By utilizing condition-monitoring processes and technologies, the effectiveness of the equipment is monitored during operation using the predictive maintenance technique. An engineer can use the recorded data to predict the asset’s eventual failure point and replace or repair it before it fails. Predictive maintenance lowers the likelihood of repairs while also avoiding unanticipated reactive maintenance, cutting equipment downtime, and lowering preventative maintenance costs.
The market worth of predictive maintenance in 2021 was USD 4.32 billion and will be worth USD 45.75 billion by 2030, growing at a 29.98% CAGR during 2021-2030.
The global predictive maintenance market is expanding due to factors such as the growing demand to boost asset uptime and cut costs, as well as rising investments in predictive maintenance due to IoT adoption. The predictive maintenance market will also increase due to the implementation of cutting-edge technologies like machine learning and the integration of IIoT.
Because managers in the manufacturing sector are becoming more aware of maintenance procedures and the need to minimize downtime, the predictive maintenance sector has been expanding steadily. Managers in the manufacturing industry are constantly looking for ways to enhance the maintenance procedures for the machinery in their production facilities and decrease errors and maximize advantages.
Predictive maintenance solutions are becoming increasingly effective and applicable to a wider number of industries thanks to ongoing improvements in information and communication technology and the increasing adoption of the internet of things and big data.
Skilled and trained employees are required to administer the most recent software systems in order to implement AI-based IoT technologies needed for predictive analytics. Regular staff must therefore receive training on how to use new and improved systems. Businesses also quickly embrace new technologies but struggle to find qualified, highly educated employees. The need for highly qualified staff is expanding as most international suppliers use predictive maintenance programs. Companies must become proficient in networking, application development, and cybersecurity.
By deployment, the on-premise segment held the majority of market with the largest share of 68%. The largest market share is due to how easily they may be enabled in a plant design, followed by the mode’s significantly cheaper capital expense. However, due to the rising acceptance of cloud-based technologies across industries, the cloud deployment mode would experience substantial expansion.
By component, the solution segment held the majority of the market with the largest share. The solutions market is expanding as it is crucial for forecasting equipment failure in the future. The design of solutions facilitates determining the root cause of equipment failure. The market will experience growth as more industries, including the banking and financial sector, industrial sector, health care sector, etc., embrace productive maintenance solutions.
By Vertical, the manufacturing segment held a maximum position in the market with 30% of revenue share aspredictive maintenance systems are most frequently used in this sector.
North America ruled the regional market. Major market players’ presence in the North American region will fuel this expansion. The market will rise due to the region’s increasing technological improvements or developments. In the North American region, more market participants are in predictive maintenance.
In the Asia Pacific area, the rising demand for maintenance solutions will expand steadily over the projection period. Due to their dependability and effectiveness, these solutions are used in numerous Asia Pacific nations like China, Japan, and India. The demand for predictive maintenance solutions will increase in this region due to the expansion of small- and medium-sized manufacturing businesses in developing countries like China and Japan.
- AVEVA Group plc
- Engineering Consultants Group, Inc.
- Expert Microsystems, Inc.,
- Asystom, C3.ai, Inc.,
- Axiomtek Co. Ltd
- C3 IoT
- Fiix Inc.
- General Electric
- IBM Corporation
- Hitachi Ltd
- Oracle Corporation
- Microsoft Corporation
- Operational Excellence (OPEX) Group Ltd
- SAP SE
- PTC Inc.
- SAS Institute
- Software AG
- Uptake Technologies Inc
- Schneider Electric
- Sigma Industrial Precision
- Spark Cognition
- TIBCO Software Inc
The market worth of predictive maintenance in 2021 was USD 4.32 billion and will be worth USD 45.75 billion by 2030, growing at a 29.98% CAGR during 2021-2030. The global market for predictive maintenance is expanding due to increasing IoT, AI, and machine learning use in large enterprises as well as a booming manufacturing industry worldwide. Additionally, the growing understanding of the advantages of predictive maintenance, such as the failure of any downtime and the low operational cost of electrical machinery and equipment, are significant drivers of the growth of the Predictive Maintenance market globally.