The predictive maintenance market in APAC was valued at US$742.07 million in 2019 and is projected to reach US$2,524.93 million by 2027; it is expected to grow at a CAGR of 16.6% from 2020 to 2027.
Manufacturers adopt predictive maintenance based on machine learning. Testing different scenarios and predicting system errors depend on a large amount of historical or test data, along with tailor-made machine-learning algorithms; later alerts are generated accordingly. A machine learning algorithm will learn the behavior of the typical data, when adequately designed and implemented, and will identify deviations in real time. A machine monitoring system requires input on various parameters, such as temperature and engine velocity, to predict the breakdown time. When predictive maintenance is coupled with IIoT, it can identify faults in the equipment beforehand. With the emergence of Industry 4.0 in the manufacturing landscape, companies are keen to adopt IIoT to get better insights into their operations. Predictive maintenance relies on sensors to collect and analyze data from various sources, such as a CMMS and sensors to critical equipment. From power lines and machinery to power plants and maintenance vehicles, everything is equipped with sensors that collect time-stamped operating data. Therefore, growing focus on reducing downtime is supporting the growth of predictive maintenance market in APAC.
Based on component, the predictive maintenance market is segmented into solutions and services. The market for solution is further segmented into integrated and standalone software solutions. The demand for integrated solution segment is high owing to its increasing popularity and awareness for the same among several industry clusters. The growing need for single, multi-functional software makes integrated software more popular than standalone software. Standalone software lacks the scope of customization; however, the low cost this deployment types has led to wide deployment of these solutions in small and medium-sized enterprises.
China imposed strict lockdown and social isolation, which virtually halted the manufacturing of numerous equipment and machinery for several weeks, resulting in shrunken economy of the country. Further, the country also stopped its import as well as the export of critical raw materials and industrial equipment, impacting the supply chain of various end-user industries. Similarly, India also imposed a nationwide lockdown to mitigate the growing number of COVID-19 cases. As a result, the subsequent lockdown across China and India have hindered the predictive maintenance market growth in the region.
The overall APAC predictive maintenance market size has been derived using both primary and secondary sources. To begin the research process, exhaustive secondary research has been conducted using internal and external sources to obtain qualitative and quantitative information related to the predictive maintenance market. The process also serves the purpose of obtaining an overview and forecast for the APAC predictive maintenance market with respect to all the segments pertaining to the region. Also, multiple primary interviews have been conducted with industry participants and commentators to validate the data, as well as to gain more analytical insights into the topic. The participants who take part in such a process include industry expert such as VPs, business development managers, market intelligence managers, and national sales managers along with external consultants such as valuation experts, research analysts, and key opinion leaders specializing in the APAC predictive maintenance market. Hitachi, Ltd.; Software AG; IBM Corporation; Microsoft Corporation; PTC Inc.; Syncron AB; TIBCO Software Inc.; Schneider Electric; SAS; and General Electric Company are among a few players operating in the market in APAC.