Quick Summary:
Efficiency is paramount for the modern business executive. In the globally competitive landscape of industry and manufacturing, it pays to have a firm grip on your machinery's performance and lifespan. This is where predictive maintenance steps into the equation. Aptly named, predictive maintenance utilizes state-of-the-art tools and systems, including the internet of things (IoT), artificial intelligence (AI), and integrated systems, to monitor machine performance and preemptively identify potential failures or required replacements.
With its implementation, businesses can expect reduced maintenance costs, increased equipment shelf-life, and improved productivity. In an era where time is money, the ability to preplan and prepare repairs, prevent the disruption of production cycles, and avoid unplanned downtime is invaluable. The return on investment for predictive maintenance is tangible, backed by increased lifespan for aging industrial machinery and new potentials for remote monitoring and advanced inspections. Move with the times; seize the benefits offered by the predictive maintenance market.
Predictive maintenance refers to the technique that relies on condition-monitoring tools and systems to monitor the performance of equipment during operation. It comprises the internet of things (IoT), artificial intelligence (AI), and integrated systems to connect different assets and systems and share and analyze crucial data. It also consists of predictive maintenance sensors, industrial controls, and business systems, such as enterprise asset management (EAM) and enterprise resource planning (ERP) software. It functions by utilizing condition monitoring equipment to examine and evaluate the performance of assets. It records a wide range of data, such as temperature, vibrations, and conductivity, which enables an engineer to predict the failure of equipment or assets while allowing them to be replaced or repaired in advance. It helps reduce maintenance costs, increase the shelf life of equipment, and improve productivity. Furthermore, as predictive maintenance provides safety compliance and preemptive corrective actions, its demand is increasing around the world
Predictive Maintenance Market Trends:
At present, the rising demand for predictive maintenance due to the increasing automation of operations of various industrial assets represents one of the primary factors influencing the market positively. Besides this, the growing utilization of machine-to-machine (M2M) communication and cloud technology to investigate a wide array of information derived from industrial and business assets is offering a favorable market outlook. Additionally, there is an increase in the adoption of predictive maintenance by technicians to plan and prepare for a repair by taking appropriate steps. This, along with the rising employment of predictive maintenance to prevent the disruption of production cycles and the occurrence of unplanned downtime, is propelling the growth of the market. Apart from this, there is a rise in the utilization of predictive maintenance by businesses to generate a tangible return on investment (ROI). This, coupled with the increasing investment in extending the lifespan of various aging industrial machinery, is contributing to the growth of the market. In addition, the rising integration of predictive maintenance with remote monitoring to conduct advanced inspections and predict component and equipment failures is supporting the market growth. Moreover, the increasing employment of predictive maintenance in the healthcare sector to improve the reliability of healthcare infrastructure is bolstering the market growth.Key Market Segmentation:
The publisher provides an analysis of the key trends in each sub-segment of the global predictive maintenance market report, along with forecasts at the global, regional and country level from 2023-2028. Our report has categorized the market based on component, technique, deployment type, organization size and industry vertical.Component Insights:
- Solution
- Service
Technique Insights:
- Vibration Monitoring
- Electrical Testing
- Oil Analysis
- Ultrasonic Leak Detectors
- Shock Pulse
- Infrared
- Others
Deployment Type Insights:
- Cloud-based
- On-premises
Organization Size Insights:
- Small and Medium-sized Enterprises
- Large Enterprises
Industry Vertical Insights:
- Manufacturing
- Energy and Utilities
- Aerospace and Defense
- Transportation and Logistics
- Government
- Healthcare
- Others
Regional Insights:
North America
- United States
- Canada
Asia-Pacific
- China
- Japan
- India
- South Korea
- Australia
- Indonesia
- Others
Europe
- Germany
- France
- United Kingdom
- Italy
- Spain
- Russia
- Others
Latin America
- Brazil
- Mexico
- Others
- Middle East and Africa
Competitive Landscape:
The report has also provided a comprehensive analysis of the competitive landscape in the global predictive maintenance market. Competitive analysis such as market structure, market share by key players, player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided. Some of the companies covered include Asystom, C3.ai Inc., General Electric Company, Google LLC (Alphabet Inc.), Hitachi Ltd., International Business Machines Corporation, Microsoft Corporation, PTC Inc., SAP SE, Software AG, Tibco Software Inc., Uptake Technologies Inc., etc. Kindly note that this only represents a partial list of companies, and the complete list has been provided in the report.Key Questions Answered in This Report
1. What was the size of the global predictive maintenance market in 2022?2. What is the expected growth rate of the global predictive maintenance market during 2023-2028?
3. What are the key factors driving the global predictive maintenance market?
4. What has been the impact of COVID-19 on the global predictive maintenance market?
5. What is the breakup of the global predictive maintenance market based on the component?
6. What is the breakup of the global predictive maintenance market based on the technique?
7. What is the breakup of the global predictive maintenance market based on deployment type?
8. What is the breakup of the global predictive maintenance market based on the organization size?
9. What is the breakup of the global predictive maintenance market based on the industry vertical?
10. What are the key regions in the global predictive maintenance market?
11. Who are the key players/companies in the global predictive maintenance market?
What is the estimated value of the Global Predictive Maintenance Market?
What is the growth rate of the Global Predictive Maintenance Market?
What is the forecasted size of the Global Predictive Maintenance Market?
Who are the key companies in the Global Predictive Maintenance Market?
Report Attribute | Details |
---|---|
No. of Pages | 142 |
Published | June 2023 |
Forecast Period | 2022 - 2028 |
Estimated Market Value ( USD | $ 8 Billion |
Forecasted Market Value ( USD | $ 35.9 Billion |
Compound Annual Growth Rate | 28.4% |
Regions Covered | Global |
No. of Companies Mentioned | 12 |
Table of Contents
Companies Mentioned
- Asystom
- C3.ai Inc.
- General Electric Company
- Google LLC (Alphabet Inc.)
- Hitachi Ltd.
- International Business Machines Corporation
- Microsoft Corporation
- PTC Inc.
- SAP SE
- Software AG
- Tibco Software Inc.
- Uptake Technologies Inc.
Methodology
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