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Predictive Maintenance Market by Component (Software and Service), Deployment Type (Cloud and On-premise), Technique (Vibration Monitoring, Electrical Testing, Oil Analysis, Ultrasonic Leak Detectors, Shock Pulse, Infrared, and Others), Stakeholder (MRO, OEM/ODM, and Technology Integrators), and Industry Vertical (Manufacturing, Energy & Utilities, Aerospace & Defense, Transportation & Logistics, Government, and Others): Global Opportunity Analysis and Industry Forecast, 2019–2026 Update Available On-Demand
| A02138 | Pages: 395 | Aug 2019 | 11495 Views | | |
| Author(s) : Pramod Borasi and Vishwa Gaul , Supradip Baul | Tables: 193 | Charts: NA | Formats*: | |
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Predictive maintenance is a process for monitoring equipment through the operation to identify any deterioration, enabling maintenance to be planned and operational costs reduced. It is commonly used in the context of Industry 4.0. The primary goal of predictive maintenance is to deliver the most precise advance maintenance planning to avoid unpredicted failures. Further, there are numerous benefits of adopting predictive maintenance, such as potentially extended service life of the equipment or assets, increased plant safety, optimized handling of spare parts and less breakdowns and outages, which may have negative impacts on the environment. The global predictive maintenance market size was valued at $2,804.38 million in 2018, and is projected to reach $23,014.7 million by 2026, growing at a CAGR of 30.20% from 2019 to 2026.
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The rise in need to improve uptime of equipment and maintenance cost reduction and increasing investment on predictive maintenance are expected to impact the market growth positively. In addition, integration of predictive maintenance with IIoT and use of machine learning and real-time condition monitoring to assist in taking prompt actions are expected to be predictive maintenance market opportunity. However, lack of skilled staff and difficulty in implementation are anticipated to impede the predictive maintenance market growth.
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Based on deployment model, the on-premise segment dominated the overall predictive maintenance market size in 2018 and is expected to maintain the dominance during the forecast period. However, cloud-based predictive maintenance solutions are expected to exhibit the highest growth rate during the forecast period, owing to direct IT control, internal data delivery & handling, faster data processing, efficient resource utilization, and cost-effectiveness offered by this model, which are anticipated to boost its adoption.
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Based on industry vertical, the manufacturing segment dominated the global predictive maintenance market size in 2018. The maintenance of manufacturing equipment such as machinery, pumps, elevators, and industrial robots is facing multiple challenges due to which the predictive maintenance is being increasingly adopted across the manufacturing sector. However, the healthcare segment is expected to grow at the highest rate in the near future as predictive maintenance of biomedical devices such as X-ray, MR, tomography, and mammography is becoming one of the major concerns to increase the decision-making capabilities and optimize the operational efficiencies in the hospitals.
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North America governed the overall predictive maintenance market share, in terms of revenue, in 2018, due to largest market share due to the presence of a large number of solution and service vendors in this region. In addition, great awareness regarding predictive maintenance measures, its importance and early adoption of technology in this region has also contributed toward the demand for predictive maintenance in this region. However, Asia-Pacific is expected to witness the highest growth rate during the forecast period, owing to rise in focus on trying innovative solution for achieving optimizes output for maintenance of assets is expected to influence the growth of predictive maintenance market in Asia-Pacific.
The report focuses on the growth prospects, restraints, and trends of the global predictive maintenance market analysis. Moreover, the study includes the Porter’s five forces analysis of the industry to understand the impact of various factors such as bargaining power of suppliers, competitive intensity of competitors, threat of new entrants, threat of substitutes, and bargaining power of buyers on the growth of the predictive maintenance market.
Segment review
The global predictive maintenance market is segmented into component, deployment model, technique, stakeholder, industry vertical, and region. Based on component, it is bifurcated into solution and service. According to deployment, the market is classified into cloud and on-premise segments. Further, based on technique the market is divided into vibration monitoring, electrical testing, oil analysis, ultrasonic leak detectors, shock pulse, infrared, and others. Based on stakeholder, the predictive maintenance market is segmented into MRO, OEM/ODM, and technology integrators. Based on industry vertical, it is classified into manufacturing, energy & utilities, aerospace & defense, transportation & logistics, government, and others. Based on region, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
The report analyzes the profiles of key players operating in the predictive maintenance market, also they have implemented a number of strategies including partnership, expansion, collaboration, joint ventures, and others to heighten their status in the industry. These include IBM Corporation, Microsoft Corporation, SAP SE, General Electric, Schneider Electric, Hitachi, Ltd., PTC Inc., Software AG, SAS Institute Inc., Engineering Consultants Group, Inc., Expert Microsystems, Inc., SparkCognition, C3.ai, Inc., Uptake Technologies Inc., Fiix Inc., Operational Excellence (Opex) Group Ltd, TIBCO Software Inc., Asystom, and Sigma Industrial Precision.
