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Global Predictive Maintenance Market to 2027 - Featuring Altair, Google and Hitachi Among Others

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Research and Markets

Mar 31, 2022, 15:45 ET

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DUBLIN, March 31, 2022 /PRNewswire/ -- The "Global Predictive Maintenance Market, By Component, By Deployment Mode, By Technology, By Organization Size, By Industry, Estimation & Forecast, 2017 - 2027" report has been added to ResearchAndMarkets.com's offering.

The global predictive maintenance market held a market value of USD 4,029.4 Million in 2020 and is projected to reach USD 18,653.2 Million by the year 2027. The market is anticipated to grow at a CAGR of 26.5% from 2021 to 2027.

Predictive maintenances uses the data driven, proactive maintenance methods which are designed for analysing the condition of an equipment and helps to predict when maintenance should be performed. The market is expected to be driven by the rising urbanization coupled with rampant digitalization. Furthermore, growing demand to decrease operation and maintenance cost are also estimated to fuel the market growth.

Despite the driving factors, lack of skilled workforce are anticipated to hinder the market growth. During the COVID-19 pandemic, the supply of hardware slowed down and manufacturing capacity also reduced. Furthermore, the growth of IT infrastructure also slowed down. Therefore, the COVID-19 pandemic negatively impacted the market growth.

Growth Influencers:

Increasing demand to decrease operation and maintenance cost

Various companies are making use of machine learning as well as artificial intelligence technologies for achieving utmost accuracy, speed, and precision for analysing internet of things data. They are choosing these over conventional business intelligence tools. By using predictive maintenance tools, companies can speed up the operations by 20 times and with higher accuracy.

Furthermore, in various industries such as offshore oil & gas and industrial manufacturing, unplanned downtime due to breakdown of equipment can increase the operation as well as maintenance cost. Therefore, use of predictive maintenance can increase the demand to decrease operation and maintenance cost, hence boosting the market growth.

Competitive Landscape

Major players in the global predictive maintenance market include Altair, AWS, Fiix Software, GE, Google, Hitachi, IBM, Micro Focus, Microsoft, OPEX Group, Oracle, PTC, SAP, SAS Institute, Schneider Electric, Siemens, SEEBO, Sigma Industrial Precision, Software AG, Splunk, and Other Prominent Players. The cumulative market share of the ten major players is near about 58%.

These market players are involved in collaborations, mergers & acquisitions, and new product launches, among other initiatives, to strengthen their market presence. For instance, in August 2021, Micro Focus launched Digital Safe managed services for assisting regulated organizations to reduce operational as well as legal risk costs.

The global Predictive maintenance market report provides insights on the below pointers:

  • Market Penetration: Provides comprehensive information on the market offered by the prominent players
  • Market Development: The report offers detailed information about lucrative emerging markets and analyzes penetration across mature segments of the markets
  • Market Diversification: Provides in-depth information about untapped geographies, recent developments, and investments
  • Competitive Landscape Assessment: Mergers & acquisitions, certifications, product launches in the global Predictive maintenance market have been provided in this research report. In addition, the report also emphasizes the SWOT analysis of the leading players.
  • Product Development & Innovation: The report provides intelligent insights on future technologies, R&D activities, and breakthrough product developments

The global Predictive maintenance market report answers questions such as:

  • What is the market size and forecast of the Global Predictive maintenance Market?
  • What are the inhibiting factors and impact of COVID-19 on the Global Predictive maintenance Market during the assessment period?
  • Which are the products/segments/applications/areas to invest in over the assessment period in the Global Predictive maintenance Market?
  • What is the competitive strategic window for opportunities in the Global Predictive maintenance Market?
  • What are the technology trends and regulatory frameworks in the Global Predictive maintenance Market?
  • What is the market share of the leading players in the Global Predictive maintenance Market?
  • What modes and strategic moves are considered favorable for entering the Global Predictive maintenance Market?

