Predictive Maintenance Market

[376 Pages Report] The global market for the predictive maintenance market is projected to grow from USD 10.6 billion in 2024 to USD 47.8 billion in 2029, at a CAGR of 35.1% during the forecast period. The predictive maintenance market is propelled by several factors, including the rising adoption of emerging technologies for obtaining valuable insights, the emergence of machine learning (ML) and artificial intelligence (AI), and the increasing demand to minimize maintenance costs, equipment failures, and downtime.

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Market Dynamics

Driver: Advent of ML and AI

Digitalization in the current day and age has exponentially transformed the landscape of predictive maintenance, thereby encouraging predictive maintenance vendors and service providers to adopt digitized systems for in-store activities. Organizations are increasingly focusing on the re-engineering of businesses with the help of the immense power of the internet and digital technology. The advent of AI and ML has enhanced organizations’ ability to gather information, analyze it, and perform quick actions with minimum human intervention. Organizations are also adopting AI systems to speed up investment decisions. According to an industry expert, the use of machine learning to detect machine and system faults early extends the service life of facility machinery by 30 percent on average. Organizations that use a predictive maintenance approach minimize not only the degree of damages but also the spread of problems. This is because a problem with a low-cost part might lead to damage to a critical component, shortening the asset lifecycle.

Restraint: Lack of skilled workforce

Skilled personnel are essential for managing the latest software systems used to deploy AI-based IoT technologies and capabilities. Therefore, it is imperative to train existing employees on how to operate these new and upgraded systems. Additionally, industries are actively adopting new technologies but are encountering challenges due to a shortage of highly skilled and proficient workers. With many global vendors initiating predictive maintenance projects, there is a growing demand for a skilled workforce. Companies must develop expertise in areas such as cybersecurity, networking, and applications. They also aim to leverage IoT data for predictive analysis, failure prevention, operational optimization, product innovation, and advanced analytics incorporating AI and ML technologies.

Opportunity: Real-time condition monitoring to assist in taking rapid actions

Enhanced asset management is increasingly essential across diverse industries. Providers equipped with AI and ML capabilities can gather and transform vast amounts of customer-related data into actionable insights, particularly as IoT generates significant data from interconnected devices. AI can also be integrated with IoT devices to optimize service delivery aspects like predictive maintenance and quality assessment, eliminating the need for manual intervention. AI-driven IoT solutions are already gaining traction in various sectors, a trend expected to continue as technology advances. Real-time data from sensors, actuators, and control parameters not only forecast potential asset failures but also enable companies to monitor operations in real-time and take immediate actions when necessary.

Challenge: The requirement of frequent maintenance and upgradation to keep the systems updated

Enterprises are increasingly embracing AI-based IoT solutions for predictive maintenance and to enhance the customer experience. In this market landscape, vendors must develop predictive maintenance systems with a focus on two critical factors: maintenance and updates. An AI-based IoT system requires regular updates and maintenance to align with evolving business needs and implement technological advancements. This includes upgrading the software to accommodate new components and integrating the new system with existing ones. However, as the number of systems grows, so does the maintenance cost. Managing and upgrading AI-based IoT systems poses a significant challenge for companies aiming to provide uninterrupted solutions to their clients.

Predictive Maintenance Market Ecosystem

The predictive maintenance ecosystem comprises hardware providers supplying sensors and monitoring devices, solution providers offering software for data analysis and predictive modelling, service providers delivering consulting, implementation, and managed services, data management, and analytics providers specializing in processing and deriving insights from maintenance data, and regulatory bodies setting standards and guidelines to ensure reliability and safety.

By Vertical, Energy & Utilities segment accounts for the largest market size during the forecast period.

The energy & utilities sector conventionally relies on time-based preventive maintenance to look after its assets, leading to static maintenance routines prone to errors and delays. However, AI or machine learning (ML)-driven predictive maintenance solutions usher in intelligence-based maintenance, enabling energy companies, including those in the oil and gas sector, to leverage historical and real-time data for accurate asset and infrastructure condition forecasting. Oil and gas production plants, with their highly hazardous nature, often necessitate complete shutdowns for planned and unplanned maintenance, and unidentified faults can lead to resource leakage, safety risks, and capital loss. To address these challenges, startups develop predictive maintenance platforms utilizing AI, ML, and predictive analytics to swiftly identify asset anomalies, allowing oil and gas plant operators to detect deviations in pumps, vessels, pipelines, tanks, and drills for better maintenance planning and reduced downtimes. This approach also reduces the need for continuous manual inspections of critical infrastructures, saving labor hours and improving productivity.