Top Impacting Factors
Increase in investment on predictive maintenance
According to a survey called “digital industrial revolution with predictive maintenance”, investment in predictive maintenance initiatives generates tangible return on investment (ROI). For instance, customers reported metrics such as 2-6% increased availability, 5-10% inventory cost reduction, and 10-40% reduction in reactive maintenance. Further, around 50% of the companies in this study are running the pilot programs for predictive maintenance, which drives the growth of the predictive maintenance market. Also, companies are realizing the importance of adopting predictive maintenance technology as a stepping stone to gain the competitive advantages such as increased customer satisfaction and new "as-a-service" business models. Furthermore, as per the study by Roland Berger, VDMA and Deutsche Messe AG, 81% of companies are currently devoting time and resources to industrial predictive maintenance subject, while 40% already have confidence that practicing PdM will be mostly significant for future business. This increase in awareness and trust about the predictive maintenance also drives the demand for the global predictive maintenance market.
Data privacy and security concerns
Data needs to be collected and shared with a high level of security to protect companies’ intellectual property. Detailed manufacturing equipment information including production processes can be an important part of a company’s competitive advantage. Customers’ financial and personal data also needs to be protected, particularly with regulations such as GDPR that imposes high penalties when customers’ data is used without their clear consent. Thus, lack of privacy and security concerns while deploying predictive maintenance solution restricts the growth of the global predictive maintenance market.
Integration of predictive maintenance with IIoT and use of machine learning
Manufacturers are adopting machine learning based predictive maintenance. It depends on large amount of historical or test data, along with tailored machine-learning algorithms, to test different scenarios and predict the errors in the system. Then it generates the alerts accordingly. When properly designed and implemented, a machine learning algorithm will learn the typical data’s behavior and identify deviation in real-time. A machine monitoring system will comprise input about diverse temperatures, engine speed, and others. The system can then predict the time of the breakdown. When predictive maintenance is coupled with the IIoT, it can catch equipment failures in advance. Due to emergence of ‘Industry 4.0’ in the manufacturing landscape, companies are keen to adopt IIoT to achieve better insights into their operations. Predictive maintenance relies on sensors for gathering and analyzing data from various sources, such as a CMMS and critical equipment sensors. By means of this data, the IIoT can provide innovative prediction models and analytical tools which will predict catastrophes and handle them proactively. Thus, the integration of predictive maintenance and IIoT is expected to offer lucrative opportunities for the global predictive maintenance market during the forecast period.
Key Benefits for Predictive Maintenance Market:
- This study includes the analytical depiction of the global predictive maintenance market trends and future estimations to determine the imminent investment pockets.
- The report presents information related to key drivers, restraints, and opportunities.
- The current predictive maintenance market is quantitatively analyzed from 2018 to 2026 to highlight the financial competency of the industry.
- Porter’s five forces analysis illustrates the potency of buyers & suppliers in the predictive maintenance industry.
Predictive Maintenance Market Segments:
By Component
- Solution
- Service
By Deployment
- Cloud
- On-premise
By Technique
- Vibration Monitoring
- Electrical Testing
- Oil Analysis
- Ultrasonic Leak Detectors
- Shock Pulse
- Infrared
- Others
By Stakeholder
- MRO
- OEM/ODM
- Technology Integrators
By Industry Vertical
- Manufacturing
- Energy & utilities
- Aerospace & Defense
- Transportation & Logistics
- Government
- Healthcare
- Others
By Region
- North America
- U.S.
- Canada
- Europe
- Germany
- France
- UK
- Italy
- Rest of Europe
- Asia-Pacific
- Japan
- China
- India
- South Korea
- Rest of Asia-Pacific
- LAMEA
- Latin America
- Middle East
- Africa
Predictive Maintenance Market Key Players
- IBM Corporation
- Microsoft Corporation
- SAP SE
- General Electric
- Schneider Electric
- Hitachi, Ltd.
- PTC Inc.
- Software AG
- SAS Institute Inc.
- Engineering Consultants Group, Inc.
- Expert Microsystems, Inc.
- SparkCognition
- C3.ai, Inc.
- Uptake Technologies Inc.
- Fiix Inc.
- Operational Excellence (Opex) Group Ltd
- TIBCO Software Inc.
