Predictive Maintenance Market Size, Share & Trends Analysis Report By Component, By Solution, By Service, By Deployment, By Enterprise Size, By Monitoring Technique, By End-use, By Region, And Segment Forecasts, 2023 - 2030

Report Overview

The global predictive maintenance market size was valued at USD 7.85 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 29.5% from 2023 to 2030. Integrating AI and ML into predictive maintenance prevents unplanned downtime and asset failures. AI-based preventive maintenance solutions include IoT hardware components that connect physical assets and an advanced analytics platform that helps predict failures and avoid unplanned downtime. IoT sensors, which are embedded in the equipment, collect various data, including environmental and manufacturing operations data, to determine component failure before breakdown. AI models can also predict patterns for failure modes of certain components. AI's major benefits in predictive maintenance include preventing production losses owing to faulty equipment, eliminating manual inspection, and enhancing workplace safety by automatically collecting data from machines in hard-to-reach places.

Digital twin technology offers a replica of the actual proof in digital format by collecting real-world data of the physical system or objects. It provides simulated output, for example, determining how various inputs would affect business equipment systems. Some major applications include visualization of products in real-time, troubleshooting remote equipment, connecting disparate systems and promoting traceability, and managing complexities and system-level linkages. For the use of digital twin in predictive maintenance, generally, certain criteria need to be considered, such as predictive problem, meaning there should be a target or an outcome to predict; recorded data must be appropriate and sufficient for supporting use cases; operational history, which includes both good and bad outcomes of problems, is required, and the businesses should have domain expertise.

Some industrial machines used currently are not compatible with smart sensors used for predictive maintenance, which has been a major factor restraining the market growth. Compatibility concerns of the assets have resulted in assets being altered for system integration, which could result in additional costs, restraining businesses from adopting predictive maintenance technology.

Predictive Maintenance as a Service (PdMaaS) offers easy access to manufacturing plants at an affordable price. Several startups offer PdMaaS solutions, which help reduce infrastructure costs and maximize asset utilization. PdMaaS solutions also offer on-demand access to predictive maintenance, helping improve scalability and eliminating infrastructure and development costs. Other benefits include improving asset life, remaining useful life, machine uptime, and reliability by tracking issues concerned with assets before their breakdown.

Market Dynamics

Integrating AI and ML into predictive maintenance prevents unplanned downtime and asset failures. AI-based preventive maintenance solutions include IoT hardware components that connect physical assets and an advanced analytics platform that helps predict failures and avoid unplanned downtime. Embedded IoT sensors collect a variety of data, including environmental and manufacturing operations data, for determining the component that needs to be replaced before breakdown.

Several industries use AR technology for asset repair and maintenance. AR also helps enhance safety and productivity of repair and maintenance personnel, owing to easy availability of instruction, and easy access to data helps reduce time in retrieving data. AR technology also helps maximize uptime by assisting predictive maintenance activities.

Component Insights

Solution segment accounted for the largest share of 80.6% of the overall revenue in 2022. Predictive maintenance by solution entails adopting a software or technology solution that uses predictive analytics and data-driven information to improve tasks related to maintenance. The method employs artificial intelligence algorithms for training predictive models using historical information. These models investigate patterns and trends in data to forecast equipment failures, decline, or servicing demands. Predictive Maintenance Solutions assist businesses in streamlining maintenance procedures, leading to cost savings. Organizations may prevent costly reactive repairs, minimize equipment damage, and optimize the utilization of spare parts and assets by recognizing and fixing problems with maintenance before they deteriorate.

Services segment is projected to grow with the highest CAGR from 2023 to 2030. Predictive maintenance service providers collect data from various sources, including device records, detectors, and past service records. Service providers use numerical evaluation to create predictive models that predict equipment breakdowns and servicing needs. They analyze the information using advanced analytics approaches, such as artificial intelligence and machine learning algorithms, to discover patterns, deviations, and possible breakdowns.

Solution Insights

Integrated segment accounted for the largest share of 75.1% of the overall revenue in 2022 owing to growing preference for predictive maintenance solutions that can be easily integrated with end users’ existing Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and other software to increase responsiveness with real-time equipment monitoring, automate service procedures with AI, and leverage Big Data to obtain deeper insights, among others.

