Predictive Maintenance Market by Offering (Software, Hardware), Deployment Mode, Organization Size, Technology (IoT, AI & ML), Application (Oil Analysis, Temperature Monitoring), End-use Industry, and Geography

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Predictive Maintenance Market by Offering (Software, Hardware), Deployment Mode, Organization Size, Technology (IoT, AI & ML), Application (Oil Analysis, Temperature Monitoring), End-use Industry, and Geography-Global Forecast to 2031.

The research report titled 'Predictive Maintenance Market by Offering (Software, Hardware), Deployment Mode, Organization Size, Technology (IoT, AI & ML), Application (Oil Analysis, Temperature Monitoring), End-use Industry, and Geography-Global Forecast to 2031' provides an in-depth analysis of the predictive maintenance market in five major geographies and emphasizes on the current market trends, market sizes, market shares, recent developments, and forecasts till 2031.

The predictive maintenance market is projected to reach $79.1 billion by 2031, at a CAGR of 30.9% from 2024-2031.

The growth of the predictive maintenance market is driven by the growing need to lower maintenance costs and improve asset performance and the increasing adoption of predictive maintenance in complex infrastructure systems. However, the data privacy and security concerns restrain the growth of this market. Furthermore, the expansion of predictive maintenance solutions in healthcare devices and navigation systems is expected to generate growth opportunities for the players operating in this market. However, the lack of a skilled workforce is a major challenge impacting market growth. Additionally, the integration of digital twins and augmented reality (AR) is the latest trend in the market.

The predictive maintenance market is segmented by offering (software, hardware [sensors {vibration sensors, temperature sensors, pressure sensors, acoustic sensors, ultrasonic sensors, and other sensors}, data acquisition systems, connectivity devices, and other hardware], and services [professional services and managed services]), deployment mode (cloud-based deployments and on-premise deployments), organization size (large enterprises and small & medium-sized enterprises), technology (internet of things (IoT), AI and machine learning, cloud connectivity, modern database and ERP, advanced analytics, and digital twins), application (vibration analysis, oil analysis, acoustics monitoring, motor circuit analysis, infrared thermography, temperature monitoring, and other applications), end-use industry (manufacturing, energy & utilities, automotive & transportation, aerospace & defense, oil & gas, healthcare, construction & mining, IT & telecom, and other end-use industries), and geography. The study also evaluates industry competitors and analyses the market at the country and regional levels.

Based on offering, the predictive maintenance market is segmented into software, hardware, and services. In 2024, the software segment is expected to account for the largest share of above 81.0% of the predictive maintenance market. The segment's large market share is attributed to the growing need to lower maintenance costs, the growing adoption of predictive maintenance software to ensure compliance by providing documentation of maintenance activities and adherence to maintenance schedules, and the increasing use of predictive maintenance to provide valuable insights into equipment performance, trends, and patterns for decision making and optimization of maintenance strategies.

However, the services segment is expected to register the highest CAGR during the forecast period. This segment's growth is attributed to the adoption of predictive maintenance services to analyze equipment data and identify potential issues, the growing need to lower overall maintenance costs, and the growing integration of IoT, AI, and Ml in predictive maintenance to provide real-time monitoring of equipment performance.

Based on deployment mode, the predictive maintenance market is segmented into cloud-based deployments and on-premise deployments. In 2024, the cloud-based deployments segment is expected to account for the larger share of above 58.0% of the predictive maintenance market. The segment's large market share is attributed to the growing adoption of cloud-based solutions to scale up or down based on the needs of the business, the increasing use of cloud-based predictive maintenance to analyze large volumes of data in real time and leverage the scalability of cloud computing resources; and the cloud-based platforms offering advanced analytics capabilities, including machine learning and predictive modeling. Also, this segment is expected to register the highest CAGR during the forecast period.

Based on organization size, the predictive maintenance market is segmented into large enterprises and small & medium-sized enterprises. In 2024, the large enterprises segment is expected to account for the larger share of above 74.0% of the predictive maintenance market. The segment's large market share is attributed to the growing adoption of predictive maintenance to avoid costly unplanned downtime and repairs. Predictive maintenance is used in large enterprises to monitor equipment health in real time, identify performance degradation, and take proactive measures to maintain optimal operating conditions, further contributing to the segment's large share.