- Asystom
- Sigma Industrial Precision
CHAPTER 1: INTRODUCTION
1.1. Report description
1.2. Key market segments
1.3. Research methodology
1.3.1. Primary research
1.3.2. Secondary research
1.3.3. Analyst tools & models
CHAPTER 2: EXECUTIVE SUMMARY
2.1. CXO perspective
CHAPTER 3: MARKET LANDSCAPE
3.1. Market definition and scope
3.2. Key findings
3.2.1. Top investment pockets
3.2.2. Top impacting factors
3.2.3. Top Winning Strategies
3.3. Porter's Five Forces Analysis
3.3.1. Bargaining power of suppliers
3.3.2. Threat of new entrants
3.3.3. Threat of substitutes
3.3.4. Competitive rivalry
3.3.5. Bargaining power among buyers
3.4. Key player positioning
3.5. Market dynamics
3.5.1. Drivers
3.5.1.1. The need to improve uptime of equipment and maintenance cost reduction
3.5.1.2. Increase in investment on predictive maintenance
3.5.2. Restraints
3.5.2.1. Lack of skilled staff
3.5.2.2. Difficult to implement
3.5.2.3. Data privacy and security concerns
3.5.3. Opportunity
3.5.3.1. Integration of predictive maintenance with IIoT and use of machine learning
3.5.3.2. Real-time condition monitoring to assist in taking prompt actions
3.6. Value chain analysis
3.7. Robotics adoption in manufacturing
3.8. AI implementation analysis across industry verticals
3.9. Qualitative insights (detection and diagnosis)
3.9.1. Case Studies
3.9.2. Mueller Industries
3.9.3. Deutsche Bahn AG
3.9.4. VAALCO Energy, Inc.
3.9.5. Israel Electric Corporation (IEC)
3.10. Models and approaches
3.10.1. Statistical Pattern Classification:
3.10.1.1. Bayesian Network
3.10.1.2. Neural networks
3.10.1.3. Linear Classification
3.10.1.4. Hybrid Models
3.10.2. Pattern Classification: Phases and Components
3.10.2.1. Training Phase
3.10.2.2. Testing Phase
3.10.3. Sub problems Pattern Classification
3.10.3.1. Feature Extraction
3.10.3.2. Over fitting
3.10.3.3. Model Selection
3.10.3.4. Prior Knowledge
3.10.3.5. Missing Features
3.10.3.6. Mereology
3.10.3.7. Segmentation
3.10.3.8. Context
3.10.3.9. Invariance’s Evidence
3.10.3.10. Pooling Costs and Risks
3.10.3.11. Computational Complexity
CHAPTER 4: PREDICTIVE MAINTENANCE MARKET, BY COMPONENT
4.1. Overview
4.2. Solution
4.2.1. Key market trends, growth factors and opportunities
4.2.2. Market size and forecast, by region
4.2.3. Market analysis by country
4.3. Service
4.3.1. Key market trends, growth factors, and opportunities
4.3.2. Market size and forecast, by region
4.3.3. Market analysis by country
4.3.4. Professional Service
4.3.4.1. Market size and forecast
4.3.4.2. Support and Maintenance
4.3.4.3. Deployment and Integration
4.3.4.4. Training and Education
4.3.5. Managed Service
CHAPTER 5: PREDICTIVE MAINTENANCE MARKET, BY TECHNIQUE
5.1. Overview
5.2. Vibration Monitoring
5.2.1. Key market trends, growth factors and opportunities
5.2.2. Market size and forecast, by region
5.2.3. Market analysis by country
5.3. Electrical Testing
5.3.1. Key market trends, growth factors, and opportunities
5.3.2. Market size and forecast, by region
5.3.3. Market analysis by country
5.4. Oil Analysis
5.4.1. Key market trends, growth factors, and opportunities
5.4.2. Market size and forecast, by region
5.4.3. Market analysis by country
5.5. Ultrasonic Leak Detectors
5.5.1. Key market trends, growth factors, and opportunities
5.5.2. Market size and forecast, by region
5.5.3. Market analysis by country
5.6. Shock Pulse
5.6.1. Key market trends, growth factors, and opportunities
5.6.2. Market size and forecast, by region
5.6.3. Market analysis by country
5.7. Infrared
5.7.1. Key market trends, growth factors and opportunities
5.7.2. Market size and forecast, by region
5.7.3. Market analysis by country
5.8. Others
5.8.1. Key market trends, growth factors and opportunities
5.8.2. Market size and forecast, by region
5.8.3. Market analysis by country
CHAPTER 6: PREDICTIVE MAINTENANCE MARKET, BY DEPLOYMENT TYPE
6.1. Overview
6.2. Cloud
6.2.1. Key market trends, growth factors and opportunities
6.2.2. Market size and forecast, by region
6.2.3. Market analysis by country
6.3. On-premise
6.3.1. Key market trends, growth factors and opportunities
6.3.2. Market size and forecast, by region
6.3.3. Market analysis by country
CHAPTER 7: PREDICTIVE MAINTENANCE MARKET, BY STAKEHOLDER
7.1. Overview
7.2. MRO
7.2.1. Key market trends, growth factors and opportunities
7.2.2. Market size and forecast, by region
7.2.3. Market analysis by country
7.3. OEM/ODM
7.3.1. Key market trends, growth factors and opportunities
7.3.2. Market size and forecast, by region
7.3.3. Market analysis by country
7.4. Technology Integrators
7.4.1. Key market trends, growth factors and opportunities
7.4.2. Market size and forecast, by region
7.4.3. Market analysis by country
7.4.4. Pure play
7.4.5. End-to-end
CHAPTER 8: PREDICTIVE MAINTENANCE MARKET, BY INDUSTRY VERTICAL
8.1. Overview
8.2. Manufacturing
8.2.1. Key market trends, growth factors and opportunities
8.2.2. Market size and forecast, by region
8.2.3. Market analysis by country
8.3. Energy & utilities
