Automotive Predictive Maintenance Market Value Surge at 18.6% CAGR By 2032

Prudour Private Limited

Updated · Apr 28, 2023

Automotive Predictive Maintenance Market Value Surge at 18.6% CAGR By 2032

Market Overview

Published Via 11Press : The global automotive predictive maintenance market size is expected to be worth around USD 100 Billion by 2032 from USD 18.9 billion in 2021, growing at a CAGR of 18.6% during the forecast period 2022 to 2032.

The automotive predictive maintenance market involves advanced technologies used to monitor and predict vehicle maintenance needs before any breakdown occurs. This market encompasses hardware and software solutions using real-time sensor and telematics system data to detect any potential issues with vehicles and provide predictive maintenance alerts to drivers or fleet managers.

Automotive predictive maintenance market growth can be seen through connected vehicles that offer real-time data about performance and maintenance needs, providing real-time access to vital vehicle performance information and requirements. Predictive maintenance solutions help prevent unexpected breakdowns while simultaneously cutting maintenance costs while improving vehicle safety and reliability.

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Key Benefits

  • Reduce Maintenance Costs: Predictive maintenance solutions can significantly lower maintenance costs by anticipating potential issues before they turn into major headaches, which in turn prevent breakdowns, save on repairs costs, and extend vehicle lifespan.
  • Predictive Maintenance Solutions Can Enhance Vehicle Performance: Predictive maintenance solutions can identify issues impacting vehicle performance, such as engine or transmission troubles or tire wear issues, so as to address them proactively and improve both overall vehicle efficiency and overall performance.
  • Improved Safety: Predictive maintenance solutions can enhance vehicle safety by identifying any potential safety concerns such as brake or tire wear issues that need addressing proactively in order to help avoid accidents, keep drivers and passengers safe, and prevent incidents altogether.
  • Predictive maintenance solutions can increase customer satisfaction by decreasing vehicle downtime, avoiding breakdowns, and making sure vehicles operate reliably and smoothly.
  • Increased Efficiency: Predictive maintenance solutions provide fleet managers with tools they need to increase efficiency through reduced downtime and decreased maintenance costs.

Regional Snapshot

  • North America: North America is projected to hold a substantial portion of the automotive predictive maintenance market worldwide. Growth here will be spurred by increased adoption of connected vehicles as well as several key players present in this region.
  • Europe: Europe is expected to emerge as an attractive market for automotive predictive maintenance due to the growing adoption of electric vehicles and demand for fleet management services.
  • Asia-Pacific: Automotive predictive maintenance market growth will see strong expansion across Asia-Pacific due to increasing adoption of connected vehicles and an upsurge in fleet management services demand in emerging economies such as China and India.
  • Middle East and Africa: With increasing adoption of connected vehicles and rising fleet management needs across these regions, automotive predictive maintenance market growth in these two regions should experience substantial expansion.
  • Latin America: Latin America has emerged as an attractive market for automotive predictive maintenance due to the increasing penetration of connected vehicles and demand for fleet management services in this region.


  • Growing Demand for Connected Vehicles: Connected vehicles provide real-time data about performance and maintenance needs that helps drive automotive predictive maintenance market. Predictive solutions help prevent unexpected breakdowns while decreasing maintenance costs while simultaneously increasing vehicle safety and reliability.
  • Fleet Management Trend: As commercial and logistics sectors adopt fleet management services more frequently, predictive maintenance solutions have seen an upsurge in popularity and implementation. Predictive maintenance allows fleet managers to optimize vehicle utilization while decreasing downtime costs while mitigating maintenance expenses.
  • Recent advances in IoT and AI Technologies: Recent advancements in Internet of Things (IoT) and Artificial Intelligence (AI) technologies are making predictive maintenance solutions increasingly sophisticated and effective, providing real-time monitoring of vehicle performance while employing machine learning algorithms to identify any issues before they become major headaches.
  • Rising Adoption of Electric Vehicles: As more electric cars hit the roads, their growing adoption has created demand for dedicated maintenance and monitoring solutions. Predictive maintenance solutions may assist with identifying any issues with batteries or components in electric vehicle batteries that might reduce lifespan or performance and extend them for an increased lifespan and improved performance.
  • Government Regulations and Initiatives: Government regulations and initiatives related to vehicle safety and emissions have led to the increasing adoption of predictive maintenance solutions, particularly ones focused on vehicle emissions reductions. Predictive maintenance services ensure vehicles meet regulatory requirements while operating safely and efficiently.


