Predictive Maintenance Market
Predictive Maintenance Market by Monitoring Infrastructure (Sensors & Sensing, Imaging & Inspection Devices, Edge Monitoring, Connectivity Hardware), Software (APM, IIoT, Digital Twin, AI-driven Predictive Maintenance Platforms) - Global Forecast to 2031
OVERVIEW
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
The predictive maintenance market is expected to grow from USD 9.71 billion in 2026 to USD 16.74 billion by 2031, at a CAGR of 11.5%. This growth is linked to the wider use of connected equipment and monitoring systems in industrial environments. Machines and production infrastructure generate operational data that can be reviewed to understand equipment condition and performance. Predictive maintenance solutions use this data to detect possible faults before equipment fails. As a result, industries such as manufacturing, energy, transportation, and utilities are adopting these tools to reduce downtime and maintain stable operations.
KEY TAKEAWAYS
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BY REGIONNorth America is expected to hold the largest market share of 29.0% in 2026.
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BY OFFERINGMonitoring infrastructure is expected to have the largest market share in 2026.
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BY TECHNOLOGYEdge computing & edge AI is positioned to showcase the highest growth rate of 14.2% during the forecast period.
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BY VERTICALThe manufacturing segment is projected to hold the largest market share of 21.2% in 2026.
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COMPETITIVE LANDSCAPE - KEY PLAYERSABB, AWS, and Schneider Electric are identified as some of the leading players in the predictive maintenance market, given their strong market share and product footprint.
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COMPETITIVE LANDSCAPE - STARTUPS/SMEsDingo, Uptake, and Samotics, among others, have distinguished themselves among other players by securing strong footholds in specialized niche areas, underscoring their potential as emerging leaders.
Technology vendors continue to expand predictive maintenance platforms and monitoring solutions. These systems combine sensors, monitoring devices, and analytics software to track equipment condition. Vendors are also adding machine learning and automation features to improve fault detection and maintenance planning. As industrial environments become more connected, companies are also focusing on system reliability and secure device connectivity.
TRENDS & DISRUPTIONS IMPACTING CUSTOMERS' CUSTOMERS
Predictive maintenance has developed gradually over the years. In the past, maintenance activities mainly depended on routine inspections or repairs after equipment failure. Today, many organizations use connected monitoring systems that collect real-time data from machines. Maintenance teams review this data to track equipment condition and detect potential faults early. Industrial data platforms and cloud systems are commonly used to store and analyze this operational information.
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
MARKET DYNAMICS
Level
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Increasing need to reduce equipment downtime and maintenance costs
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Increasing adoption of IoT-enabled equipment monitoring in industrial operations
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High implementation and infrastructure setup costs
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Data management and integration challenges across multiple equipment systems
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Growing adoption of edge computing for faster equipment data processing
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Growing use of AI and machine learning for predictive maintenance analytics
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Integration of predictive maintenance solutions with legacy industrial systems
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Ensuring data accuracy and reliability for predictive maintenance models
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
Driver: Increasing need to reduce equipment downtime and maintenance costs
Companies adopt predictive maintenance mainly to lower equipment downtime and maintenance expenses. When machines fail unexpectedly, production can stop, and operating costs rise. Predictive maintenance uses machine data to spot signs of wear or possible faults before a breakdown happens. This allows maintenance teams to plan repairs ahead of time and keep equipment running more reliably.
Restraint: High implementation and infrastructure setup costs
The cost of implementation can slow adoption in some organizations. Setting up sensors, monitoring infrastructure, and analytics tools requires initial investment. Some facilities may also need to modify existing machines or add monitoring devices to collect the required data. These expenses can be challenging for small and medium-sized organizations, particularly in plants that operate many machines.
Opportunity: Growing adoption of edge computing for faster equipment data processing
Edge computing and edge AI are creating new opportunities for predictive maintenance. Industrial environments increasingly process equipment data close to where it is generated. Edge systems allow data to be analyzed locally on devices or gateways rather than sending all information to centralized platforms. This approach supports faster fault detection and quicker response from maintenance teams.
Challenge: Integration of predictive maintenance solutions with legacy industrial systems
Integration with existing industrial systems can make predictive maintenance projects difficult in some facilities. Many factories still run older machines along with different control systems. Linking these systems with modern monitoring platforms may require system modifications or upgrades. Companies may also need technical staff who can manage the tools and interpret maintenance data.