8.3.1. Key market trends, growth factors and opportunities
8.3.2. Market size and forecast, by region
8.3.3. Market analysis by country
8.4. Aerospace & Defense
8.4.1. Key market trends, growth factors and opportunities
8.4.2. Market size and forecast, by region
8.4.3. Market analysis by country
8.5. Transportation & logistics
8.5.1. Key market trends, growth factors and opportunities
8.5.2. Market size and forecast, by region
8.5.3. Market analysis by country
8.6. Government
8.6.1. Key market trends, growth factors and opportunities
8.6.2. Market size and forecast, by region
8.6.3. Market analysis by country
8.7. Healthcare
8.7.1. Key market trends, growth factors and opportunities
8.7.2. Market size and forecast, by region
8.7.3. Market analysis by country
8.8. Others
8.8.1. Key market trends, growth factors and opportunities
8.8.2. Market size and forecast, by region
8.8.3. Market analysis by country
CHAPTER 9: PREDICTIVE MAINTENANCE MARKET, BY REGION
9.1. Overview
9.2. North America
9.2.1. Key market trends, growth factors and opportunities
9.2.2. Market size and forecast, by Component
9.2.3. Market size and forecast, by Technique
9.2.4. Market size and forecast, by Deployment Type
9.2.5. Market size and forecast, by Stakeholders
9.2.6. Market size and forecast, by Industry Vertical
9.2.7. Market analysis by country
9.2.7.1. U.S.
9.2.7.1.1. Market size and forecast, by Component
9.2.7.1.2. Market size and forecast, by Technique
9.2.7.1.3. Market size and forecast, by Deployment Type
9.2.7.1.4. Market size and forecast, by Stakeholders
9.2.7.1.5. Market size and forecast, by Industry Vertical
9.2.7.2. Canada
9.2.7.2.1. Market size and forecast, by Component
9.2.7.2.2. Market size and forecast, by Technique
9.2.7.2.3. Market size and forecast, by Deployment Type
9.2.7.2.4. Market size and forecast, by Stakeholders
9.2.7.2.5. Market size and forecast, by Industry Vertical
9.3. Europe
9.3.1. Key market trends, growth factors and opportunities
9.3.2. Market size and forecast, by Component
9.3.3. Market size and forecast, by Technique
9.3.4. Market size and forecast, by Deployment Type
9.3.5. Market size and forecast, by Stakeholders
9.3.6. Market size and forecast, by Industry Vertical
9.3.7. Market analysis by country
9.3.7.1. Germany
9.3.7.1.1. Market size and forecast, by Component
9.3.7.1.2. Market size and forecast, by Technique
9.3.7.1.3. Market size and forecast, by Deployment Type
9.3.7.1.4. Market size and forecast, by Stakeholders
9.3.7.1.5. Market size and forecast, by Industry Vertical
9.3.7.2. France
9.3.7.2.1. Market size and forecast, by Component
9.3.7.2.2. Market size and forecast, by Technique
9.3.7.2.3. Market size and forecast, by Deployment Type
9.3.7.2.4. Market size and forecast, by Stakeholders
9.3.7.2.5. Market size and forecast, by Industry Vertical
9.3.7.3. UK
9.3.7.3.1. Market size and forecast, by Component
9.3.7.3.2. Market size and forecast, by Technique
9.3.7.3.3. Market size and forecast, by Deployment Type
9.3.7.3.4. Market size and forecast, by Stakeholders
9.3.7.3.5. Market size and forecast, by Industry Vertical
9.3.7.4. Italy
9.3.7.4.1. Market size and forecast, by Component
9.3.7.4.2. Market size and forecast, by Technique
9.3.7.4.3. Market size and forecast, by Deployment Type
9.3.7.4.4. Market size and forecast, by Stakeholders
9.3.7.4.5. Market size and forecast, by Industry Vertical
9.3.7.5. Rest of Europe
9.3.7.5.1. Market size and forecast, by Component
9.3.7.5.2. Market size and forecast, by Technique
9.3.7.5.3. Market size and forecast, by Deployment Type
9.3.7.5.4. Market size and forecast, by Stakeholders
9.3.7.5.5. Market size and forecast, by Industry Vertical
9.4. Asia-Pacific
9.4.1. Key market trends, growth factors and opportunities
9.4.2. Market size and forecast, by Component
9.4.3. Market size and forecast, by Technique
9.4.4. Market size and forecast, by Deployment Type
9.4.5. Market size and forecast, by Stakeholders
9.4.6. Market size and forecast, by Industry Vertical
9.4.7. Market analysis by country
9.4.7.1. Japan
9.4.7.1.1. Market size and forecast, by Component
9.4.7.1.2. Market size and forecast, by Technique
9.4.7.1.3. Market size and forecast, by Deployment Type
9.4.7.1.4. Market size and forecast, by Stakeholders
9.4.7.1.5. Market size and forecast, by Industry Vertical
9.4.7.2. China
9.4.7.2.1. Market size and forecast, by Component
9.4.7.2.2. Market size and forecast, by Technique
9.4.7.2.3. Market size and forecast, by Deployment Type
9.4.7.2.4. Market size and forecast, by Stakeholders
9.4.7.2.5. Market size and forecast, by Industry Vertical
9.4.7.3. India
9.4.7.3.1. Market size and forecast, by Component
9.4.7.3.2. Market size and forecast, by Technique
9.4.7.3.3. India size and forecast, by Deployment Type
9.4.7.3.4. Market size and forecast, by Stakeholders
9.4.7.3.5. Market size and forecast, by Industry Vertical
9.4.7.4. South Korea
9.4.7.4.1. Market size and forecast, by Component
9.4.7.4.2. Market size and forecast, by Technique
