Asia-Pacific Predictive Maintenance Market, By Components (Solution, Services), Deployment Mode (Cloud, On-Premise), Organisation Size (Large Enterprises, Small and Medium-Sized Enterprises), Vertical (Manufacturing, Energy and Utilities, Transportation, Government, Healthcare, Aerospace and Defense, Others), Stakeholder (MRO, OEM/ODM, Technology Integrators) – Industry Trends and Forecast to 2029.
Asia-Pacific Predictive Maintenance Market Analysis and Size
Several firms are currently launching next-generation, end-to-end cloud-based platforms. The increased application of new and emerging technologies to gain valuable insight into decision-making has aided industry growth. Various vertical end-users are increasingly seeking cost savings and downtime, which has fuelled market growth.
Data Bridge Market Research analyses that the predictive maintenance market was valued at 1.73 billion in 2021 and is expected to reach the value of USD 7.59 billion by 2029, at a CAGR of 20.3% during the forecast period. In addition to the market insights such as market value, growth rate, market segments, geographical coverage, market players, and market scenario, the market report curated by the Data Bridge Market Research team includes in-depth expert analysis, import/export analysis, pricing analysis, production consumption analysis, and pestle analysis.
Asia-Pacific Predictive Maintenance Market Scope and Segmentation
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Report Metric |
Details |
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Forecast Period |
2022 to 2029 |
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Base Year |
2021 |
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Historic Years |
2020 (Customizable to 2014 - 2019) |
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Quantitative Units |
Revenue in USD billion, Volumes in Units, Pricing in USD |
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Segments Covered |
Components (Solution, Services), Deployment Mode (Cloud, On-Premise), Organisation Size (Large Enterprises, Small and Medium-Sized Enterprises), Vertical (Manufacturing, Energy and Utilities, Transportation, Government, Healthcare, Aerospace and Defense, Others), Stakeholder (MRO, OEM/ODM, Technology Integrators) |
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Countries Covered |
Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific |
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Market Players Covered |
Microsoft (U.S.), IBM Corporation (U.S.), SAP SE (Germany), SAS AG (Germany), TIBCO Software Inc. (U.S.), Hewlett Packard Enterprise Development LP (U.S.), Altair Engineering Inc. (U.S.), Splunk Inc. (U.S.), Oracle (U.S.), Google LLC (U.S.), Amazon Web Services, Inc. (U.S.), General Electric (U.S.), Schneider Electric (France), Hitachi, Ltd. (Japan), PTC (U.S.), RapidMiner, Inc. (U.S.), Operational Excellence (OPEX) Group Ltd, (U.K.), Dingo (Australia), Factory5 (Russia) |
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Opportunities |
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Market Definition
A predictive maintenance software system is used to monitor any instrumentation or machine's performance and condition while in operation. The software system monitors the instrumentation using advanced techniques, allowing the machinery to be maintained on a regular basis before any failure occurs. The predictive maintenance software system has found use in a variety of fields, including the detection of three-phase power imbalances caused by harmonic distortion, the identification of distinct motor electrical phenomenon spikes, and the detection of heating caused by dangerous bearings.
Predictive Maintenance Market Dynamics
- DriversExpansion of small and medium sized industries
One of the major factors driving market growth is the increasing number of small and medium-sized businesses around the world. In other words, an increase in the number of banking, financial services, and insurance (BFSI), government and public sector, healthcare and life sciences, manufacturing, retail and e-commerce, telecommunications, and information technology (IT) industries is directly influencing market growth.
- Advancements in futuristic technologies
Constant advancements in big data, machine-to-machine (M2M) communication, and artificial intelligence have opened up new avenues for the dissemination of information derived from artificial means. IoT bias generates massive data from various sources, such as detectors, cameras, and other connected bias. However, the data has no value unless converted into actionable, contextual information. Big data and data visualization methods allow pharmacists to gain new perspectives through batch processing and offline analysis. Real-time data analysis and decision-making are frequently performed manually; however, to make it scalable, it is preferable to be performed automatically.
Opportunities
- Advanced machine language operations
Across nearly every perpendicular, advanced asset operation is becoming less desirable. As a result of connected bias, result providers equipped with AI and ML can collect and transform a vast quantum of client-related data into meaningful perceptivity. AI can also be combined with IoT bias to optimize various aspects of service delivery, such as predictive conservation and quality assessment, without the need for human intervention.