PREDICTIVE MAINTENANCE MARKET: COMMERCIAL USE CASES ACROSS INDUSTRIES
| COMPANY | USE CASE DESCRIPTION | BENEFITS |
|---|---|---|
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Riyadh Airports Company updated maintenance operations at King Khalid International Airport using IBM Maximo and IBM Cognos Analytics. The system replaced manual processes with digital workflows and allowed real-time tracking of assets and work orders. | Maintenance paperwork fell by more than 80%, dashboards improved operational visibility, contractor onboarding time dropped from about 10 days to 2–3 days, and inspector productivity increased by over 40%. The system also helped maintenance teams plan work earlier and reduce downtime. |
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VPI installed the IBM Maximo Application Suite across its combined cycle gas turbine power plants to manage equipment and maintenance activities. The system placed asset records and maintenance tasks on one platform and added predictive maintenance functions. It also connected maintenance information with compliance processes used at the plants. | With visibility across nearly 60,000 assets, VPI improved equipment reliability, reduced manual administrative work, and supported safer and more efficient plant operations. |
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Western Digital introduced SAS Asset Performance Analytics in its manufacturing facilities to track equipment performance. The system reviews machine and sensor data to find signs of equipment problems and production irregularities. | Detecting yield deviations earlier helped lower the number of defective units and reduce production losses. This helped maintain consistent product quality and stable manufacturing operations. |
Logos and trademarks shown above are the property of their respective owners. Their use here is for informational and illustrative purposes only.
MARKET ECOSYSTEM
The predictive maintenance ecosystem includes several participants. Vendors provide sensors, monitoring devices, and analytics platforms used to track equipment performance. Industrial equipment manufacturers add monitoring capabilities to machines and automation systems. Cloud providers support predictive maintenance by offering data storage and processing services. System integrators and consulting firms assist organizations with deployment and system integration.
Logos and trademarks shown above are the property of their respective owners. Their use here is for informational and illustrative purposes only.
MARKET SEGMENTS
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
Predictive Maintenance Market, By Offering
Sensors and sensing devices used in monitoring infrastructure account for a large share of predictive maintenance solutions. These devices capture operational data such as vibration, temperature, and pressure from machines. The collected data is used to monitor equipment condition and identify unusual patterns that may indicate potential faults.
Predictive Maintenance Market, By Technology
Edge computing and edge AI are expected to expand rapidly in predictive maintenance environments. These technologies allow equipment data to be processed close to where it is generated rather than sending all information to centralized systems. Local processing supports faster fault detection and enables real-time monitoring in industrial operations.
Predictive Maintenance Market, By Monitoring Technique
Vibration monitoring accounts for the largest share in predictive maintenance deployments. Vibration sensors track changes in machine movement and help identify imbalance, misalignment, bearing wear, and other mechanical issues. Because many industrial assets, such as motors, pumps, compressors, and turbines, rely on rotating components, vibration analysis is widely used to detect early signs of equipment faults and support planned maintenance activities.
Predictive Maintenance Market, By Asset Type
Industrial robots and automation equipment are expected to grow at the highest rate. Many factories are expanding the use of robotics and automated production systems. Monitoring these assets helps maintain stable production operations and reduce unexpected failures.
Predictive Maintenance Market, By Vertical
Manufacturing accounts for the largest share of the predictive maintenance market because production facilities depend heavily on machines and automated equipment. When machines fail, production lines can stop, and operating costs increase. Predictive maintenance helps track machine condition and detect possible faults earlier, allowing maintenance teams to plan repairs before a breakdown occurs.
REGION
Asia Pacific is projected to be the fastest-growing region in predictive maintenance market
Asia Pacific is expected to see the fastest growth in the predictive maintenance market. Countries such as China, India, Japan, and South Korea are expanding industrial automation and digital manufacturing programs. The growing use of connected machines and Industry 4.0 technologies is increasing the demand for predictive maintenance solutions in the region.
PREDICTIVE MAINTENANCE MARKET: COMPANY EVALUATION MATRIX
ABB is a key vendor in the predictive maintenance landscape through its industrial automation systems and equipment monitoring technologies. Splunk is also gaining presence by using its data analytics and monitoring platforms to analyze machine data and support predictive maintenance applications.