9.4.7.4.3. Market size and forecast, by Deployment Type
9.4.7.4.4. Market size and forecast, by Stakeholders
9.4.7.4.5. Market size and forecast, by Industry Vertical
9.4.7.5. Rest of Asia-Pacific
9.4.7.5.1. Market size and forecast, by Component
9.4.7.5.2. Market size and forecast, by Technique
9.4.7.5.3. Market size and forecast, by Deployment Type
9.4.7.5.4. Market size and forecast, by Stakeholders
9.4.7.5.5. Market size and forecast, by Industry Vertical
9.5. LAMEA
9.5.1. Key market trends, growth factors and opportunities
9.5.2. Market size and forecast, by Component
9.5.3. Market size and forecast, by Technique
9.5.4. Market size and forecast, by Deployment Type
9.5.5. Market size and forecast, by Stakeholders
9.5.6. Market size and forecast, by Industry Vertical
9.5.7. Market analysis by country
9.5.7.1. Latin America
9.5.7.1.1. Market size and forecast, by Component
9.5.7.1.2. Market size and forecast, by Technique
9.5.7.1.3. Market size and forecast, by Deployment Type
9.5.7.1.4. Market size and forecast, by Stakeholders
9.5.7.1.5. Market size and forecast, by Industry Vertical
9.5.7.2. Middle East
9.5.7.2.1. Market size and forecast, by Component
9.5.7.2.2. Market size and forecast, by Technique
9.5.7.2.3. Market size and forecast, by Deployment Type
9.5.7.2.4. Market size and forecast, by Stakeholders
9.5.7.2.5. Market size and forecast, by Industry Vertical
9.5.7.3. Africa
9.5.7.3.1. Market size and forecast, by Component
9.5.7.3.2. Market size and forecast, by Technique
9.5.7.3.3. Market size and forecast, by Deployment Type
9.5.7.3.4. Market size and forecast, by Stakeholders
9.5.7.3.5. Market size and forecast, by Industry Vertical
CHAPTER 10: COMPANY PROFILES
10.1. Asystom
10.1.1. Company overview
10.1.2. Key Executives
10.1.3. Company snapshot
10.1.4. Product portfolio
10.1.5. Key strategic moves and developments
10.2. C3.ai, Inc.
10.2.1. Company overview
10.2.2. Key Executives
10.2.3. Company snapshot
10.2.4. Product portfolio
10.2.5. Key strategic moves and developments
10.3. Engineering Consultants Group, Inc.
10.3.1. Company overview
10.3.2. Key Executives
10.3.3. Company snapshot
10.3.4. Product portfolio
10.3.5. Key strategic moves and developments
10.4. Expert Microsystems, Inc.
10.4.1. Company overview
10.4.2. Key Executives
10.4.3. Company snapshot
10.4.4. Product portfolio
10.5. Fiix Inc.
10.5.1. Company overview
10.5.2. Key Executives
10.5.3. Company snapshot
10.5.4. Product portfolio
10.5.5. Key strategic moves and developments
10.6. Operational Excellence (OPEX) Group Ltd
10.6.1. Company overview
10.6.2. Key Executives
10.6.3. Company snapshot
10.6.4. Product portfolio
10.6.5. Key strategic moves and developments
10.7. Sigma Industrial Precision
10.7.1. Company overview
10.7.2. Key Executives
10.7.3. Company snapshot
10.7.4. Product portfolio
10.8. Spark Cognition
10.8.1. Company overview
10.8.2. Key Executives
10.8.3. Company snapshot
10.8.4. Product portfolio
10.8.5. Key strategic moves and developments
10.9. TIBCO Software Inc.
10.9.1. Company overview
10.9.2. Key Executives
10.9.3. Company snapshot
10.9.4. Product portfolio
10.9.5. Key strategic moves and developments
10.10. Uptake Technologies Inc.
10.10.1. Company overview
10.10.2. Key Executives
10.10.3. Company snapshot
10.10.4. Product portfolio
10.10.5. Key strategic moves and developments
10.11. General Electric
10.11.1. Company overview
10.11.2. Key Executives
10.11.3. Company snapshot
10.11.4. Operating business segments
10.11.5. Product portfolio
10.11.6. Business performance
10.11.7. Key strategic moves and developments
10.12. Hitachi, Ltd.
10.12.1. Company overview
10.12.2. Key Executives
10.12.3. Company snapshot
10.12.4. Operating business segments
10.12.5. Product portfolio
10.12.6. R&D Expenditure
10.12.7. Business performance
10.12.8. Key strategic moves and developments
10.13. International Business Machines Corporation (IBM)
10.13.1. Company overview
10.13.2. Key Executives
10.13.3. Company snapshot
10.13.4. Operating business segments
10.13.5. Product portfolio
10.13.6. R&D Expenditure
10.13.7. Business performance
10.13.8. Key strategic moves and developments
10.14. Microsoft Corporation
10.14.1. Company overview
10.14.2. Key Executives
10.14.3. Company snapshot
10.14.4. Operating business segments
10.14.5. Product portfolio
10.14.6. R&D Expenditure
10.14.7. Business performance
10.14.8. Key strategic moves and developments
10.15. PTC Inc.
10.15.1. Company overview
10.15.2. Key Executives
10.15.3. Company snapshot
10.15.4. Operating business segments
10.15.5. Product portfolio
10.15.6. R&D Expenditure
10.15.7. Business performance
10.15.8. Key strategic moves and developments
10.16. SAP
10.16.1. Company overview
10.16.2. Key Executives
10.16.3. Company snapshot
10.16.4. Operating business segments
10.16.5. Product portfolio
10.16.6. R&D Expenditure
10.16.7. Business performance
10.16.8. Key strategic moves and developments
10.17. SAS Institute Inc.
10.17.1. Company overview
10.17.2. Key Executives
10.17.3. Company snapshot
