024-2029).
- The predictive maintenance (PdM) platform has recently gained market traction. PdM solutions are integrated with new or existing machinery infrastructure to assess machine health and detect signs of impending failure. PdM integration ensures return on investment (ROI) and enables organizations to meet and exceed sustainability goals by enabling global remote machine monitoring.
- Predictive maintenance is significantly assisting the energy industry in improving asset efficiency. Emerging technologies such as big data analytics, the Internet of Things (IoT), and cloud data storage enable industrial equipment and sensors to send condition-based data to a centralized server, making fault detection more practical and direct. The increase in uptime, lower maintenance costs, unexpected failures, and spare part inventory have propelled and flourished the market simultaneously. Furthermore, reducing repair and overhaul times is critical for the predictive maintenance market's growth.
- The majority of energy companies are asset-intensive businesses. It takes time and effort to ensure that these resources work correctly to provide energy to consumers. Machine learning techniques, such as decision trees, can be used to optimize the operation of the equipment and, by extension, the entire system. Similarly, comparable algorithms can automate the transformation of preventative maintenance programs into predictive ones. It also allows for marginal pricing, time shifting, and asset utilization, allowing energy to be generated and delivered.
- Predictive maintenance services and solutions send out an alert before the machine fails. Integrating business information, sensor data, and enterprise asset management (EAM) systems allow for the rapid transition from reactive to predictive maintenance services and solutions.
- However, factors such as high installation costs, environmental concerns, rising operating costs, rising consumer expectations, and data misinterpretation leading to false requests hinder predictive maintenance market growth. Because of the growing need for better insights into usage and performance patterns to help make better decisions, these challenges increase the adoption rate of various analytics tools.
- COVID-19 significantly impacted the market. The global economic slowdown had both positive and negative consequences for the market. For example, the drop in energy consumption was caused by the lockdowns, which hurt the market. However, due to a lack of personnel and a disrupted supply chain during the outbreak, companies operating in the industry attempted to keep the machinery running in good condition.
Predictive Maintenance in the Energy Market Trends
Solutions Segment is Anticipated to Witness Significant Growth
- In the energy sector, there has been an increase in demand for customized industrial predictive maintenance solutions, primarily for remote monitoring operations. Big data has also played an essential role in analyzing processes, assets, and heavy equipment.
- Several vendors, including SAP, IBM, and Microsoft, are active in the market, offering customized predictive maintenance solutions and services based on the needs of organizations. These solutions can help organizations protect their critical equipment and gain a competitive advantage in productivity.
- Artificial intelligence (AI) and machine learning (ML) enable organizations to gain complete visibility of their operations and generate insights that can aid in the resolution of some of the industry's most disruptive challenges. Because of the volume of big data generated by energy sector companies, forward-thinking businesses invest in monitoring and predictive analytics tools that help leverage this data to its full potential. According to Gartner, 40% of new monitoring and control systems in this sector will use Internet of Things (IoT) to enable intelligent operations by the forecasted period.
- Due to the depletion of coal resources, the power generation industry is shifting away from coal and toward solar and wind energy. Because of changing climatic conditions, most countries strictly regulate coal power plants. As electricity consumption rises, developing countries invest in advanced technologies and equipment to expand their production capacities.
- The deployment of predictive maintenance solutions is expected to empower end users to increase productivity while minimizing failures in the power generation industry by maximizing innovative maintenance activities. The power generation industry in the Asia-Pacific developing countries requires higher efficiency, better control, and faster monitoring to reduce the likelihood of operational failure.
- Investments in renewable energy generation, particularly wind turbines, offshore wind farms, and solar farms, have fueled the predictive maintenance solutions market growth in countries such as China and India.
North America to Occupy a Significant Market Share
- The predictive maintenance in the energy market is dominated by North America, followed by Europe. This is due to underlying factors such as the existence of many service providers, technological advancements, and increased knowledge of preventative maintenance. The growing emphasis on research & development (R&D) for technological advances in developed economies such as Canada and the United States has fueled demand for predictive maintenance solutions throughout the region. According to the United States Energy Information Administration (US EIA), the total energy consumption rate is expected to rise by 5% between 2020 and 2040.
- Businesses must provide energy efficiency and reduce downtime to remain profitable. This drives the data analytics market in utilities and energy. Rising environmental concerns and increased investments in sustainable energy will impact market growth.