Restraints
- Lack of skilled workers
Trained workers must manage the most recent software systems to implement AI-based IoT technologies and skill sets. As a result, workers must be trained on how to operate new and upgraded systems. Furthermore, diligence are dynamic in their embrace of new technologies. However, they face a shortage of largely professed pool and complete workers. As the majority of global merchants organize prophetic conservation systems, the demand for a broadly professed pool grows. Companies must develop grit in areas such as cybersecurity, networking, and operations.
This predictive maintenance market report provides details of new recent developments, trade regulations, import-export analysis, production analysis, value chain optimization, market share, impact of domestic and localized market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on the predictive maintenance market contact Data Bridge Market Research for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.
COVID-19 Impact on Predictive Maintenance Market
COVID - 19 has fundamentally altered the dynamics of business operations. Though the COVID - 19 outbreak has shed light on flaws in business models across verticals, it has also provided several opportunities for businesses to digitalize and expand across borders as the abandonment and integration of technologies such as AI, analytics, IoT, and blockchain has increased during the lockdown period. During the first and alternate diggings of 2020, the retail and manufacturing sectors saw significant drops in business performance. Nonetheless, with the availability of vaccines and significant control of the epidemic, these sectors are expected to see increased investment throughout the cast period as prophetic conservation results rise in elevation across various business functions.
Recent Development
- In May 2021 SAS Institute launched its SAS Viya platform to help lay the groundwork for data and logical success by incorporating new data operation results into its critical, pall native SASViya platform.
Asia-Pacific Predictive Maintenance Market Scope
The predictive maintenance market is segmented on the basis of components, deployment mode, organization size, vertical, and stakeholder. The growth amongst these segments will help you analyze meagre growth segments in the industries and provide the users with a valuable market overview and market insights to help them make strategic decisions for identifying core market applications.
Components
- Solution
- Services
Deployment Mode
- Cloud
- On-Premise
Organization Size
- Large Enterprises
- Small and Medium-Sized Enterprises
Vertical
- Manufacturing
- Energy and Utilities
- Transportation
- Government
- Healthcare
- Aerospace
- Defense
- Others
Stakeholder
- MRO
- OEM/ODM
- Technology Integrators
Predictive Maintenance Market Regional Analysis/Insights
The predictive maintenance market is analyzed and market size insights and trends are provided by country, component, deployment mode, organization size, vertical, and stakeholder as referenced above.
The countries covered in the predictive maintenance market report are Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, and Rest of Asia-Pacific.
China is the dominant region because of the increasing use of smart sensors and on-board electronics that can communicate via cloud-based analytics systems, the product vendor is able to assess the working condition and service requirements of equipment in advance.
The country section of the report also provides individual market impacting factors and changes in market regulation that impact the current and future trends of the market. Data points like down-stream and upstream value chain analysis, technical trends and porter's five forces analysis, case studies are some of the pointers used to forecast the market scenario for individual countries. Also, the presence and availability of Asia-Pacific brands and their challenges faced due to large or scarce competition from local and domestic brands, impact of domestic tariffs and trade routes are considered while providing forecast analysis of the country data.
Competitive Landscape and Predictive Maintenance Market Share Analysis
The predictive maintenance market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, Asia-Pacific presence, production sites and facilities, production capacities, company strengths and weaknesses, product launch, product width and breadth, application dominance. The above data points provided are only related to the companies' focus related to predictive maintenance market.
Some of the major players operating in the predictive maintenance market are:
- Microsoft (U.S.)
- IBM Corporation (U.S.)
- SAP SE (Germany)
- SAS AG (Germany)
- TIBCO Software Inc. (U.S.)
- Hewlett Packard Enterprise Development LP (U.S.)
- Altair Engineering Inc. (U.S.)
- Splunk Inc. (U.S.)
- Oracle (U.S.)
- Google LLC (U.S.)
- Amazon Web Services, Inc. (U.S.)
- General Electric (U.S.)
- Schneider Electric (France)
- Hitachi, Ltd. (Japan)
- PTC (U.S.)
- RapidMiner, Inc. (U.S.)
- Operational Excellence (OPEX) Group Ltd, (U.K.)
- Dingo (Australia)
- Factory5 (Russia)
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