However, the small & medium-sized enterprises segment is expected to register the highest CAGR during the forecast period. The growth of this segment is attributed to the growing adoption of predictive maintenance to reduce the burden on maintenance staff by automating the monitoring and analysis of equipment health. Predictive maintenance helps SMEs meet regulatory requirements by ensuring that equipment is properly maintained and operating within prescribed limits. The rising use of predictive maintenance in SMEs to enhance their operational efficiency, mitigate risks, and position themselves for long-term sustainability contributes to the segment's growth.

Based on technology, the predictive maintenance market is segmented into the Internet of Things (IoT), AI and machine learning, cloud connectivity, modern database and ERP, advanced analytics, and digital twins. In 2024, the IoT segment is expected to account for the largest share of the predictive maintenance market. The segment's large market share is attributed to the growing use of IoT-based predictive maintenance to predict equipment failures and improve technician efficiency by providing real-time information about equipment performance.

However, the AI and machine learning segment is expected to register the highest CAGR during the forecast period. This segment's growth is attributed to the growing adoption of AI and ML in predictive maintenance for real-time analytics. AI-based predictive maintenance contributes to energy savings and reduces the environmental footprint of industrial operations. The AI and ML algorithms analyze large volumes of data from sensors, equipment logs, and other sources to identify patterns and trends, contributing to the segment's high growth.

Based on application, the predictive maintenance market is segmented into vibration analysis, oil analysis, acoustics monitoring, motor circuit analysis, infrared thermography, temperature monitoring, and other applications. In 2024, the temperature monitoring segment is expected to account for the largest share of above 26.0% of the predictive maintenance market. The segment's large market share is attributed to the growing adoption of predictive maintenance for equipment failures or malfunctions for early intervention and the rising use of predictive maintenance to provide notification to maintenance personnel for investigation and preventive action.

However, the vibration analysis segment is expected to register the highest CAGR during the forecast period. The segment's growth is attributed to the growing adoption of predictive maintenance for vibration analysis to detect, measure, and analyze vibration in rotating parts of machinery; the rising use of predictive maintenance to control downtime and maintenance processes and enhance product quality with machinery running at rated tolerances more consistently.

Based on end-use industry, the predictive maintenance market is segmented into manufacturing, energy & utilities, automotive & transportation, aerospace & defense, oil & gas, healthcare, construction & mining, IT & telecom, and other end-use industries. In 2024, the manufacturing segment is expected to account for the largest share of above 30.0% of the predictive maintenance market. The segment's large market share is attributed to the growing adoption of predictive maintenance to avoid costs associated with unscheduled downtime and the increasing adoption of Industry 4.0 for manufacturing to increase production efficiency and reduce costs.

However, the healthcare segment is expected to register the highest CAGR during the forecast period. This segment's growth is attributed to the growing use of IoT and telematics in healthcare facilities and the increasing use of predictive maintenance to collect data on parameters such as temperature, pressure, and electrical current of medical equipment. Additionally, predictive maintenance provides facility managers with real-time data that is used to schedule maintenance at timely intervals.

Based on geography, the predictive maintenance market is segmented into North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa. In 2024, North America is expected to account for the largest share of above 33.0% of the predictive maintenance market. North America's significant market share can be attributed to the increasing demand for predictive maintenance in the healthcare sector, the growing demand to reduce equipment failure, maintenance costs, and downtime, the rising adoption of advanced technology such as IoT, AI, and ML; and the increasing the number of industries in North America to meet demand and supply.

However, the Asia-Pacific market is expected to register the highest CAGR of above 32.0% during the forecast period. This market's growth is attributed to the growing expansion of small & medium-sized industries, the growing industrialization coupled with increasing government initiatives, the growing need to lower maintenance costs and improve asset performance, and the emergence of industry 4.0 in the manufacturing landscape in countries such as China, India, and Japan.

The key players operating in the predictive maintenance market are International Business Machines Corporation (U.S.), ABB Ltd (Switzerland), Hitachi, Ltd. (Japan), Siemens AG (Germany), Amazon Web Services, Inc. (A Subsidiary of Amazon.com, Inc.) (U.S.), Google LLC (A Subsidiary of Alphabet Inc.) (U.S.), Microsoft Corporation (U.S.), Emerson Electric Co. (U.S.), Oracle Corporation (U.S.), Splunk Inc. (A Subsidiary of Cisco Systems, Inc.) (U.S.), Axiomtek Co., Ltd. (Taiwan), Presage Insights pvt ltd (India), XMPro Inc. (U.S.), Faclon Labs Private Limited (India), and SenseGrow Inc. (U.S.).