By Solution, Standalone segment is projected to grow at the highest CAGR during the forecast period.

Initially, predictive maintenance was integrated into IoT suites and platforms as a basic capability. However, given the escalating volume of operational data generated by organizations today, there's a growing demand for advanced predictive maintenance solutions. These advanced solutions empower organizations to transition from reactive to proactive maintenance processes. Standalone predictive maintenance solutions meet this rising need for advanced capabilities by employing vertical-focused and automated maintenance techniques. They offer flexibility and a variety of optional features, enabling enterprises to forecast equipment failures based on historical data from operational machines, leading to cost savings and reduced downtime. Although standalone software lacks customization capabilities, its affordability makes it popular among small and mid-sized enterprises. Standalone solution providers also offer support for integrating their predictive maintenance solutions into various leading IoT suites.

North America to account for the largest market size during the forecast period.

North America is expected to be the largest contributor to the adoption of AI technologies. The countries considered for the analysis of the North American predictive maintenance market are the US and Canada. Both countries have heavily invested in R&D activities, contributing to the growth of new technologies. Companies in various countries of this region, especially in the US, are leveraging AI, IoT, ML, and deep learning technologies in their business processes to gain a competitive edge in the market. The countries in this region have a well-established economy, enabling predictive maintenance vendors to invest in technological advancements and innovation. Additionally, the region is known as the center of innovation as IT giants are launching new offerings and aggressively collaborating with the predictive maintenance market. The government and public sector have also joined the race to become technologically advanced to cater to a larger customer base. North America is contributing significantly to the predictive maintenance market and is expected to grow at a good pace. Furthermore, the presence of global vendors, such as IBM, Microsoft, General Electric, and Fiix Software, plays a vital role in the adoption of predictive maintenance solutions in the market.

Key Market Players

The major predictive maintenance hardware, solution and service providers include 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). These companies have used both organic and inorganic growth strategies such as product launches, acquisitions, and partnerships to strengthen their position in the predictive maintenance market.

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Scope of the Report

This research report categorizes the predictive maintenance market based on component (hardware, solution [By deployment mode] & services), technology, technique, organization size, vertical and region.

Component:

• Hardware
• Solution
  • Integrated
  • Standalone
• Solution By Deployment Mode
  • Cloud
  • On-premises
• Services
  • Professional Services
    • System Integration
    • Support & Maintenance
    • Consulting
  • Managed Services

By Technology:

• Analytics & Data Management
• Artificial Intelligence (AI)
• Internet Of Things (IoT) Platform
• Sensors and Other Devices

By Technique:

• Vibration Analysis
• Infrared Thermography
• Acoustic Monitoring
• Oil Analysis
• Motor Circuit Analysis
• Other Techniques

By Organization Size:

• Large Enterprises
• SMEs

By Vertical:

• Energy & Utilities (includes oil and gas)
• Manufacturing
• Automotive & Transportation
• Aerospace & Defense
• Construction & Mining
• Healthcare
• Telecommunications
• Other Verticals (Retail, Agriculture, Logistics)

By Region:

• North America
• Europe
• Asia Pacific
• Middle East & Africa
• Latin America

Recent Developments:

• In January 2024, Siemens and AWS deepened their collaboration to simplify the development and scaling of generative artificial intelligence (AI) applications for businesses across various industries and sizes. This partnership enables domain experts in fields like engineering, manufacturing, logistics, insurance, or banking to leverage advanced generative AI technology to create and enhance applications efficiently.
• In December 2023, ABB enhanced its ABB Ability Field Information Manager (FIM 3.0) to provide system engineers and maintenance teams with enhanced connectivity and expanded reach across the latest communication protocols.
• In June 2023, Qatar Airways and Google Cloud partnered to create innovative data and artificial intelligence (AI) solutions tailored for the airline industry. This collaboration will concentrate on enhancing areas like predictive maintenance, passenger experience, and cargo operations, aiming to elevate efficiency and customer satisfaction within the airline sector.
• In April 2023, TrendMiner launched an updated version of its predictive maintenance software, the Digital Twin Manager. This release includes enhanced support for cloud data sources from AWS and Microsoft, along with interactive search functionality, enabling users to make data-driven decisions more efficiently.
• In January 2023, AVEVA, a global leader in industrial software, finalized its acquisition by Schneider Electric. AVEVA's strategic objective is to emerge as the top Software as a Service (SaaS) provider in software and industrial information, transitioning to a subscription-only business model.

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