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
KEY MARKET PLAYERS
- ABB (Switzerland)
- Honeywell (US)
- Schneider Electric (France)
- AWS (US)
- IBM (US)
- Augury (US)
- Google (US)
- Microsoft (US)
- Hitachi (Japan)
- GE (US)
- Oracle (US)
- Altair (US)
- TIBCO Software (US)
- Siemens (Germany)
- SAS (US)
- SAP (Germany)
- Emerson (US)
MARKET SCOPE
| REPORT METRIC | DETAILS |
|---|---|
| Market Size in 2025 (Value) | USD 8.36 Billion |
| Market Size in 2026 (Value) | USD 9.71 Billion |
| Market Forecast in 2031 (Value) | USD 16.74 Billion |
| CAGR | 11.5% |
| Years Considered | 2021-2031 |
| Base Year | 2025 |
| Forecast Period | 2026-2031 |
| Units Considered | USD Billion |
| Report Coverage | Revenue forecast, company ranking, competitive landscape, growth factors, and trends |
| Segments Covered |
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| Regions Covered | North America, Asia Pacific, Europe, the Middle East & Africa, Latin America |
WHAT IS IN IT FOR YOU: PREDICTIVE MAINTENANCE MARKET REPORT CONTENT GUIDE
DELIVERED CUSTOMIZATIONS
We have successfully delivered the following deep-dive customizations:
| CLIENT REQUEST | CUSTOMIZATION DELIVERED | VALUE ADDS |
|---|---|---|
| Predictive Maintenance Platform Evaluation and Deployment Review | A customized assessment of predictive maintenance platforms was conducted for industrial enterprises, evaluating digital maintenance and asset monitoring deployments. The review focused on scalability, integration with existing industrial equipment and IoT systems, support for real-time equipment monitoring, and compatibility with cloud and edge environments. Vendors were evaluated based on analytics capabilities, AI-driven fault detection, and asset performance management features. | The engagement helped the client identify predictive maintenance solutions aligned with operational requirements and equipment monitoring needs. It reduced deployment risks, improved confidence in long-term system scalability, and enabled faster implementation of predictive maintenance initiatives across multiple facilities. |
| Predictive Maintenance Vendor Landscape Assessment | A tailored customization mapped leading predictive maintenance vendors and technologies supporting equipment monitoring, asset performance analytics, and industrial data management. Vendors were assessed based on capabilities such as sensor integration, machine learning–based failure prediction, industrial IoT compatibility, and real-time analytics support. | The customization provided clearer visibility into the predictive maintenance vendor ecosystem and supported technology investment decisions. It enabled the client to align predictive maintenance capabilities with operational objectives while ensuring cost efficiency and scalability for future deployments. |
RECENT DEVELOPMENTS
- December 2025 : IBM introduced Maximo Condition Insight, a new AI-powered capability in the Maximo Application Suite that analyzes asset data, work orders, and sensor information to provide predictive maintenance insights and improve asset health monitoring.
- November 2025 : Schneider Electric introduced EcoStruxure Foresight Operation, an AI-driven platform designed to improve building and infrastructure operations through predictive analytics and maintenance insights.
- June 2025 : IBM released Maximo Application Suite Manage Component Patch 8.7.21, continuing updates to its enterprise asset management platform used for predictive maintenance and asset monitoring.
- March 2025 : Schneider Electric and Compass Datacenters deployed predictive maintenance and AI-based analytics to improve data-center maintenance operations, reducing onsite maintenance interventions by about 40% and operational costs by 20%.
- June 2024 : IBM released Maximo Application Suite 9.0, adding enhanced AI-driven predictive maintenance features and expanded IoT integration for real-time asset monitoring and failure prediction.
Table of Contents
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Methodology
The research methodology for the predictive maintenance market report involved extensive use of secondary sources and directories, as well as various reputable open-source databases, to identify and collect relevant information for this technical and market-oriented study. In-depth interviews were conducted with various primary respondents, including offering infrastructure, by sensors & sensing devices, imaging & inspection devices, edge monitoring infrastructure, connectivity hardware, end user; high-level executives of multiple companies offering predictive maintenance monitoring infrastructure, software, services, and industry consultants, to obtain and verify critical qualitative and quantitative information and assess the market prospects and industry trends.
Secondary Research
During the secondary research process, various secondary sources were consulted to identify and collect information for the study. The secondary sources included annual reports, press releases, investor presentations, white papers, and certified publications.
Secondary research was used to gather key information on the industry’s value chain, the market’s monetary chain, the overall pool of key players, market classification, and segmentation based on industry trends, regional markets, and key developments from both market- and technology-oriented perspectives.
Primary Research
In the primary research process, a diverse range of stakeholders from both the supply and demand sides of the Predictive maintenance ecosystem were interviewed to gather qualitative and quantitative insights specific to this market. From the supply side, key industry experts, including chief executive officers (CEOs), vice presidents (VPs), marketing directors, technology & innovation directors, and technical leads from vendors offering predictive maintenance software & services, were consulted. Additionally, system integrators, service providers, and IT service firms that implement and support Predictive maintenance were included in the study. On the demand side, input from IT decision-makers, infrastructure managers, and business heads of prominent industry end users was collected to understand the user perspectives and adoption challenges within targeted industries.
The primary research ensured that all crucial parameters affecting the predictive maintenance market, from technological advancements and evolving use cases to regulatory and compliance needs, were considered. Each factor was thoroughly analyzed, verified through primary research, and evaluated to obtain precise quantitative and qualitative data for this market.