Key Questions Answered in the Report:

What are the high-growth market segments in terms of offering, deployment mode, organization size, technology, application, and end-use industry?
What is the historical market size for the predictive maintenance market?
What are the market forecasts and estimates for 2024-2031?
What are the major drivers, restraints, opportunities, challenges, and trends in the predictive maintenance market?
Who are the major players in the predictive maintenance market, and what are their market shares?
What is the competitive landscape like?
What are the recent developments in the predictive maintenance market?
What are the different strategies adopted by major market players?
What are the trends and high-growth countries?
Who are the local emerging players in the predictive maintenance market, and how do they compete with other players?

Scope of the Report:

Predictive Maintenance Market Assessment-by Offering

Software
Hardware
- Sensors
Vibration Sensors
Temperature Sensors
Pressure Sensors
Acoustic Sensors
Ultrasonic Sensors
Other Sensors
- Data Acquisition Systems
- Connectivity Devices
- Other Hardware
Services
- Professional Services
- Managed Services

Predictive Maintenance Market Assessment-by Deployment Mode

Cloud-based Deployments
On-premise Deployments

Predictive Maintenance Market Assessment-by Organization Size

Large Enterprises
Small & Medium-sized Enterprises

Predictive Maintenance Market Assessment-by Technology

Internet of Things (IoT)
AI and Machine Learning
Cloud Connectivity
Modern Database and ERP
Advanced Analytics
Digital Twins

Predictive Maintenance Market Assessment-by Application

Vibration Analysis
Oil Analysis
Acoustics Monitoring
Motor Circuit Analysis
Infrared Thermography
Temperature Monitoring
Other Applications

Predictive Maintenance Market Assessment-by End-use Industry

Manufacturing
Energy & Utilities
Automotive & Transportation
Aerospace & Defense
Oil & Gas
Healthcare
Construction & Mining
IT & Telecom
Other End-use Industries

Predictive Maintenance Market Assessment-by Geography

North America
- U.S.
- Canada
Europe
- Germany
- U.K.
- France
- Italy
- Netherlands
- Spain
- Sweden
- Rest of Europe
Asia-Pacific
- Japan
- China
- India
- South Korea
- Singapore
- Australia & New Zealand
- Indonesia
- Rest of Asia-Pacific
Latin America
- Mexico
- Brazil
- Rest of Latin America
Middle East & Africa
- UAE
- Saudi Arabia
- Israel
- Rest of Middle East & Africa

Product Code: MRICT - 1041253

1. Introduction

1.1. Market Definition & Scope
1.2. Currency & Limitations
- 1.2.1. Currency
- 1.2.2. Limitations

2. Research Methodology

2.1. Research Approach
2.2. Process of Data Collection and Validation
- 2.2.1. Secondary Research
- 2.2.2. Primary Research /Interviews with Key Opinion Leaders of the Industry
2.3. Market Sizing and Forecast
- 2.3.1. Market Size Estimation Approach
- 2.3.2. Growth Forecast Approach
2.4. Assumptions for the Study

3. Executive Summary

3.1. Market Overview
3.2. Market Analysis, by Offering
3.3. Market Analysis, by Deployment Mode
3.4. Market Analysis, by Organization Size
3.5. Market Analysis, by Technology
3.6. Market Analysis, by Application
3.7. Market Analysis, by End-use Industry
3.8. Market Analysis, by Geography
3.9. Competitive Analysis

4. Market Insights

4.1. Overview
4.2. Factors Affecting Market Growth
- 4.2.1. Drivers
  - 4.2.1.1. Growing Need to Lower Maintenance Costs and Improve Asset Performance
  - 4.2.1.2. Increasing Adoption of Predictive Maintenance in Complex Infrastructure Systems
- 4.2.2. Restraints
  - 4.2.2.1. Data Privacy and Security
- 4.2.3. Opportunities
  - 4.2.3.1. Expansion of Predictive Maintenance Solutions in Healthcare Devices and Navigation Systems
- 4.2.4. Challenges
  - 4.2.4.1. Lack of Skilled Workforce
4.3. Key Trends
- 4.3.1. Integration of Digital Twins and Augmented Reality (AR)
4.4. Case Studies
- 4.4.1. Case Study A
- 4.4.2. Case Study B
- 4.4.3. Case Study C
4.5. Value Chain Analysis

