Predictive Maintenance Market by Component (Hardware, Solution (Deployment Mode), & Services), Technology, Technique (Vibration Analysis, Infrared Thermography, Motor Circuit Analysis), Organization Size, Vertical, & Region - Global Forecast to 2029

Predictive Maintenance Market by Component (Hardware, Solution (Deployment Mode), & Services), Technology, Technique (Vibration Analysis, Infrared Thermography, Motor Circuit Analysis), Organization Size, Vertical, & Region - Global Forecast to 2029

The global predictive maintenance market is valued at USD 10.6 billion in 2024 and is estimated to reach USD 47.8 billion in 2029, registering a CAGR of 35.1% during the forecast period. The continuous advancements in big data, Machine-to-Machine (M2M) communication, and cloud technology have opened up new possibilities for analyzing information derived from industrial assets. IoT devices generate vast amounts of data from diverse sources like sensors, cameras, and interconnected devices. However, this data alone lacks value until it is transformed into actionable, context-rich information. Big data analytics and data visualization techniques empower users to uncover fresh insights through batch processing and offline analysis. While real-time data analysis and decision-making are often manual, automating these processes is preferred for scalability. AI technology plays a crucial role in analyzing the extensive data volumes generated by various components of the IoT ecosystem and converting them into meaningful insights. Enterprises are integrating AI into their established analytical frameworks to automate data interpretation and gain real-time insights from IoT-generated data. AI equips enterprises with frameworks and tools to analyze real-time data and discover multiple use cases for IoT applications.

“By component, the solution segment is projected to hold the largest market size during the forecast period.”

Predictive maintenance solutions have emerged as indispensable components for numerous successful and large enterprises, given the challenges they face in monitoring asset conditions using traditional maintenance methods. Moreover, these solutions offer real-time asset monitoring, a significant advancement over rule-based maintenance approaches, allowing organizations to adopt proactive maintenance strategies that reduce costs, minimize downtime, and optimize operational maintenance processes. The solutions market is categorized into standalone and integrated offerings, with integrated solutions experiencing substantial demand due to their comprehensive functionality and widespread adoption across diverse sectors and regions. Integrated software, offering both single and multi-functional capabilities, has gained popularity over standalone software, which lacks customization capabilities but remains prevalent in small and mid-sized enterprises due to its affordability.

“By Technology, Artificial Intelligence is registered to grow at the highest CAGR during the forecast period.”

AI technology plays a significant role in predictive maintenance by leveraging advanced algorithms and machine learning models to analyze data and predict potential equipment failures before they occur. This technology enables organizations to move from reactive maintenance to proactive and even predictive maintenance strategies, ultimately improving asset reliability, reducing downtime, and optimizing maintenance costs. Examples of AI technology in predictive maintenance include the use of predictive modeling techniques such as regression analysis, decision trees, and neural networks to forecast equipment failures based on historical data patterns. AI-powered anomaly detection algorithms can also identify abnormal behavior in real-time sensor data, allowing for timely intervention and preventive maintenance actions. Additionally, AI-driven condition monitoring systems utilizing IoT sensors can continuously monitor equipment health and performance metrics, providing early warnings of potential issues and enabling predictive maintenance scheduling. These AI technologies empower organizations to transform their maintenance practices, enhance operational efficiency, and drive better business outcomes.

“Asia Pacific is projected to witness the highest CAGR during the forecast period.”

Asia Pacific has witnessed advanced and dynamic adoption of new technologies and is expected to record the highest CAGR during the forecast period. The commercialization of IoT technology and the need for further advancements to leverage the technology to the best are expected to drive the adoption of predictive maintenance solutions in the region. The region includes major economies, such as China and Japan, which are expected to register high growth in the predictive maintenance market. Verticals, such as energy and utilities, transportation and logistics, and healthcare and life sciences, are expected to adopt predictive maintenance solutions and services at the highest rate in the region. Companies operating in Asia Pacific would benefit from the flexible economic conditions, industrialization- and globalization-motivated policies of the governments, as well as from the growing digitalization, which is expected to have a huge impact on the business community. Other countries in the region, such as South Korea, Singapore, Hong Kong, and Malaysia, are exploring ways to integrate predictive maintenance solutions and services. The countries considered for the analysis of the Asian predictive maintenance market are China, Japan, India, Bangladesh, and the rest of Asia Pacific.

Breakdown of primaries

In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the predictive maintenance market.

By Company: Tier I: 34%, Tier II: 43%, and Tier III: 23%

By Designation: C-Level Executives: 50%, Directors: 30%, and Others: 20%

By Region: North America: 25%, Europe: 30%, Asia Pacific: 30%, Middle East & Africa: 10%, Latin America: 5%

Major vendors offering predictive maintenance hardware, solution and services across the globe are IBM (US), ABB (Switzerland), Schneider Electric (France), AWS (US), Google (US), Microsoft (US), Hitachi (Japan), SAP (Germany), SAS Institute (US), Software AG (Germany), TIBCO Software (US), Altair (US), Oracle (US), Splunk (US), C3.ai (US), Emerson (US), GE (US), Honeywell (US), Siemens (Germany), PTC (US), Dingo (Australia), Uptake (US), Samotics (Netherlands), WaveScan (Singapore), Quadrical Ai (Canada), UpKeep (US), Limble (US), SenseGrow (US), Presage Insights (India), Falcon Labs (India).

Research Coverage

The market study covers predictive maintenance across segments. It aims at estimating the market size and the growth potential across different segments, such as component (hardware, solution [by deployment mode] & services), technology, technique, organization size, vertical and region. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.

Key Benefits of Buying the Report

The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall market for predictive maintenance and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.

The report provides insights on the following pointers:

Analysis of key drivers (Increasing use of emerging technologies to gain valuable insights, Advent of ML and AI, Growing need to reduce maintenance cost, equipment failure, and downtime), restraints (Lack of skilled workforce, Data security concerns), opportunities (Rising internet proliferation and growing usage of connected and integrated technologies, Real-time condition monitoring to assist in taking prompt actions), and challenges (Frequent maintenance and upgradation requirement to keep the systems updated, Ownership and privacy of collected data) influencing the growth of the predictive maintenance market

Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the predictive maintenance market.

Market Development: Comprehensive information about lucrative markets – the report analyses the predictive maintenance market across varied regions.

Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in predictive maintenance market strategies; the report also helps stakeholders understand the pulse of the predictive maintenance market and provides them with information on key market drivers, restraints, challenges, and opportunities.

Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players such as IBM (US), ABB (Switzerland), Schneider Electric (France), AWS (US), Google (US) among others in the predictive maintenance market.