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The industrial predictive maintenance market share in APAC is expected to increase by USD 7.44 billion from 2021 to 2026, and the market's growth momentum will accelerate at a CAGR of 34.71%.
This industrial predictive maintenance market in APAC research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers the industrial predictive maintenance market in APAC segmentation by End-user (oil and gas, chemical and petrochemical, aerospace and defense, power generation, and others), deployment (cloud and on-premises), and geography (China, Japan, India, and Rest of APAC). The industrial predictive maintenance market in APAC report also offers information on several market vendors, including General Electric Co., Huawei Investment and Holding Co. Ltd., International Business Machines Corp., Oracle Corp., Robert Bosch GmbH, SAP SE, SAS Institute Inc., Siemens AG, Splunk Inc., and TIBCO Software Inc. among others.
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The developments in customized industrial predictive maintenance is notably driving the industrial predictive maintenance market in APAC, although factors such as low investments in the latest machinery and measuring equipment may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the industrial predictive maintenance industry in APAC. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.
Key Industrial Predictive Maintenance Market Driver in APAC
One of the key factors driving the global industrial predictive maintenance market growth is the developments in customized industrial predictive maintenance. Countries such as China, Japan, and South Korea are leading the automation industry in the region, which is creating opportunities for the growth of the industrial predictive maintenance market in APAC. The rise in the adoption of advanced technologies, such as IoT, industrial IoT (IIoT), AI, and big data, as well as investments in improving product quality and production assets in APAC, are expected to lead to the increased adoption of industrial predictive maintenance during the forecast period. Therefore, vendors such as SAP SE, International Business Machines Corp., and Oracle Corp. provide custom-made industrial predictive maintenance solutions and services based on the needs of specific end-users, which will protect their critical equipment and enable them to gain a competitive edge in productivity.
Key Industrial Predictive Maintenance Market Trend in APAC
Shift from reactive to predictive maintenance is one of the key industrial predictive maintenance market trends that is expected to impact the industry positively in the forecast period. The integration of business information along with sensor data and enterprise asset management (EAM) systems is allowing end-user industries to move away from reactive and shift to predictive maintenance services and solutions. The development of IoT solutions that use real-time machinery data to determine the operational efficiency and condition of the equipment, with the support of sophisticated analytics, helps to predict failures early, unlike preventive maintenance. The disadvantages associated with preventive maintenance are the key factors for the shift to predictive maintenance, as preventive maintenance does not prevent catastrophic failures, is labor-intensive, and needs unnecessary maintenance, which causes damage to equipment and components. Such factors will further support the market growth during the forecast years.
Key Industrial Predictive Maintenance Market Challenge in APAC
One of the key challenges to the global industrial predictive maintenance market growth is the low investments in the latest machinery and measuring equipment. Industrial predictive maintenance requires that the software solutions and services exhibit better performance and have a better impact on industrial assets and production. In addition, there are difficulties in retrofitting existing and older industrial machinery with sensors and monitoring equipment. End-user industries such as oil and gas, chemical and petrochemical, and power generation generally still operate using older machinery, which will, in turn, hamper the adoption of predictive maintenance solutions and services. Moreover, the adoption of industrial predictive maintenance is currently low in developing countries such as India, China, Indonesia, and Malaysia due to the lack of awareness. Hence, low investments may hinder the market growth during the forecast period.
This industrial predictive maintenance market in APAC analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2022-2026.
Technavio categorizes the industrial predictive maintenance market in APAC as a part of the global industrial machinery market. Our research report has extensively covered external factors influencing the parent market growth potential in the coming years, which will determine the levels of growth of the industrial predictive maintenance market in APAC during the forecast period.
The report analyzes the market's competitive landscape and offers information on several market vendors, including:
This statistical study of the industrial predictive maintenance market in APAC encompasses successful business strategies deployed by the key vendors. The industrial predictive maintenance market in APAC is fragmented and the vendors are deploying growth strategies such as the expansion of product portfolios to compete in the market.
To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.
The industrial predictive maintenance market in APAC forecast report offers in-depth insights into key vendor profiles. The profiles include information on the production, sustainability, and prospects of the leading companies.
Our report provides extensive information on the value chain analysis for the industrial predictive maintenance market in APAC, which vendors can leverage to gain a competitive advantage during the forecast period. The end-to-end understanding of the value chain is essential in profit margin optimization and evaluation of business strategies. The data available in our value chain analysis segment can help vendors drive costs and enhance customer services during the forecast period.
The value chain of the global industrial machinery market includes the following core components:
The report has further elucidated on other innovative approaches being followed by manufacturers to ensure a sustainable market presence.
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43% of the market's growth will originate from China during the forecast period. Market growth in China will be faster than the growth of the market in Japan.
Rapid growth in IoT applications and rising opportunities for research and development (R&D) will put the above end-user industries in a superior position in China, which will facilitate the industrial predictive maintenance market growth in China over the forecast period. This market research report entails detailed information on the competitive intelligence, marketing gaps, and regional opportunities in store for vendors, which will assist in creating efficient business plans.
COVID Impact and Recovery Analysis
The outbreak of COVID-19 had a negative impact on the industrial predictive maintenance market in APAC in 2020. The pandemic resulted in numerous deaths in China, prompting stringent nationwide lockdowns in the country. This had a significant impact on various industries that use industrial predictive maintenance solutions in the country as they were compelled to shut down temporarily. However, in 2021, due to the availability of COVID-19 vaccines and the large-scale vaccination drives carried out by the government, industrial activities gradually resumed and reached normal levels, which will lead to the growth of the market in China during the forecast period.
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The industrial predictive maintenance market share growth in APAC by the oil and gas segment will be significant during the forecast period. The increasing awareness of various industrial operations among end-user industries will have a significant impact on the growth of the industrial predictive maintenance market in the oil and gas industry in APAC during the forecast years.
This report provides an accurate prediction of the contribution of all the segments to the growth of the industrial predictive maintenance market size in APAC and actionable market insights on post COVID-19 impact on each segment.
Industrial Predictive Maintenance Market Scope in APAC |
|
Report Coverage |
Details |
Page number |
120 |
Base year |
2021 |
Forecast period |
2022-2026 |
Growth momentum & CAGR |
Accelerate at a CAGR of 34.71% |
Market growth 2022-2026 |
$ 7.44 billion |
Market structure |
Fragmented |
YoY growth (%) |
24.46 |
Regional analysis |
APAC |
Performing market contribution |
China at 43% |
Key consumer countries |
China, Japan, India, and Rest of APAC |
Competitive landscape |
Leading companies, Competitive strategies, Consumer engagement scope |
Key companies profiled |
General Electric Co., Huawei Investment and Holding Co. Ltd., International Business Machines Corp., Oracle Corp., Robert Bosch GmbH, SAP SE, SAS Institute Inc., Siemens AG, Splunk Inc., and TIBCO Software Inc. |
Market dynamics |
Parent market analysis, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID 19 impact and recovery analysis and future consumer dynamics, Market condition analysis for forecast period |
Customization purview |
If our report has not included the data that you are looking for, you can reach out to our analysts and get segments customized. |
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