10.17.4. Product portfolio
10.17.5. Key strategic moves and developments
10.18. Schneider Electric SE
10.18.1. Company overview
10.18.2. Key Executives
10.18.3. Company snapshot
10.18.4. Operating business segments
10.18.5. Product portfolio
10.18.6. R&D Expenditure
10.18.7. Business performance
10.18.8. Key strategic moves and developments
10.19. Software AG
10.19.1. Company overview
10.19.2. Key Executives
10.19.3. Company snapshot
10.19.4. Operating business segments
10.19.5. Product portfolio
10.19.6. R&D Expenditure
10.19.7. Business performance
10.19.8. Key strategic moves and developments
LIST OF TABLES
TABLE 01. GLOBAL PREDICTIVE MAINTENANCE MARKET, BY COMPONENT, 2018-2026 ($MILLION)
TABLE 02. PREDICTIVE MAINTENANCE MARKET FOR SOLUTION REVENUE, BY REGION 2018-2026 ($MILLION)
TABLE 03. PREDICTIVE MAINTENANCE MARKET REVENUE FOR SERVICE, BY REGION, 2018-2026 ($MILLION)
TABLE 04. GLOBAL PREDICTIVE MAINTENANCE MARKET, BY SERVICE, 2018-2026($MILLION)
TABLE 05. GLOBAL PREDICTIVE MAINTENANCE MARKET, BY PROFESSIONAL SERVICE, 2018-2026($MILLION)
TABLE 06. GLOBAL PREDICTIVE MAINTENANCE MARKET, BY TECHNIQUE, 2018-2026($MILLION)
TABLE 07. PREDICTIVE MAINTENANCE MARKET REVENUE FOR VIBRATION MONITORING, BY REGION 2018-2026 ($MILLION)
TABLE 08. PREDICTIVE MAINTENANCE MARKET REVENUE FOR ELECTRICAL TESTING, BY REGION, 2018-2026 ($MILLION)
TABLE 09. PREDICTIVE MAINTENANCE MARKET REVENUE FOR OIL ANALYSIS, BY REGION, 2018-2026 ($MILLION)
TABLE 10. PREDICTIVE MAINTENANCE MARKET REVENUE FOR ULTRASONIC LEAK DETECTORS, BY REGION, 2018-2026 ($MILLION)
TABLE 11. PREDICTIVE MAINTENANCE MARKET REVENUE FOR SHOCK PULSE, BY REGION, 2018-2026 ($MILLION)
TABLE 12. PREDICTIVE MAINTENANCE MARKET REVENUE FOR INFRARED, BY REGION 2018-2026 ($MILLION)
TABLE 13. PREDICTIVE MAINTENANCE MARKET REVENUE FOR OTHERS, BY REGION 2018-2026 ($MILLION)
TABLE 14. GLOBAL PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026($MILLION)
TABLE 15. PREDICTIVE MAINTENANCE MARKET REVENUE FOR CLOUD, BY REGION 2018-2026 ($MILLION)
TABLE 16. PREDICTIVE MAINTENANCE MARKET REVENUE FOR ON-PREMISE, BY REGION 2018-2026 ($MILLION)
TABLE 17. GLOBAL PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDER, 2018-2026($MILLION)
TABLE 18. PREDICTIVE MAINTENANCE MARKET REVENUE FOR MRO, BY REGION 2018-2026 ($MILLION)
TABLE 19. PREDICTIVE MAINTENANCE MARKET REVENUE FOR OEM/ODM, BY REGION 2018-2026 ($MILLION)
TABLE 20. GLOBAL PREDICTIVE MAINTENANCE MARKET, BY TECHNOLOGY INTEGRATORS, 2018-2026($MILLION)
TABLE 21. PREDICTIVE MAINTENANCE MARKET REVENUE FOR TECHNOLOGY INTEGRATORS, BY REGION 2018-2026 ($MILLION)
TABLE 22. GLOBAL PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026($MILLION)
TABLE 23. PREDICTIVE MAINTENANCE MARKET REVENUE FOR MANUFACTURING, BY REGION 2018-2026 ($MILLION)
TABLE 24. PREDICTIVE MAINTENANCE MARKET REVENUE FOR ENERGY & UTILITIES, BY REGION 2018-2026 ($MILLION)
TABLE 25. PREDICTIVE MAINTENANCE MARKET REVENUE FOR AEROSPACE & DEFENSE, BY REGION 2018-2026 ($MILLION)
TABLE 26. PREDICTIVE MAINTENANCE MARKET REVENUE FOR TRANSPORTATION & LOGISTICS, BY REGION 2018-2026 ($MILLION)
TABLE 27. PREDICTIVE MAINTENANCE MARKET REVENUE FOR GOVERNMENT, BY REGION 2018-2026 ($MILLION)
TABLE 28. PREDICTIVE MAINTENANCE MARKET REVENUE FOR HEALTHCARE, BY REGION 2018-2026 ($MILLION)
TABLE 29. PREDICTIVE MAINTENANCE MARKET REVENUE FOR OTHERS, BY REGION 2018-2026 ($MILLION)
TABLE 30. NORTH AMERICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 31. NORTH AMERICA PREDICTIVE MAINTENANCE MARKET VALUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 32. NORTH AMERICA PREDICTIVE MAINTENANCE MARKET VALUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 33. NORTH AMERICA PREDICTIVE MAINTENANCE MARKET VALUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 34. NORTH AMERICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 35. NORTH AMERICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY COUNTRY, 2018-2026 ($MILLION)
TABLE 36. U.S. PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 37. U.S. PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 38. U.S. PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 39. U.S. PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 40. U.S. PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 41. CANADA PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 42. CANADA PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 43. CANADA PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 44. CANADA PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 45. CANADA PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 46. EUROPE PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 47. EUROPE PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 48. EUROPE PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 49. EUROPE PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 50. EUROPE PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 51. EUROPE PREDICTIVE MAINTENANCE MARKET REVENUE, BY COUNTRY, 2018-2026 ($MILLION)