- Other factors driving market growth include increased investment in artificial intelligence (AI) and machine learning (ML) to reduce asset downtime and maintenance costs, adoption of the Internet of things (IoT), the need to extend the overall lifespan of machinery and equipment, declining sensor prices, advancements in sensor technology, and the evolution of high-speed networking technologies. Furthermore, regulatory compliance has been a significant driver of the Internet of things (IoT) technology adoption in the United States. The passage of the Energy Act (EA) in the United States has sped up efforts to track sustainable energy consumption.
- The energy industry, one of the largest in the United States, is attracting significant investment. For example, according to Bloomberg New Energy Finance (BNEF), the United States is expected to invest approximately USD 7,00,000 million in renewable energy capacity over the next 20 years. These factors are expected to boost the growth of the predictive maintenance market.
- The energy sector remains a target for deal activity as environmental, social, and governance (ESG) strategies are strengthened. General investor interest remains high, although macroeconomic pressures could pose various valuation challenges for North American energy, power, and utility companies. For instance, J.P. Morgan paid USD 7.8 billion (USD 7,800 million) for South Jersey Industries. Similarly, ArcLight Clean Energy Transition Corp paid USD 1.5 billion (USD 1,500 million) to acquire OPAL Fuels LLC. This boosts the growth of predictive maintenance in North America.
Predictive Maintenance in the Energy Industry Overview
Numerous domestic and international firms make predictive maintenance in the energy market extremely competitive. The market is moderately concentrated, with significant players expanding their market dominance through strategies such as product innovation and mergers and acquisitions. IBM Corporation, SAP SE, Robert Bosch GmbH, and Siemens AG are some of the market's major players.
In June 2022, Siemens acquired Senseye, which provides industrial companies with predictive maintenance and asset intelligence. With the acquisition of Senseye, Siemens expanded its portfolio in innovative predictive maintenance and asset intelligence. Senseye is a manufacturer and industrial company that offers outcome-oriented predictive maintenance solutions. The predictive maintenance solution from Senseye allows for a 50% reduction in unplanned machine downtime and a 30% increase in maintenance staff productivity.
In May 2022, Hitachi Ltd. launched Lumada Inspection Insights, developed by Hitachi Energy and Hitachi Vantara, to help businesses automate asset inspection and advance sustainability goals. The new approach employs artificial intelligence (AI) and machine learning (ML) to evaluate resources, hazards, and various image types to address multiple reasons for failure.
Moreover, in January 2022, IBM announced the acquisition of Envizi, a data and analytics software provider for environmental performance management. This acquisition expands IBM's growing investments in artificial intelligence (AI)-powered software, such as IBM Maximo asset management solutions, IBM Environmental Intelligence Suite, and IBM Sterling supply chain solutions, to assist organizations in creating more resilient and sustainable operations and supply chains.
Furthermore, the acquisition broadens the company's product and service offerings. With rising demand for cloud-based services, IBM Cloud's broad range of services and expertise assist the world's smarter businesses to transform their processes, assimilate new technologies and capabilities, and pivot quickly to new market opportunities.
Predictive Maintenance in the Energy Market Leaders
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IBM Corporation
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SAP SE
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Siemens AG
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Intel Corporation
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Robert Bosch GmbH
*Disclaimer: Major Players sorted in no particular order
Predictive Maintenance in the Energy Market News
- September 2022: Electricity Growth and Use in Developing Economies Atlas AI, a predictive analytics platform, partnered with the Rockefeller Foundation, a US-based energy research organization, to assist Sub-Saharan African countries such as Kenya, Rwanda, Uganda, and Nigeria in addressing the impending green infrastructure investment gap and accelerating climate action initiatives through the use of satellite data and machine learning (ML) technologies.
- June 2022: Hinduja Tech, an e-mobility engineering and digital services company, entered the Internet of things (IoT) market with Senseye, which provides artificial intelligence (AI) - powered solutions for machine reliability and predictive maintenance. This platform predicts machine failure to improve care by integrating digital services such as end-to-end SAP automotive solutions and manufacturing and plant engineering experience.
- February 2022: The European Union announced plans to invest EUR 1.6 billion (USD 1,690 million) in Morocco's green energy sector to promote green and digital transition. Predictive maintenance solutions are thus expected to gain popularity among consumers.
