Predictive Maintenance Market Share, Size, Trends, Industry Analysis Report, By Component (Solutions, Services); By Deployment Model (Cloud, On-Premise); By Organization Size; By Industry Vertical; By Region; Segment Forecast, 2022 - 2030
Description
The global predictive maintenance market size is expected to reach USD 156.88 billion by 2034 according to a new study by Polaris Market Research. The report “Predictive Maintenance Market Share, Size, Trends, Industry Analysis Report, By Component (Solutions, Services); By Deployment Model (Cloud, On-Premise); By Organization Size; By Industry Vertical; By Region; Segment Forecast, 2025 - 2034” gives a detailed insight into current market dynamics and provides analysis on future market growth.
Prominent factors favoring the industry growth include the need for reduced cost and downtime associated with the maintenance, a rise in demand for cloud-based predictive maintenance solutions, and a huge investment in these solutions in the manufacturing sector. In addition, the upsurge in demand for remote monitoring and management of assets and business processes owing to the advent of the COVID-19 pandemic has boosted the growth of the global industry.
Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) has created lucrative growth opportunities for the industry. Businesses across the industry verticals including manufacturing, energy & utilities, transportation & logistics, and healthcare, have realized the importance of such solutions, which has further fueled the growth of the industry.
In 2024, the manufacturing segment will dominate the global market. The predictive maintenance framework helps to perform real-time detection, visualization, and alert creation, along with recommendations for solutions at different stages of the manufacturing process. The failure in critical equipment is costly to manufacturers in terms of downtime, loss of productivity as well as repairs, which in turn, is driving the growth of this segment.
On the other hand, Asia Pacific is anticipated to witness the highest growth in the global market. The region is seeing rising adoption of predictive maintenance solutions in different industry verticals. For instance, in October 2021, SMRT Trains, the Singapore-based transport operator, announced the collaboration with Bentley Systems, an American-based software development company that installed a predictive decision support system (PDSS) for North-South and East-West lines across Singapore. In addition, the SMRT Trains has also commenced its deployment of the PDSS solution on the Circle Line (CCL) across Singapore.
Market participants such as Altair, Splunk, General Electric, Google, Hitachi, IBM, Microsoft, Oracle, SAP, Schneider Electric, SAS Institute, AWS, Software AG, HPE, and Tibco Software are some of the key players operating in the global market. Players in the global market are adopting a number of strategies to expand their geographical presence, develop new applicant tracking systems, and generate novel revenue streams.
For instance, in January 2022, Aizon, an AI software provider launched Aizon Asset Health, the new asset monitoring application for biotech companies and pharmaceutical manufacturers to optimize the performance of equipment and other assets.
Polaris Market Research has segmented the predictive maintenance market report on the basis of component, deployment model, organization size, industry vertical, and region:
Predictive Maintenance, Component Outlook (Revenue - USD Billion, 2020-2034)
Solutions
Services
Predictive Maintenance, Deployment Model Outlook (Revenue - USD Billion, 2020-2034)
Cloud
On-premise
Predictive Maintenance, Organization Size Outlook (Revenue - USD Billion, 2020-2034)
Large Enterprises
Small and Medium-sized Enterprises
Predictive Maintenance, Industry Vertical Outlook (Revenue - USD Billion, 2020-2034)
Government & Defense
Manufacturing
Energy & Utilities
Transportation & Logistics
Healthcare & Life Sciences
Predictive maintenance, Regional Outlook (Revenue - USD Billion, 2020-2034)
North America
U.S.
Canada
Europe
France
Germany
UK
Italy
Spain
Netherlands
Russia
Asia Pacific
China
India
Japan
Malaysia
South Korea
Indonesia
Latin America
Mexico
Brazil
Argentina
Middle East & Africa
UAE
Saudi Arabia
Israel
South Africa
Prominent factors favoring the industry growth include the need for reduced cost and downtime associated with the maintenance, a rise in demand for cloud-based predictive maintenance solutions, and a huge investment in these solutions in the manufacturing sector. In addition, the upsurge in demand for remote monitoring and management of assets and business processes owing to the advent of the COVID-19 pandemic has boosted the growth of the global industry.
Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) has created lucrative growth opportunities for the industry. Businesses across the industry verticals including manufacturing, energy & utilities, transportation & logistics, and healthcare, have realized the importance of such solutions, which has further fueled the growth of the industry.
