A predictive maintenance software system is utilized to monitor and analyze the performance and condition of machines and equipment while they are in operation. With the employment of advanced techniques, this software system enables the maintenance of machinery to be carried out proactively, preventing failures before they occur. The predictive maintenance software system has diverse applications, including detecting motor efficiency variations, identifying three-phase power imbalances caused by harmonic distortion, and detecting excessive heat generated by faulty bearings.
According to Data Bridge Market Research the Predictive Maintenance Market accounted for USD 3,923.85 million in 2022, and expected to reach USD 60,608.62 million by 2030. The market is expected to grow with a CAGR of 40.80% in the forecast period of 2023 to 2030.
“Growing demand to reduce equipment failure, maintenance costs, and downtime”
The growth of the predictive maintenance market is driven by the rising need to minimize equipment failure, maintenance expenses, and downtime. Equipment downtime refers to the period when specific equipment is not operational due to unforeseen equipment failures. Such unplanned downtime and frequent equipment failures of large machinery impede business operations, leading to temporary production halts, financial penalties, wasted staff time, and other detrimental effects. Consequently, the increasing demand for reducing equipment failure, maintenance costs, and downtime is expected to drive the adoption of predictive maintenance solutions in the forecast period.
What restraints the growth of the predictive maintenance market?
“High requirement for regular maintenance and upgradation to keep the systems updated”
Predictive maintenance systems have a significant need for regular maintenance and upgrades. Due to the dynamic nature of industrial processes and evolving technologies, these systems require ongoing attention to ensure optimal performance. Regular maintenance activities, such as sensor calibration, data validation, and software updates, are necessary to maintain accuracy and reliability. Additionally, periodic system upgrades are essential to incorporate new algorithms, enhance data analytics capabilities, and stay up to date with emerging trends in predictive maintenance practices.
Segmentation: North America Predictive Maintenance Market
The predictive maintenance market is segmented on the basis of components, deployment mode, system integration, organization size, vertical and stakeholder.
- On the basis of components, the predictive maintenance market is segmented into solutions, integrated, standalone, service, system integration, support and maintenance, consulting.
- On the basis of deployment mode, the predictive maintenance market is segmented into on-premises, cloud.
- On the basis of system integration, the predictive maintenance market is segmented into support and maintenance, consulting.
- On the basis of organization size, the predictive maintenance market is segmented into large enterprises, small and medium-sized enterprises (SMEs).
- On the basis of vertical, the predictive maintenance market is segmented into government and defence, manufacturing, energy and utilities, transportation and logistics, healthcare and life sciences.
- On the basis of stakeholder, the predictive maintenance market is segmented into MRO, OEM/ODM, technology integrators.
Regional Insights: U.S. dominates the Predictive Maintenance Market
The U.S. dominates the predictive maintenance market because of growing investments in emerging technologies such as machine learning, IoT, and artificial intelligence, which enhances this region's solution and service segments. Furthermore, the rising adoption of predictive maintenance by the banking and IT and telecom industries will further grow this region's market.
To know more about the study visit, https://www.databridgemarketresearch.com/ko/reports/north-america-predictive-maintenance-market
Recent Developments
- In 2022, Siemens, a Germany-based technology company focused on transport, healthcare, industry, and infrastructure, acquired Senseye for an undisclosed amount. With this acquisition, Senseye became a subsidiary of Siemens and is expected to strengthen its position in the digital services portfolio.
The Prominent Key Players Operating in the Predictive Maintenance Market Include:
- Microsoft (U.S.)
- IBM (U.S.)
- SAP (Germany)
- SAS Institute Inc. (U.S.)
- Software AG (Germany)
- Cloud Software Group, Inc. (U.S.)
- Hewlett Packard Enterprise Development LP (U.S.)
- Altair Engineering Inc. (U.S.)
- Splunk Inc. (U.S.)
- Oracle (U.S.)
- Google (U.S.)
- Amazon Web Services, Inc. (U.S.)
- General Electric (U.S.)
- Schneider Electric (France)
- Hitachi, Ltd. (Japan)
- PTC (U.S.)
- RapidMiner (U.S)
- Operational Excellence (OPEX) Group Ltd, (U.K.)
- DINGO Software Pty. Ltd. (Australia)
- CHIRON Swiss SA (Russia)
Above are the key players covered in the report, to know about more and exhaustive list of predictive maintenance market companies contact, https://www.databridgemarketresearch.com/ko/contact
Research Methodology: North America Predictive Maintenance Market
Data collection and base year analysis are done using data collection modules with large sample sizes. The market data is analyzed and estimated using market statistical and coherent models. In addition, market share analysis and key trend analysis are the major success factors in the market report. The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, North America vs Regional and Vendor Share Analysis. Please request analyst call in case of further inquiry.