Once the initial phase of market engineering was completed, including detailed calculations for market statistics, segment-specific growth forecasts, and data triangulation, a second round of primary research was conducted. This step was crucial for refining and validating critical data points, such as predictive maintenance offerings (monitoring infrastructure, software, services), industry adoption trends, the competitive landscape, and key market dynamics like demand drivers (Increasing need to reduce equipment downtime and maintenance costs, Increasing adoption of IoT-enabled equipment monitoring in industrial operations), challenges (Integration of predictive maintenance solutions with legacy industrial systems, Ensuring data accuracy and reliability for predictive maintenance models), opportunities (Growing adoption of edge computing for faster equipment data processing, Growing use of AI and machine learning for predictive maintenance analytics), and restraints (High implementation and infrastructure setup costs, Data management and integration challenges across multiple equipment systems).
In the comprehensive market engineering process, the top-down and bottom-up approaches, along with several data triangulation methods, were extensively employed to perform market estimation and forecasting for the overall market segments and subsegments listed in this report. Extensive qualitative and quantitative analysis was performed on the complete market engineering process to record the critical information/insights throughout the report.
Breakdown of Primary Participants
Note: Tier 1 companies' revenue is more than USD 10 billion; tier 2 companies 'revenue ranges between USD 1 and 10 billion; and tier 3 companies' revenue ranges between USD 500 million and USD 1 billion
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Market Size Estimation
The market was divided into several segments and subsegments after determining the overall market size using the market size estimation processes described above. To complete the overall market engineering process and determine the exact statistics for each market segment and subsegment, data triangulation and market segmentation procedures were employed, wherever applicable. The overall market size was then used in the top-down approach to estimate the size of other individual markets by applying percentage splits to the market segmentation.
Data Triangulation
The market was divided into several segments and subsegments after determining the overall market size using the market size estimation processes described above. To complete the overall market engineering process and determine the exact statistics for each market segment and subsegment, data triangulation and market segmentation procedures were employed, wherever applicable. The overall market size was then used in the top-down approach to estimate the size of other individual markets by applying percentage splits to the market segmentation.
Market Definition
According to IBM, predictive maintenance is the use of advanced analytics, machine learning, and sensor-based data monitoring to evaluate equipment condition in real-time and anticipate potential failures before they occur. These solutions collect operational data from connected assets and apply predictive models to identify performance anomalies and maintenance needs. By leveraging IoT technologies, historical datasets, and AI-driven analytics, organizations can proactively schedule maintenance, minimize unplanned downtime, and extend equipment lifespans. Predictive maintenance platforms also support operational efficiency by optimizing maintenance planning, improving asset reliability, and enabling data-driven decision-making across industrial and infrastructure environments.
Key Stakeholders
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- Predictive maintenance solution providers
- Industrial equipment and sensor manufacturers
- Industrial IoT (IIoT) platform providers
- AI and analytics technology providers
- System integrators and consulting firms
- Cloud service providers
- Industrial automation and robotics providers
- Data analytics and software platform vendors
- Edge computing and connectivity solution providers
- Maintenance service providers
- Engineering and reliability consulting firms
- Technology distributors and value-added resellers
- Research institutions and technology developers
- End-user organizations
Report Objectives
- To define, describe, and predict the predictive maintenance market by offering (monitoring infrastructure, software, services), technology, monitoring technique, asset type, end user, and region
- To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing market growth
- To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders
- To forecast the market size of segments with respect to five main regions: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
- To analyze each submarket with respect to individual growth trends, prospects, and contributions to the overall predictive maintenance market
- To analyze competitive developments, such as partnerships, product launches, mergers & acquisitions, in the predictive maintenance market
- To analyze the impact of macroeconomic factors on the predictive maintenance market across all regions
Available customizations:
Using the provided market data, MarketsandMarkets offers customizations tailored to the company’s specific needs. The following customization options are available for the report.
Product analysis
- Product comparative analysis, which gives a detailed comparison of innovative products offered by prominent vendors
Geographic analysis
- Further breakup of additional European countries by offering (monitoring infrastructure, software, services), technology, monitoring technique, asset type, and end user
- Further breakup of additional Asia Pacific countries by offering (monitoring infrastructure, software, services), technology, monitoring technique, asset type, and end user
- Further breakup of additional Middle East & African countries by offering (monitoring infrastructure, software, services), technology, monitoring technique, asset type, and end user
- Further breakup of additional Latin American countries by offering (monitoring infrastructure, software, services), technology, monitoring technique, asset type, and end user
Company information
- Detailed analysis and profiling of additional market players (up to five)
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