In 2024, the manufacturing segment will dominate the global market. The predictive maintenance framework helps to perform real-time detection, visualization, and alert creation, along with recommendations for solutions at different stages of the manufacturing process. The failure in critical equipment is costly to manufacturers in terms of downtime, loss of productivity as well as repairs, which in turn, is driving the growth of this segment.
On the other hand, Asia Pacific is anticipated to witness the highest growth in the global market. The region is seeing rising adoption of predictive maintenance solutions in different industry verticals. For instance, in October 2021, SMRT Trains, the Singapore-based transport operator, announced the collaboration with Bentley Systems, an American-based software development company that installed a predictive decision support system (PDSS) for North-South and East-West lines across Singapore. In addition, the SMRT Trains has also commenced its deployment of the PDSS solution on the Circle Line (CCL) across Singapore.
Market participants such as Altair, Splunk, General Electric, Google, Hitachi, IBM, Microsoft, Oracle, SAP, Schneider Electric, SAS Institute, AWS, Software AG, HPE, and Tibco Software are some of the key players operating in the global market. Players in the global market are adopting a number of strategies to expand their geographical presence, develop new applicant tracking systems, and generate novel revenue streams.
For instance, in January 2022, Aizon, an AI software provider launched Aizon Asset Health, the new asset monitoring application for biotech companies and pharmaceutical manufacturers to optimize the performance of equipment and other assets.
Polaris Market Research has segmented the predictive maintenance market report on the basis of component, deployment model, organization size, industry vertical, and region:
Predictive Maintenance, Component Outlook (Revenue - USD Billion, 2020-2034)
Solutions
Services
Predictive Maintenance, Deployment Model Outlook (Revenue - USD Billion, 2020-2034)
Cloud
On-premise
Predictive Maintenance, Organization Size Outlook (Revenue - USD Billion, 2020-2034)
Large Enterprises
Small and Medium-sized Enterprises
Predictive Maintenance, Industry Vertical Outlook (Revenue - USD Billion, 2020-2034)
Government & Defense
Manufacturing
Energy & Utilities
Transportation & Logistics
Healthcare & Life Sciences
Predictive maintenance, Regional Outlook (Revenue - USD Billion, 2020-2034)
North America
U.S.
Canada
Europe
France
Germany
UK
Italy
Spain
Netherlands
Russia
Asia Pacific
China
India
Japan
Malaysia
South Korea
Indonesia
Latin America
Mexico
Brazil
Argentina
Middle East & Africa
UAE
Saudi Arabia
Israel
South Africa
Table of Contents
111 Pages
- 1. Introduction
- 1.1. Report Description
- 1.1.1. Objectives of the Study
- 1.1.2. Market Scope
- 1.1.3. Assumptions
- 1.2. Stakeholders
- 2.1. Market Highlights
- 3.1. Overview
- 3.1.1. Data Mining
- 3.2. Data Sources
- 3.2.1. Primary Sources
- 3.2.2. Secondary Sources
- 4.1. Wind Turbine Market – Market Snapshot
- 4.2. Wind Turbine Market Dynamics
- 4.2.1. Drivers and Opportunities
- 4.2.1.1. Expansion of Offshore Wind Projects
- 4.2.1.2. Technological Advancements in Turbine Efficiency
- 4.2.2. Restraints and Challenges
- 4.2.2.1. High upfront costs and lengthy permitting processes
- 4.3. Porter’s Five Forces Analysis
- 4.3.1. Bargaining Power of Suppliers (Moderate)
- 4.3.2. Threats of New Entrants: (Low)
- 4.3.3. Bargaining Power of Buyers (Moderate)
- 4.3.4. Threat of Substitute (Moderate)
- 4.3.5. Rivalry among existing firms (High)