TABLE 52. GERMANY PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 53. GERMANY PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 54. GERMANY PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 55. GERMANY PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 56. GERMANY PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 57. FRANCE PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 58. FRANCE PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 59. FRANCE PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 60. FRANCE PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 61. FRANCE PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 62. UK PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 63. UK PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 64. UK PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 65. UK PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 66. UK PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 67. ITALY PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 68. ITALY PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 69. ITALY PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 70. ITALY PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 71. ITALY PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 72. REST OF EUROPE PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 73. REST OF EUROPE PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 74. REST OF EUROPE PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 75. REST OF EUROPE PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 76. REST OF EUROPE PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 77. ASIA-PACIFIC PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 78. ASIA-PACIFIC PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 79. ASIA-PACIFIC PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 80. ASIA-PACIFIC PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 81. ASIA-PACIFIC PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 82. ASIA-PACIFIC PREDICTIVE MAINTENANCE MARKET REVENUE, BY COUNTRY, 2018-2026 ($MILLION)
TABLE 83. JAPAN PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 84. JAPAN PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 85. JAPAN PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 86. JAPAN PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 87. JAPAN PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 88. CHINA PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 89. CHINA PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 90. CHINA PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 91. CHINA PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 92. CHINA PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 93. INDIA PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 94. INDIA PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 95. INDIA PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 96. INDIA PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 97. INDIA PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 98. SOUTH KOREA PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 99. SOUTH KOREA PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 100. SOUTH KOREA PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 101. SOUTH KOREA PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 102. SOUTH KOREA PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 103. REST OF ASIA-PACIFIC PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 104. REST OF ASIA-PACIFIC PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 105. REST OF ASIA-PACIFIC PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 106. REST OF ASIA-PACIFIC PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 107. REST OF ASIA-PACIFIC PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 108. LAMEA PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 109. LAMEA PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 110. LAMEA PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 111. LAMEA PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 112. LAMEA PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 113. LAMEA PREDICTIVE MAINTENANCE MARKET REVENUE, BY COUNTRY, 2018-2026 ($MILLION)
TABLE 114. LATIN AMERICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 115. LATIN AMERICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 116. LATIN AMERICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 117. LATIN AMERICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 118. LATIN AMERICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 119. MIDDLE EAST PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 120. MIDDLE EAST PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 121. MIDDLE EAST PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 122. MIDDLE EAST PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 123. MIDDLE EAST PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 124. AFRICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY COMPONENT 2018-2026 ($MILLION)
