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 globalpredictive maintenance market sizewas valued atUSD 7.85 billion in 2022,预计年复合增长的恶性肿瘤h 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 the component that must be replaced 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 the need for manual inspection, and enhancing workplace safety by automatically collecting data from machines in hard-to-reach places.

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

Several industries useARtechnology for asset repair and maintenance. For AR technology, the technician aims the camera at the electrical wiring, machine, and plumbing, and the system displays the necessary information on the screen. While taking readings, such as vibration and temperature, the AR-integrated systems also consider historical data, enabling the user to understand the major changes in the reading. AR also helps enhance the safety and productivity of repair and maintenance personnel, owing to the 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.

Some industrial machines used currently are not compatible with the smart sensors used for predictive maintenance, which has been a major factor restraining the growth of the predictive maintenance market. The compatibility concerns of the assets have resulted in assets being altered for the sensors to be built into them, which could result in additional costs to the company, restraining businesses from adopting predictive maintenance technology. Considering the limited budget of SMEs, the additional costs could negatively affect the company’s financial balance, resulting in reduced adoption of predictive maintenance solutions among SMEs.

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 enable the manufacturer to leverage existing assets. PdMaaS solutions also offer on-demand access to predictive maintenance, helping improve scalability and eliminating the need for infrastructure and development costs. Some of the major benefits of PdMaaS in the manufacturing sector include improving asset life, remaining useful life, machine uptime, and reliability by tracking issues concerned with assets before their breakdown.

Component Insights

The 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 employsartificial intelligencealgorithms 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.

The services segment is projected to grow with 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

The integrated segment accounted for the largest share of 75.1% of the overall revenue in 2022. The growth of the integrated segment can be attributed to the growing preference for predictive maintenance solutions that can be easily integrated with the 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.

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

Service Insights

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

The training and consulting segment is projected to grow with the highest CAGR from 2023 to 2030. The growing level of complexities in operating conditions and the machinery used in industrial operations are prompting the end users to obtain specialized opinions from a predictive maintenance expert before drafting a maintenance strategy, thereby driving the adoption of training & consulting services in the predictive maintenance market. 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

The on-premise segment is projected to occupy the largest market share of 75.8% in 2022. The growth of the on-premise segment can be attributed to the benefits, such as better control and a high level of customization, among others, associated with on-premise installation. Several leading incumbents of the predictive maintenance industrymarket have been offering on-premise solutions. For instance, SAP SE offers on-premise editions of its predictive maintenance solutions. The company operates in more than 78 countries, and the company’s on-premise solutions have been equally popular among consumers in these countries.

The cloud segment is projected to grow with the highest CAGR in the predictive maintenance market from 2023 to 2030. The growth of the cloud segment can be attributed to the benefits, 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. Several leading incumbents of the predictive maintenance market are offering predictive maintenance solutions based on cloud computing technology. For instance, in May 2022, Google Cloud announced the launch of 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

The large enterprises segment of the predictive maintenance market 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.

小型和中型企业projecte段d 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, to gain a competitive advantage in the global market, 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 greater job freedom. This factor is observed as a critical factor driving segment growth.

Monitoring Technique Insights

振动监测的预测maintenance market is expected to occupy 26.6% in 2022. The growth of the vibration monitoring segment can be attributed t the technological advancement of sensors, which enables the collection of accurate and real-time data from several types of equipment. The integration ofIoThas also fueled the growth of vibration monitoring, as the IoT technology enables seamless connectivity sensors to the centralized monitoring system, which offers real-time information on the condition of the machinery.

The oil analysis segment is projected to grow with the highest CAGR in the predictive maintenance market from 2023 to 2030. The growth of the oil analysis segment can be attributed to the advancement in oil analysis techniques, enabling regular monitoring of the lubricating oils' physical and chemical properties, which helps identify the signs of 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

The manufacturing segment of the predictive maintenance market is expected to occupy 27.9% in 2022. Usingadvanced analytics解决方案,比如预测维护、数字technologies can help manufacturers reduce costs and increase production and efficiency. These systems can help reduce downtime and improve production efficiency. The COVID-19 pandemic emerged as the major catalyst behind the digital transformation of the industry. 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.

The aerospace & defense segment is projected to grow with the highest CAGR in the predictive maintenance market from 2023 to 2030. Businesses in the aerospace & defense industries often utilize an antiquated data management approach that needs more breadth and insight to spot patterns or enable early failure diagnosis and isolation. The conservative nature of the tightly regulated sector makes developing and verifying new solutions and services based on emerging technology costly and time-consuming. As aircraft connect more to the internet, maintenance data is expected to grow exponentially, making traditional data management methods no longer sustainable.

Regional Insights

The North American region dominated the market in 2022 and accounted for a market share of 34.81% in 2022. The North American predictive maintenance market is expected to grow significantly over the forecast period. The growth of the North American predictive maintenance market can be attributed to the increasing adoption of advanced technologies such asMachine Learning(ML), acoustic monitoring, Artificial Intelligence (AI), and the Internet of Things (IoT), the proliferation of customer channels, and growing concerns over asset maintenance and operational costs. The growth of the North American market can be attributed to the growing 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.

The Asia Pacific region is projected to grow with the highest CAGR in the predictive maintenance market from 2023 to 2030. Asia Pacific's predictive maintenance industrymarket is expected to grow significantly over the forecast period. The regional market's growth can be attributed to the 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 MarketReport Scope

Report Attribute	Details
Market size value in 2023	USD 9.84 billion
Revenue forecast in 2030	USD 60.13 billion
Growth Rate	CAGR of 29.5% from 2023 to 2030
Base year for estimation	2022
Historical data	2018 - 2021
Forecast period	2023 - 2030
Report updated	September 2023
Quantitative units	Revenue in USD Billion and CAGR from 2023 to 2030
Report coverage	Revenue forecast, company market share, competitive landscape, growth factors, and trends
Segments covered	Component, solution, service, deployment, enterprise size, monitoring technique, end use, and region
Regional scope	North America, Europe, Asia Pacific, Latin America, Middle East & Africa
Country scope	U.S., Canada, U.K., Germany, France, Italy, Spain, China, India, Japan, Australia, South Korea, Brazil, Argentina, Mexico, UAE, South Africa, Saudi Arabia
Key companies profiled	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, and Software AG
Customization scope	Free report customization (equivalent to up to 8 analysts working days) with purchase. Addition or alteration to country, regional & segment scope.
价格和购买该俱乐部ns	Avail customized purchase options to meet your exact research needs.Explore purchase options

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