The global predictive maintenance market size is predicted to reach USD 6.27 billion in 2024 and USD 19.30 billion by 2029, growing at an interesting annual development rate (CAGR) of 25.2% over the conjecture period.
Predictive maintenance is a technique that allows analyzing and determining the status of any equipment in order to effectively estimate the turnaround time for maintenance performance. The effective use of the technique saves time and money as it assists in the effective detection of any pattern of failure. There are several advantages associated with predictive maintenance techniques, such as savings in production time and expense related to equipment parts and associated raw materials. Predictive maintenance technique also minimizes the total duration of maintenance and repair of industrial equipment. It can minimize various reliability and quality issues, along with the reduction in excess inventory.
Given the aggressive time constraints for various industrial products and services, it is important to identify the causes of potential failure before they have a chance to occur. Evolving technologies such as the Internet of Things (IoT), cloud storage, and big data analytics allow more industrial equipment and assembly robots to deliver conditional data, which makes troubleshooting easier and trains us.
The predictive maintenance market is segmented according to verticals. Vertical sectors include government and defense, manufacturing, transportation and logistics, energy and utilities, healthcare and life sciences, and others (agriculture, telecommunications, media, and retail). The energy and utilities segment is the fastest growing segment due to the increasing demand for energy consumption analysis applications. The ability of standalone solutions to identify asset monitoring issues up front and make repairs that minimize disruptions to power generation would spur market growth.
The cloud deployment model is determined to record rapid growth during the anticipated period.
Most providers in the predictive maintenance market offer cloud-based maintenance solutions to maximize benefits and effectively automate the equipment maintenance process. Adoption of cloud-based predictive maintenance solutions is expected to grow, primarily due to their benefits, such as ease of maintenance of generated data, cost-effectiveness, scalability, and efficient management.
With the increasing consumer awareness related to the augmenting maintenance costs and downtime due to abnormal machine failures, the call for predictive maintenance solutions is multiplying globally. Predictive maintenance solutions help companies identify patterns in constant data streams to predict equipment failure.
Trained workers are needed to manage the latest software systems and implement AI-based IoT technologies and skills. Therefore, existing workers must receive training on how to operate new and improved systems. Furthermore, industries are dynamic in their adoption of new technologies.
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REPORT METRIC |
DETAILS |
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Market Size Available |
2023 to 2029 |
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Base Year |
2023 |
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Forecast Period |
2024 to 2029 |
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CAGR |
25.2% |
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Segments Covered |
By Component, Vertical, Deployment Mode, Organization Size, and Region |
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Various Analyses Covered |
Global, Regional & Country Level Analysis, Segment-Level Analysis, DROC, PESTLE Analysis, Porter’s Five Forces Analysis, Competitive Landscape, Analyst Overview on Investment Opportunities |
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Regions Covered |
North America, Europe, APAC, Latin America, Middle East & Africa |
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Market Leaders Profiled |
IBM (United States), Microsoft (United States), SAP (Germany), Hitachi (Japan), PTC (United States), GE (United States), Schneider Electric (France), Software AG (Germany), SAS (United States), TIBCO (United States), C3 IoT (United States), Uptake (United States), Softweb Solutions (United States), Asystom (France), Ecolibrium Energy (India), Fiix Software (Canada), OPEX Group (United Kingdom), Dingo (Australia), Sigma Industrial Precision (Spain), Google (USA), Oracle (USA), HPE (USA), AWS (USA), Micro Focus (UK), Splunk (USA), Altair (USA), RapidMiner (USA) and Seebo (Israel) and Others. |
Based on the components, the global predictive maintenance market can be divided into solutions and services.
In terms of vertical, the market can be classified into government and defense, manufacturing, energy and utilities, oil and gas, and transportation and logistics. Among vertical sectors, the energy and utilities segment is expected to develop with a substantial CAGR during the forecast period.
Based on the deployment mode, the global market is bifurcated into cloud and on-premises. The cloud segment is developing at an exponential rate and will record the highest market portion in the future.
The global market is separated into large enterprises and small and medium enterprises (SMEs).
Depending on the region, the global predictive maintenance market can be classified as North America, Europe, Asia-Pacific, the Middle East, Africa, and South America. Of these, North America, with its early adoption of technology and key players, is leading the global market.
The players operating in the market are analytics providers, software platform providers, and software as a service (SaaS) providers. These players continually invest in research and development (R&D) to provide the most affordable and comprehensive solutions to end-user industries. The companies in this business are implementing several organic and inorganic growth strategies, like new products, updates, partnerships, business expansions, and mergers and acquisitions, in order to strengthen their offerings.
The main providers of the global predictive maintenance market are IBM (United States), Microsoft (United States), SAP (Germany), Hitachi (Japan), PTC (United States), GE (United States), Schneider Electric (France), Software AG (Germany), SAS (United States), TIBCO (United States), C3 IoT (United States), Uptake (United States), Softweb Solutions (United States), Asystom (France), Ecolibrium Energy (India), Fiix Software (Canada), OPEX Group (United Kingdom), Dingo (Australia), Sigma Industrial Precision (Spain), Google (USA), Oracle (USA), HPE (USA), AWS (USA), Micro Focus (UK), Splunk (USA), Altair (USA), RapidMiner (USA) and Seebo (Israel).
By Component
By Deployment Mode
By Organization Size
By Vertical
By Region
Frequently Asked Questions
The primary industries include manufacturing, energy and utilities, transportation, healthcare, and aerospace. These sectors heavily rely on machinery and equipment, making them prime candidates for predictive maintenance to ensure operational efficiency and minimize unexpected downtime.
The key technologies include IoT sensors, AI, machine learning algorithms, cloud computing, and big data analytics. IoT sensors collect real-time data from equipment, which is then analyzed using AI and machine learning to predict potential failures. Cloud computing provides the infrastructure for storing and processing large amounts of data.
Predictive maintenance contributes to sustainability by reducing waste, minimizing energy consumption, and extending the lifespan of equipment. By preventing unexpected failures, it also reduces the environmental impact associated with manufacturing and disposing of parts and equipment. This aligns with global efforts towards more sustainable industrial practices.
Future trends include the increasing use of AI and machine learning for more accurate predictions, the integration of augmented reality (AR) for maintenance tasks, the growth of edge computing to process data closer to the source, and the development of more user-friendly and accessible predictive maintenance platforms. Additionally, there will be a greater emphasis on cybersecurity to protect sensitive industrial data.
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