5. Global Predictive Maintenance Market Assessment-by Offering

5.1. Overview
5.2. Software
5.3. Hardware
- 5.3.1. Sensors
  - 5.3.1.1. Vibration Sensors
  - 5.3.1.2. Temperature Sensors
  - 5.3.1.3. Pressure Sensors
  - 5.3.1.4. Acoustic Sensors
  - 5.3.1.5. Ultrasonic Sensors
  - 5.3.1.6. Other Sensors
- 5.3.2. Data Acquisition Systems
- 5.3.3. Connectivity Devices
- 5.3.4. Other Hardware
5.4. Services
- 5.4.1. Professional Services
- 5.4.2. Managed Services

6. Global Predictive Maintenance Market Assessment-by Deployment Mode

6.1. Overview
6.2. Cloud-based Deployments
6.3. On-premise Deployments

7. Global Predictive Maintenance Market Assessment-by Organization Size

7.1. Overview
7.2. Large Enterprises
7.3. Small & Medium-sized Enterprises

8. Global Predictive Maintenance Market Assessment-by Technology

8.1. Overview
8.2. Internet of Things (IoT)
8.3. AI and Machine Learning
8.4. Cloud Connectivity
8.5. Modern Database and ERP
8.6. Advanced Analytics
8.7. Digital Twins

9. Global Predictive Maintenance Market Assessment-by Application

9.1. Overview
9.2. Vibration Analysis
9.3. Oil Analysis
9.4. Acoustics Monitoring
9.5. Motor Circuit Analysis
9.6. Infrared Thermography
9.7. Temperature Monitoring
9.8. Other Applications

10. Global Predictive Maintenance Market Assessment-by End-use Industry

10.1. Overview
10.2. Manufacturing
10.3. Energy & Utilities
10.4. Automotive & Transportation
10.5. Aerospace & Defense
10.6. Oil & Gas
10.7. Healthcare
10.8. Construction & Mining
10.9. IT & Telecom
10.10. Other End-use Industries

11. Predictive Maintenance Market Assessment-by Geography

11.1. Overview
11.2. North America
- 11.2.1. U.S.
- 11.2.2. Canada
11.3. Europe
- 11.3.1. Germany
- 11.3.2. U.K.
- 11.3.3. France
- 11.3.4. Italy
- 11.3.5. Netherlands
- 11.3.6. Spain
- 11.3.7. Sweden
- 11.3.8. Rest of Europe
11.4. Asia-Pacific
- 11.4.1. Japan
- 11.4.2. China
- 11.4.3. India
- 11.4.4. South Korea
- 11.4.5. Singapore
- 11.4.6. Australia & New Zealand
- 11.4.7. Indonesia
- 11.4.8. Rest of Asia-Pacific
11.5. Latin America
- 11.5.1. Mexico
- 11.5.2. Brazil
- 11.5.3. Rest of Latin America
11.6. Middle East & Africa
- 11.6.1. UAE
- 11.6.2. Saudi Arabia
- 11.6.3. Israel
- 11.6.4. Rest of Middle East & Africa

12. Competition Analysis

12.1. Overview
12.2. Key Growth Strategies
12.3. Competitive Benchmarking
12.4. Competitive Dashboard
- 12.4.1. Industry Leaders
- 12.4.2. Market Differentiators
- 12.4.3. Vanguards
- 12.4.4. Emerging Companies
12.5. Market Ranking, by Key Players

13. Company Profiles (Company Overview, Financial Overview, Product Portfolio, and Strategic Developments)

13.1. International Business Machines Corporation
13.2. ABB Ltd
13.3. Hitachi, Ltd.
13.4. Siemens AG
13.5. Amazon Web Services, Inc. (A Subsidiary of Amazon.com, Inc.)
13.6. Google LLC (A Subsidiary of Alphabet Inc.)
13.7. Microsoft Corporation
13.8. Emerson Electric Co.
13.9. Oracle Corporation
13.10. Splunk Inc. (A Subsidiary of Cisco Systems, Inc.)
13.11. Axiomtek Co., Ltd.
13.12. Presage Insights pvt ltd
13.13. XMPro Inc.
13.14. Faclon Labs Private Limited
13.15. SenseGrow Inc.

(Note: SWOT analyses of the top 5 companies will be provided.)

14. Appendix

14.1. Available Customization
14.2. Related Reports