- 4.4. PESTEL Analysis
- 4.5. Wind Turbine Market Trends
- 4.6. Value Chain Analysis
- 5.1. Key Findings
- 5.2. Introduction
- 5.2.1. U.S. Wind Turbine Market, by Axis, 2020-2034 (USD Billion)
- 5.3. Horizontal (HAWTs)
- 5.3.1. U.S. Wind Turbine Market, by Horizontal (HAWTs), 2020-2034 (USD Billion)
- 5.4. Vertical (VAWTs)
- 5.4.1. U.S. Wind Turbine Market, by Vertical (VAWTs), 2020-2034 (USD Billion)
- 6.1. Key Findings
- 6.2. Introduction
- 6.2.1. U.S. Wind Turbine Market, by Installation, 2020-2034 (USD Billion)
- 6.3. Onshore
- 6.3.1. U.S. Wind Turbine Market, by Onshore, 2020-2034 (USD Billion)
- 6.4. Offshore
- 6.4.1. U.S. Wind Turbine Market, by Semi-automatic, 2020-2034 (USD Billion)
- 7.1. Key Findings
- 7.2. Introduction
- 7.2.1. U.S. Wind Turbine Market, by Connectivity, 2020-2034 (USD Billion)
- 7.3. Grid connected
- 7.3.1. U.S. Wind Turbine Market, by Grid connected, 2020-2034 (USD Billion)
- 7.4. Standalone
- 7.4.1. U.S. Wind Turbine Market, by Standalone, 2020-2034 (USD Billion)
- 8.1. Key Findings
- 8.2. Introduction
- 8.2.1. U.S. Wind Turbine Market, by Rating, 2020-2034 (USD Billion)
- 8.3. 100 kW
- 8.3.1. U.S. Wind Turbine Market, by 100 kW, 2020-2034 (USD Billion)
- 8.4. 100 kW to 250 kW
- 8.4.1. U.S. Wind Turbine Market, by 100 kW to 250 kW, 2020-2034 (USD Billion)
- 8.5. > 250 kW to 500 kW
- 8.5.1. U.S. Wind Turbine Market, by > 250 kW to 500 kW, 2020-2034 (USD Billion)
- 8.6. > 500 kW to 1 MW
- 8.6.1. U.S. Wind Turbine Market, by > 500 kW to 1 MW, 2020-2034 (USD Billion)
- 8.7. 1 MW to 2 MW
- 8.7.1. U.S. Wind Turbine Market, by 1 MW to 2 MW, 2020-2034 (USD Billion)
- 8.8. > 2 MW
- 8.8.1. U.S. Wind Turbine Market, by > 2 MW, 2020-2034 (USD Billion)
- 9.1. Key Findings
- 9.2. Introduction
- 9.2.1. U.S. Wind Turbine Market, by Application, 2020-2034 (USD Billion)
- 9.3. Residential
- 9.3.1. U.S. Wind Turbine Market, by Residential, 2020-2034 (USD Billion)
- 9.4. Commercial & Industrial
- 9.4.1. U.S. Wind Turbine Market, by Commercial & Industrial, 2020-2034 (USD Billion)
- 9.5. Others
- 9.5.1. U.S. Wind Turbine Market, by Others, 2020-2034 (USD Billion)
- 10.1. Expansion and Acquisition Analysis
- 10.1.1. Expansion
- 10.1.2. Acquisitions
- 10.2. Partnerships/Collaborations/Agreements/Exhibitions
- 11.1. Acciona Energía
- 11.1.1. Company Overview
- 11.1.2. Financial Performance
- 11.1.3. Product Benchmarking
- 11.1.4. Recent Development
- 11.2. E.ON Climate & Renewables
- 11.2.1. Company Overview
- 11.2.2. Financial Performance
- 11.2.3. Product Benchmarking
- 11.2.4. Recent Development
- 11.3. GE Renewable Energy
- 11.3.1. Company Overview
- 11.3.2. Financial Performance
- 11.3.3. Product Benchmarking
- 11.3.4. Recent Development
- 11.4. Nordex
- 11.4.1. Company Overview
- 11.4.2. Financial Performance
- 11.4.3. Product Benchmarking
- 11.4.4. Recent Development
- 11.5. Orsted U.S. Offshore Wind
- 11.5.1. Company Overview
- 11.5.2. Financial Performance
- 11.5.3. Product Benchmarking
- 11.5.4. Recent Development
- 11.6. Siemens Gamesa Renewable Energy
- 11.6.1. Company Overview
- 11.6.2. Financial Performance
- 11.6.3. Product Benchmarking
- 11.6.4. Recent Development
- 11.7. Suzlon Energy
- 11.7.1. Company Overview
- 11.7.2. Financial Performance
- 11.7.3. Product Benchmarking
- 11.7.4. Recent Development
- 11.8. TPI Composites
- 11.8.1. Company Overview
- 11.8.2. Financial Performance
- 11.8.3. Product Benchmarking
- 11.8.4. Recent Development
- 11.9. Vestas
- 11.9.1. Company Overview
- 11.9.2. Financial Performance
- 11.9.3. Product Benchmarking
- 11.9.4. Recent Development
Pricing
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