Key Topics Covered:

Chapter 1. Research Framework

Chapter 2. Research Methodology

Chapter 3. Executive Summary: Global Predictive Maintenance Market

Chapter 4. Global Predictive Maintenance Market Overview
4.1.1. Device Layer
4.1.2. Connectivity Layer
4.1.3. Data Layer
4.1.4. App Layer
4.1.5. Service Layer
4.2. Industry Outlook
4.3. PESTLE Analysis
4.4. Porter's Five Forces Analysis
4.4.1. Bargaining Power of Suppliers
4.4.2. Bargaining Power of Buyers
4.4.3. Threat of Substitutes
4.4.4. Threat of New Entrants
4.4.5. Degree of Competition
4.5. Market Dynamics and Trends
4.5.1. Growth Drivers
4.5.2. Restraints
4.5.3. Challenges
4.5.4. Key Trends
4.6. Covid-19 Impact Assessment on Market Growth Trend
4.7. Technology Analysis
4.8. Market Growth and Outlook
4.8.1. Market Revenue Estimates and Forecast (US$ Mn), 2017 - 2027
4.8.2. Pricing Analysis
4.9. Competition Dashboard
4.9.1. Market Concentration Rate
4.9.2. Company Market Share Analysis (Value %), 2020
4.9.3. Competitor Mapping

Chapter 5. Predictive Maintenance Market Analysis, By Component
5.1. Key Insights
5.2. Market Size and Forecast, 2017 - 2027 (US$ Mn)
5.2.1. Solutions
5.2.1.1. Integrated
5.2.1.2. Standalone
5.2.2. Services
5.2.2.1. Managed Services
5.2.2.2. Professional Services

Chapter 6. Predictive Maintenance Market Analysis, By Deployment Mode
6.1. Key Insights
6.2. Market Size and Forecast, 2017 - 2027 (US$ Mn)
6.2.1. Cloud
6.2.1.1. Public Cloud
6.2.1.2. Private Cloud
6.2.1.3. Hybrid Cloud
6.2.2. On-Premises

Chapter 7. Predictive Maintenance Market Analysis, By Technology
7.1. Key Insights
7.2. Market Size and Forecast, 2017 - 2027 (US$ Mn)
7.2.1. Vibration Monitoring
7.2.2. Electrical Testing
7.2.3. Oil Analysis
7.2.4. Ultrasonic Detectors
7.2.5. Shock Pulse
7.2.6. Thermal/Infrared Monitoring
7.2.7. Optical (cameras) Monitoring
7.2.8. ML Database
7.2.9. Others

Chapter 8. Predictive Maintenance Market Analysis, By Organization Size
8.1. Key Insights
8.2. Market Size and Forecast, 2017 - 2027 (US$ Mn)
8.2.1. Large Enterprises
8.2.2. Small and Medium-Sized Enterprises

Chapter 9. Predictive Maintenance Market Analysis, By Industry
9.1. Key Insights
9.2. Market Size and Forecast, 2017 - 2027 (US$ Mn)
9.2.1. Aerospace and Defense
9.2.2. Manufacturing
9.2.3. Energy and Utilities
9.2.4. Oil & Gas
9.2.5. Transportation
9.2.6. Warehouse & Logistics
9.2.7. Healthcare and Lifesciences
9.2.8. Automotive
9.2.9. Marine/ Shipping
9.2.10. Others

Chapter 10. Predictive Maintenance Market Analysis, By Region/ Country

Chapter 11. North America Predictive Maintenance Market Analysis

Chapter 12. Europe Predictive Maintenance Market Analysis

Chapter 13. Asia Pacific Predictive Maintenance Market Analysis

Chapter 14. Middle East & Africa Predictive Maintenance Market Analysis

Chapter 15. South America Predictive Maintenance Market Analysis

Chapter 16. Company Profile (Company Overview, Financial Matrix, Key Product landscape, Key Personnel, Key Competitors, Contact Address, and Business Strategy Outlook)
16.1. Altair
16.2. AWS
16.3. FIIX Software
16.4. GE
16.5. Google
16.6. Hitachi
16.7. IBM
16.8. Micro Focus
16.9. Microsoft
16.10. OPEX Group
16.11. Oracle
16.12. PTC
16.13. SAP
16.14. SAS Institute
16.15. Schneider Electric
16.16. Siemens
16.17. SEEBO
16.18. Sigma Industrial Precision
16.19. Software AG
16.20. Splunk
16.21. Other Prominent Players

For more information about this report visit https://www.researchandmarkets.com/r/n3m0xc

Media Contact:

Research and Markets
Laura Wood, Senior Manager
[email protected]   

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