Standalone segment is projected to grow significantly from 2023 to 2030 owing to increasing complexities in various industries and industry verticals in terms of processes that need to be followed, machinery to be used, and operating temperatures and pressures to be maintained, among others. These complexities are much more evident in various manufacturing facilities, where a strong emphasis on adopting more eco-friendly raw materials has changed the dynamics of industrial processes.

Service Insights

Integration and deployment segment is estimated to occupy 42.6% of the market share in 2022. Owing to continued digitalization, growing technological awareness, and increasing adoption of predictive maintenance solutions, among other factors. Growing preference among SMEs for cloud-based predictive maintenance solutions and easy availability of cost-effective, cloud-based predictive maintenance solutions also bode well for the segment's growth.

Training and consulting segment is projected to grow with the highest CAGR from 2023 to 2030 owing to growing level of complexities in operating conditions and the machinery used in industrial operations are prompting end users to obtain specialized opinions from a predictive maintenance expert before drafting a maintenance strategy, thereby driving the adoption of training & consulting services These experts play a vital role in devising a customized predictive maintenance strategy based on various factors, such as equipment use, work-order analysis, maintenance information, and Standard Operating Procedure (SOP).

Deployment Insights

On-premise segment is projected to occupy the largest market share of 75.8% in 2022 owing to benefits, such as better control and a high level of customization with on-premise installation. Several leading incumbents have been offering on-premise solutions. For instance, SAP SE offers on-premise editions of its predictive maintenance solutions.

Cloud segment is projected to grow with the highest CAGR in the predictive maintenance market from 2023 to 2030 owing to factors such as reduced costs, easy access to data, remote access to data, unification of information, and automatic updates, among others, associated with cloud-based deployment. For instance, in May 2022, Google Cloud launched Manufacturing Connect and Manufacturing Data Engine, two new solutions designed to enable manufacturers to improve the visibility from the factory floor to the cloud, connect historically siloed assets, and process and standardize data.

Enterprise Size Insights

Large enterprises segment is expected to occupy 72.0% in 2022. A large enterprise that manufactures, sells, and distributes products to thousands of customers across a large supply chain requires powerful software that tracks, maintains, and provides real-time insights about assets. Large enterprises rely heavily on predictive maintenance software to streamline operations and gain a competitive edge. The segment growth can also be attributed to the growing need for large enterprises to track Key Performance Indicators (KPIs) associated with a fleet, asset performance, and other facilities and the need to fit in multiple functions such as work order management, inventory management, and reporting in an organized system.

Small & medium enterprises segment is projected to grow with the highest CAGR in the predictive maintenance market from 2023 to 2030. Small & medium enterprises have traditionally used spreadsheets and manual methods for scheduling asset management and maintenance activities. However, SMEs have shifted to modernized solutions that help streamline operations, provide a centralized platform for asset management, and reduce excessive costs associated with management and maintenance activities. Small enterprises are increasingly investing in cloud-based predictive maintenance software. Cloud-based solutions require a lower initial investment and enable users to manage assets remotely, giving employees’ a greater job freedom. This factor is observed as a critical factor driving segment growth.

Monitoring Technique Insights

Vibration monitoring segment of the predictive maintenance market is expected to occupy 26.6% in 2022owing to technological advancement of sensors, which enables accurate and real-time data from several types of equipment. Moreover, the integration of IoT has also fueled the growth of vibration monitoring due to seamless connectivity of sensors with a centralized monitoring system, which offers real-time information on the condition of the machinery.

Oil analysis segment is projected to grow with the highest CAGR in the predictive maintenance market from 2023 to 2030 owing to advancements in oil analysis techniques, enabling regular monitoring of lubricating oils' physical and chemical properties, which helps identify wear and contamination, among others. Integrating advanced technologies such as AI and ML with predictive maintenance systems has enabled analyzing a large amount of data for making accurate predictions regarding oil change and other aspects, enabling improved safety and longer machinery life, among others.

End-use Insights

Manufacturing segment of the predictive maintenance market is expected to occupy 27.9% in 2022. Using advanced analytics solutions such as predictive maintenance, digital technologies can help manufacturers reduce costs and increase production and efficiency. These systems can help reduce downtime and improve production efficiency. Several manufacturers have accelerated their digital transformation initiatives by implementing new technologies such as data lakes, AI tools, new connectivity standards, robotics, and advanced analytics.

Aerospace & defense segment is projected to grow with the highest CAGR in the predictive maintenance market from 2023 to 2030. Businesses often utilize an antiquated data management approach that needs more breadth and insight to spot patterns or enable early failure diagnosis and isolation. As aircraft connect more to the internet, maintenance data is expected to grow exponentially, making traditional data management methods no longer sustainable.