Predictive Maintenance in the Energy Market Report - Table of Contents
1. INTRODUCTION
1.1 Study Assumptions and Market Definition
1.2 Scope of the Study
2. RESEARCH METHODOLOGY
3. EXECUTIVE SUMMARY
4. MARKET DYNAMICS
4.1 Market Overview
4.2 Market Drivers
4.2.1 Increasing Investments in the Energy Sector
4.2.2 Increasing Adoption of Automation
4.3 Market Challenges
4.3.1 Higher Deployment Cost
4.4 Industry Value Chain Analysis
4.5 Industry Attractiveness - Porter's Five Forces Analysis
4.5.1 Threat of New Entrants
4.5.2 Bargaining Power of Buyers
4.5.3 Bargaining Power of Suppliers
4.5.4 Threat of Substitute Products
4.5.5 Intensity of Competitive Rivalry
4.6 Assessment of COVID-19 impact on the Market
5. MARKET SEGMENTATION
5.1 By Offering
5.1.1 Solutions
5.1.2 Services
5.2 By Deployment Model
5.2.1 On-premise
5.2.2 Cloud
5.3 By Region
5.3.1 North America
5.3.2 Europe
5.3.3 Asia-Pacific
5.3.4 Latin America
5.3.5 Middle East & Africa
6. COMPETITIVE LANDSCAPE
6.1 Company Profiles
6.1.1 IBM Corporation
6.1.2 SAP SE
6.1.3 Siemens AG
6.1.4 Intel Corporation
6.1.5 Robert Bosch GmbH
6.1.6 Accenture PLC
6.1.7 ABB Ltd
6.1.8 Schneider Electric
6.1.9 Banner Engineering Corp.
6.1.10 GE Automation & Control
- *List Not Exhaustive
7. INVESTMENT ANALYSIS
8. MARKET OPPORTUNITIES AND FUTURE TRENDS
Predictive Maintenance in the Energy Industry Segmentation
Predictive Maintenance (PdM) is a technique that uses data analysis tools and techniques to detect anomalies in operation and potential defects in equipment and processes so that they can be fixed before they fail. Predictive maintenance allows the maintenance frequency to be as low as possible to avoid unplanned reactive maintenance while avoiding the costs associated with performing too much preventive maintenance.
Predictive maintenance in the energy market is segmented by offering (solution and services), deployment model (on-premise and cloud), and geography (North America, Europe, Asia-pacific, Middle East & Africa, and Latin America).
The market sizes and forecasts are provided in terms of value (USD million) for all the above segments.
| By Offering | |
| Solutions | |
| Services |
| By Deployment Model | |
| On-premise | |
| Cloud |
| By Region | |
| North America | |
| Europe | |
| Asia-Pacific | |
| Latin America | |
| Middle East & Africa |
Predictive Maintenance in the Energy Market Research FAQs
How big is the Predictive Maintenance in the Energy Market?
The Predictive Maintenance in the Energy Market size is expected to reach USD 1.79 billion in 2024 and grow at a CAGR of 25.77% to reach USD 5.62 billion by 2029.
What is the current Predictive Maintenance in the Energy Market size?
In 2024, the Predictive Maintenance in the Energy Market size is expected to reach USD 1.79 billion.
Who are the key players in Predictive Maintenance in the Energy Market?
IBM Corporation, SAP SE, Siemens AG, Intel Corporation and Robert Bosch GmbH are the major companies operating in the Predictive Maintenance in the Energy Market.
Which is the fastest growing region in Predictive Maintenance in the Energy Market?
Asia-Pacific is estimated to grow at the highest CAGR over the forecast period (2024-2029).
Which region has the biggest share in Predictive Maintenance in the Energy Market?
In 2024, the North America accounts for the largest market share in Predictive Maintenance in the Energy Market.
What years does this Predictive Maintenance in the Energy Market cover, and what was the market size in 2023?
In 2023, the Predictive Maintenance in the Energy Market size was estimated at USD 1.42 billion. The report covers the Predictive Maintenance in the Energy Market historical market size for years: 2019, 2020, 2021, 2022 and 2023. The report also forecasts the Predictive Maintenance in the Energy Market size for years: 2024, 2025, 2026, 2027, 2028 and 2029.
Predictive Maintenance in the Energy Industry Report
Statistics for the 2024 Predictive Maintenance in the Energy market share, size and revenue growth rate, created by Mordor Intelligenceā¢ Industry Reports. Predictive Maintenance in the Energy analysis includes a market forecast outlook to 2029 and historical overview. Get a sample of this industry analysis as a free report PDF download.
Predictive Maintenance in the Energy Market Report Snapshots