  • Implementation Costs Are High: Predictive maintenance solutions may require significant investments of hardware, software and personnel resources – an expense which may act as an impediment to adoption by smaller and medium-sized businesses.
  • Limit of Skilled Professionals: Implementation and maintenance of predictive maintenance solutions require specific skillsets, making adoption challenging when there are insufficient qualified individuals on hand in your company’s vicinity.
  • Integration With Legacy Systems: Many automotive businesses rely on legacy systems and processes that may not be compatible with predictive maintenance solutions, making integration challenging.
  • Data Security and Privacy Issues: Predictive maintenance solutions require collecting and analyzing large volumes of vehicle performance and maintenance-related data, raising questions of security and privacy as it relates to predictive maintenance solutions; particularly given recent breaches and cyberattacks on this sector of industry.
  • Lack of Standardization in Automotive Predictive Maintenance Market: As there is currently a lack of standardization within the automotive predictive maintenance market, various vendors offer various solutions and platforms, making it more challenging for companies to compare solutions side-by-side and limit interoperability between systems.


  • Predictive Maintenance Solutions Provide Opportunities in Emerging Economies: With predictive maintenance still only just starting to make waves across industries and geographies, many regions and industries represent immense expansion opportunities into emerging economies like India and China. Furthermore, India and China’s adoption rates of predictive maintenance solutions is expected to expand significantly within years.
  • Development of New Technologies: With IoT, AI and machine learning emerging as major players in automotive predictive maintenance technology markets, there is increasing room for innovation within automotive predictive maintenance market. New technologies allow more effective predictive maintenance solutions as well as opening up opportunities to generate additional business models and revenue streams for automotive predictive maintenance companies.
  • Integration With Other Technologies: Integrating predictive maintenance solutions with other technologies like telematics or fleet management systems presents companies with opportunities to streamline operations and increase overall efficiency, potentially cutting maintenance costs while increasing vehicle uptime and customer satisfaction.
  • Focus on Sustainability: Automotive industry sustainability initiatives have increased adoption of predictive maintenance solutions designed to decrease emissions, optimize energy use and extend lifespan of both vehicles and their components.
  • Collaboration and Partnerships: Automotive OEMs, service providers, and technology vendors working together can use collaboration and partnerships to accelerate innovation and adoption of predictive maintenance solutions faster. Companies can draw upon each others’ respective strengths to leverage them effectively for creating value propositions that drive growth and contribute to new value propositions that enhance future value propositions and growth potential.


  • Lack of Data Standardization: An inability to standardize data collection, processing and analysis across systems and platforms makes predictive maintenance solutions challenging to implement effectively, leading to inconsistencies in both quality of analysis data as well as hard to achieve accurate predictions that provide reliable estimates of maintenance needs.
  • Integration Challenges: Integrating predictive maintenance solutions with legacy systems and processes can be complex and time consuming, creating challenges in terms of data management, interoperability, scalability and more.
  • Cybersecurity Concerns: Predictive maintenance solutions require collecting and analyzing sensitive vehicle performance and maintenance data that is potentially at risk from cybersecurity attacks and breaches, leaving vehicles susceptible to data breaches, unapproved access, and other security risks. This poses cybersecurity threats which must be carefully mitigated against or risk data breach or breaches occurring and leading to catastrophic outcomes like data theft and loss.
  • Skilled Workforce Shortages: Implementation and maintenance of predictive maintenance solutions require specific skillsets that may be scarcely available; this may create challenges with recruiting skilled employees as well as increased labor costs.
  • Return on Investment (ROI): Implementing predictive maintenance solutions requires an initial, substantial outlay that may not give immediate return; convincing companies of their worth over time may prove challenging, making ROI uncertain or nonexistent.

Market Segments

Based on Component

  • Solutions
  • Integrated
  • Standalone
  • Services
  • Managed Services
  • Professional Service

Based on By Technology

  • IoT
  • Big Data & Data Analytics
  • Business Intelligence (BI)
  • Cloud Computing
  • 5G

Based on Vehicle Type

  • Passenger Cars
  • Light Commercial Vehicles
  • Heavy Duty Trucks
  • Buses and Coaches

Based on Application

  • Oil Change
  • Transmission Checkup
  • Belt Change
  • Brake and Tire Inspection
  • Coolant Replacement
  • Engine Air Filter
  • Cabin Filter
  • Others Application

Key Players

  • IBM
  • SAP
  • SAS Institute Inc.
  • Software AG
  • TIBCO Software Inc
  • Hewlett Packard Enterprise Development LP
  • Altair Engineering Inc.
  • Splunk Inc
  • Oracle
  • Google
  • Amazon Web Services, Inc
  • General Electric
  • Schneider Electric
  • Hitachi, Ltd.
  • PTC
  • RapidMiner Inc
  • Operational Excellence (OPEX) Group Ltd
  • Dingo
  • CHIRON Swiss SA

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Top Impacting Factors:

  • Companies Are Committed to Cost Optimization: With competition increasing in the automotive sector, companies have turned their focus towards optimizing cost optimization while upholding quality and efficiency. Predictive maintenance solutions provide valuable cost reduction opportunities by identifying issues before they become serious problems – thus decreasing downtime and cutting repair expenses.
  • Increased Adoption of IoT and Connected Devices: With the continued rise of connected devices like IoT, predictive maintenance solutions have seen increasing adoption as these technologies allow companies to collect real-time vehicle and equipment data and analyze it on-board for analysis, providing companies with invaluable real-time insight that allows them to identify maintenance needs as well as predict potential failures so as to minimize downtime costs by taking proactive measures such as taking preventative steps against failure and downtime.
  • Artificial Intelligence and Machine Learning Technologies: Artificial intelligence (AI) and machine learning technologies have advanced significantly over recent years, offering more accurate and dependable predictive maintenance solutions than ever. By analyzing large amounts of data to spot patterns or trends quickly and accurately predict maintenance requirements with greater efficiency than ever.
  • Growing Recognition of Sustainability and Environmental Concerns: Rising awareness of sustainability and environmental issues has driven increased adoption of predictive maintenance solutions that help reduce emissions, optimize energy usage and extend vehicle lifespan while decreasing waste, increasing efficiency and mitigating the environmental impacts associated with automotive operations. Predictive maintenance solutions may reduce emissions while optimizing energy use while prolonging vehicle component lifespan – solutions which also have numerous other potential advantages over their traditional counterparts.
  • Transition to Autonomous and Electric Vehicles: As more autonomous and electric vehicles enter the marketplace, predictive maintenance solutions offer new opportunities. Each vehicle requires unique attention from maintenance, so predictive maintenance helps identify issues immediately to reduce downtime while guaranteeing optimal performance.

Recent Development

  • Partnerships and Collaborations: Major players in the automotive predictive maintenance market have formed strategic alliances and collaborations in order to expand their offerings and capabilities. For example, IBM and BMW recently announced an initiative together that will develop a cloud-based predictive maintenance platform dedicated for BMW production systems.
  • Investment and Acquisition Activity in Automotive Predictive Maintenance Market: Investment and acquisition activity within the automotive predictive maintenance market is increasing as companies seek to broaden their capabilities and reach. ZF, for instance, announced plans in 2021 to acquire Wabco; an automotive parts supplier providing predictive maintenance solutions to commercial vehicles.
  • AI and Machine Learning Advancements: Modern advances in artificial intelligence (AI) and machine learning technologies are providing predictive maintenance solutions with more accuracy and reliability, such as Siemens’ announcement in 2021 of an AI-powered predictive maintenance solution for electric vehicle using machine learning algorithms to analyze data and predict potential failures.

Report Scope

Report Attribute Details
The market size value in 2022 USD 18.9 Bn
Revenue forecast by 2032 USD 100 Bn
Growth Rate CAGR Of 18.6%
Regions Covered North America, Europe, Asia Pacific, Latin America, and Middle East & Africa, and Rest of the World
Historical Years 2017-2022
Base Year 2022
Estimated Year 2023
Short-Term Projection Year 2028
Long-Term Projected Year 2032

Frequently Asked Question

Q: What is the current market size for the Automotive Predictive Maintenance Market?
A: According to a report by, the Automotive Predictive Maintenance Market was valued at USD 18.9 billion in 2022 and is expected to reach USD 100 billion by 2032, growing at a CAGR of 18.6% during the forecast period.

Q: What are the key segments of the Automotive Predictive Maintenance Market?
A: The Automotive Predictive Maintenance Market can be segmented based on Component (Solutions, Integrated, Standalone, Services, Managed Services, Professional Service), Based on By Technology (IoT, Big Data & Data Analytics, Business Intelligence (BI), Cloud Computing, 5G), Based on Vehicle Type(Passenger Cars, Light Commercial Vehicles, Heavy Duty Trucks, Buses and Coaches), Application (Oil Change, Transmission Checkup, Belt Change, Brake and Tire Inspection, Coolant Replacement, Engine Air Filter, Cabin Filter, Others Application), and geography (North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa).

Q: Who are the key players in the Automotive Predictive Maintenance Market?
A: Some of the key players in the Automotive Predictive Maintenance Market include IBM, SAP, SAS Institute Inc., Software AG, TIBCO Software Inc, Hewlett Packard Enterprise Development LP, Altair Engineering Inc., Splunk Inc, Oracle, Google, Amazon Web Services, Inc, General Electric, Schneider Electric, Hitachi, Ltd., PTC, RapidMiner Inc, Operational Excellence (OPEX) Group Ltd, Dingo, CHIRON Swiss SA.

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Prudour Private Limited
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