Regional Insights

North American region dominated the market in 2022 and accounted for a market share of 34.81% in 2022. The growth can be attributed to the increasing adoption of advanced technologies such as Machine Learning (ML), acoustic monitoring, Artificial Intelligence (AI), and the Internet of Things (IoT), proliferation of customer channels, and growing concerns over asset maintenance and operational costs. Moreover, adoption of IoT-connected devices in consumer electronics and M2M applications, increasing demand for connected cars in the automobile industry, and a growing need for innovative consumer electronics are driving the market growth.

Asia Pacific region is projected to grow with the highest CAGR in the predictive maintenance market from 2023 to 2030. Asia Pacific's market's growth can be attributed to diverse expansion of small and medium-sized industries; constant advancements in big data, Machine-to-Machine (M2M), and AI, among others; and growing adoption of cost-effective cloud-based solutions by all organizations.

Key Companies & Market Share Insights

Prominent Predictive Maintenance (MVNO) market players are Cisco Systems, Inc., General Electric Company, SAP SE, Schneider Electric SE, and Siemens. Industry players are also adopting various strategic initiatives such as partnerships, mergers & acquisitions, collaborating with other firms to gain a competitive edge, and deploying better customer services. For instance, in May 2023, Cisco Systems, Inc. and NTT, a telecom infrastructure services company, collaborated to develop and offer real-time data insights, improved decision-making, and enhanced security with the help of predictive maintenance, supply chain management, and asset tracking capabilities.

In June 2023, Accenture plc acquired Nextira, an Amazon Web Services (AWS) premier partner that leverages AWS services to deliver predictive analytics, cloud-native innovations, and an immersive experience to its client base. These AWS services and solutions help boost the engineering capabilities of Accenture Cloud First and provide full-scale cloud capabilities to clients. Nextira offers cloud-based services with cutting-edge artificial intelligence, machine learning, engineering skills, and data analytics to facilitate consumers to build, design, launch, and improve high-performance computing settings. Some of the prominent players operating in the global predictive maintenance market are:

• Accenture plc

• Cisco Systems, Inc.

• General Electric

• Honeywell International Inc.

• Hitachi, Ltd.

• IBM Corporation

• Microsoft

• PTC

• Robert Bosch GmbH

• Rockwell Automation

• SAP SE

• SAS Institute

• Schneider Electric SE

• Siemens

• Software AG

Predictive Maintenance Market Report Scope

Global Predictive Maintenance Market Report Segmentation

This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2018 to 2030. For the purpose of this study, Grand View Research has segmented the global predictive maintenance market report based on component, solution, service, deployment, enterprise size, monitoring technique, end-use and region:

• Component Outlook (Revenue, USD Billion; 2018 - 2030)

  • Solution

  • Service

• Solution Outlook (Revenue, USD Billion; 2018 - 2030)

  • Integrated

  • Standalone

• Service Outlook (Revenue, USD Billion; 2018 - 2030)

  • Integration and Deployment

  • Support & Maintenance

  • Training & Consulting

• Deployment Model Outlook (Revenue, USD Billion; 2018 - 2030)

  • Cloud

  • On-premise

• Enterprise Size Outlook (Revenue, USD Billion; 2018 - 2030)

  • Small & Medium Enterprises

  • Large Enterprises

• Monitoring Technique Outlook (Revenue, USD Billion; 2018 - 2030)

  • Torque Monitoring

  • Vibration Monitoring

  • Oil Analysis

  • Thermography

  • Corrosion Monitoring

  • Others

• End-use Outlook (Revenue, USD Billion; 2018 - 2030)

  • Aerospace & Defense

  • Automotive & Transportation

  • Energy & Utilities

  • Healthcare

  • IT & Telecommunications

  • Manufacturing

  • Oil & Gas

  • Others

• Regional Outlook (Revenue, USD Billion; 2018 - 2030)

  • North America

    • U.S.

    • Canada

  • Europe

    • U.K.

    • Germany

    • France

    • Italy

    • Spain

  • Asia Pacific

    • China

    • India

    • Japan

    • Australia

    • South Korea

  • Latin America

    • Brazil

    • Mexico

    • Argentina

  • Middle East & Africa

    • UAE

    • Saudi Arabia

    • South Africa