TABLE 125. AFRICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY TECHNIQUE, 2018-2026 ($MILLION)
TABLE 126. AFRICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY DEPLOYMENT TYPE, 2018-2026 ($MILLION)
TABLE 127. AFRICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY STAKEHOLDERS, 2018-2026 ($MILLION)
TABLE 128. AFRICA PREDICTIVE MAINTENANCE MARKET REVENUE, BY INDUSTRY VERTICAL, 2018-2026 ($MILLION)
TABLE 129. ASYSTOM: KEY EXECUTIVES
TABLE 130. ASYSTOM: COMPANY SNAPSHOT
TABLE 131. ASYSTOM: PRODUCT PORTFOLIO
TABLE 132. C3.AI, INC.: KEY EXECUTIVES
TABLE 133. C3.AI, INC.: COMPANY SNAPSHOT
TABLE 134. C3.AI, INC.: PRODUCT PORTFOLIO
TABLE 135. ENGINEERING CONSULTANTS GROUP, INC.: KEY EXECUTIVES
TABLE 136. ENGINEERING CONSULTANTS GROUP, INC.: COMPANY SNAPSHOT
TABLE 137. ENGINEERING CONSULTANTS GROUP, INC.: PRODUCT PORTFOLIO
TABLE 138. EXPERT MICROSYSTEMS, INC.: KEY EXECUTIVES
TABLE 139. EXPERT MICROSYSTEMS, INC.: COMPANY SNAPSHOT
TABLE 140. EXPERT MICROSYSTEMS, INC.: PRODUCT PORTFOLIO
TABLE 141. FIIX INC.: KEY EXECUTIVES
TABLE 142. FIIX INC.: COMPANY SNAPSHOT
TABLE 143. FIIX INC.: PRODUCT PORTFOLIO
TABLE 144. OPERATIONAL EXCELLENCE (OPEX) GROUP LTD: KEY EXECUTIVES
TABLE 145. OPERATIONAL EXCELLENCE (OPEX) GROUP LTD: COMPANY SNAPSHOT
TABLE 146. OPERATIONAL EXCELLENCE (OPEX) GROUP LTD: PRODUCT PORTFOLIO
TABLE 147. SIGMA INDUSTRIAL PRECISION: KEY EXECUTIVES
TABLE 148. SIGMA INDUSTRIAL PRECISION: COMPANY SNAPSHOT
TABLE 149. SIG
As per the CXOs perspective, plant employees are more accepting of growing change such as the automation of workflows. If IIoT predictive maintenance simply offers new insights to activate effective maintenance events, then its adoption is anticipated to be easier. Furthermore, increase in need to reduce the downtime and improve asset life are some of the major factors that drive the predictive maintenance market growth. Moreover, vibration monitoring, electrical testing, and oil analysis are expected to be the major segment in terms of revenue in the predictive maintenance market. Also, manufacturing industry is anticipated to be a leading segment and continue to lead even in the upcoming years.
The global predictive maintenance market has a very competitive environment. Further, the market comprises of several international and regional players. The global players focus on increasing their presence in many regions. This increases the competition in terms of features, quality, and price for the local vendors. Market players are adopting various business strategies to enhance their product offerings, business expansion, and increase their market penetration. For instance, in January 2019, Hitachi Ltd., announced that it will strengthen its wind power generator maintenance services and also expand its core product of wind power generation solution business. The move is the part of Hitachi’s efforts to strengthen its renewable energy business, including collaborative and community-based creation-oriented energy projects including storage batteries, solar power, and other elements
Q1. What is the market share of Predictive Maintenance Market by 2026?
A. Predictive Maintenance Market is projected to reach $23,014.7 million by 2026.
Q2. Which are the leading players in Predictive Maintenance Market?
A. Major Players are profiled in the study are IBM Corporation,Microsoft Corporation,SAP SE,General Electric,Schneider Electric,Hitachi, Ltd.,PTC Inc.,Software AG,SAS Institute Inc. and others.
Q3. How can I get report sample of Predictive Maintenance market report?
A. To get Sample of Predictive Maintenance market report
Q4. What is the CAGR of Predictive Maintenance Market by 2026?
A. The global Predictive Maintenance market is growing at a CAGR of 30.20% from 2019 to 2026.
Q5. What would be the revenue generated by the Predictive Maintenance Market by the end of 2026?
A. $23.01 billion revenve expected by Predictive Maintenance market at the end of 2026.
Q6. How can I get company profiles on top 10 players of Predictive Maintenance Market?
A. To get company profiles of major key players
Q7. What factors are anticipated to drive Predictive Maintenance industry?
A. The need to improve uptime of equipment and maintenance cost reduction and Increase in investment on predictive maintenance are the major factors.
Q8. Which region will provide more business opportunities during forecast period?
A. North America governed the overall market share in 2018. However, Asia-Pacific is expected to exhibit highest growth rate throughout the forecast period.
Q9. What will be the new opportunities in the Predictive Maintenance market?
A. Integration of predictive maintenance with IIoT and use of machine learning, Real-time condition monitoring to assist in taking prompt actions, Difficult to implement and Data privacy and security concerns are major impacting factors.
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