Global Predictive Maintenance Market Is Estimated To Witness High Growth Owing To Rising Machine Failures And Increasing Focus On Cost Savings
The global Predictive Maintenance Market is estimated to be valued at USD 8.96 Bn in 2024, exhibiting a CAGR of 21.8% over the forecast period (2024-2031). Rising machine failures due to aging infrastructure and emphasis on cost reduction are compelling organizations to adopt predictive maintenance solutions. Furthermore, growing need to improve asset lifecycle and optimize operational costs is also driving the market growth.
Market Dynamics:
The global predictive maintenance market is witnessing growth owing to rising machine failures which can disrupt business operations and increase maintenance costs. Predictive maintenance helps organizations identify such issues in advance and plan maintenance activities accordingly. This reduces unplanned downtime and associated costs. The other major driver is the increasing focus on operational cost optimization. Predictive maintenance provides insights around asset performance and helps prioritize maintenance tasks. This allows organizations to efficiently manage maintenance budgets and reduce expenses significantly over the long run. Predictive solutions leverage technologies like AI, IoT, and data analytics to gather equipment condition data continuously and analyze it to detect anomaly patterns indicating upcoming issues.
Improved Equipment Uptime is a Key Driver for Global Predictive Maintenance Market Adoption
One of the major drivers for the growth of the global predictive maintenance market is the need for improved equipment uptime. Predictive maintenance technologies allow equipment owners and operators to plan maintenance activities based on real-time equipment condition monitoring rather than on pre-defined schedules. This helps reduce unexpected downtime due to equipment failures. By undertaking maintenance repairs at an optimum time, equipment uptime can be maximized. This is especially important for critical assets where any downtime results in significant loss of production. Industries such as oil and gas, manufacturing and transportation rely heavily on equipment being available at all times. Adopting predictive maintenance allows them to achieve near 100% asset availability through condition-based planned repairs.
Cost Savings from Avoiding Unexpected Equipment Failures is Driving More Organizations Towards Predictive Maintenance
Another key driver for the predictive maintenance market is the prospect of substantial cost savings from avoiding unexpected equipment failures. Unplanned downtime due to equipment breakdowns is expensive for organizations as it disrupts operations and results in lost productivity and revenue. It also leads to higher maintenance costs from rush repair jobs. Predictive maintenance helps identify machinery issues beforehand so repairs can be carried out at a scheduled planned downtime when the impact is lowest. This eliminates expensive emergency repairs and losses from production stoppages. Many companies have reported maintenance cost reductions upwards of 25-30% after implementing predictive analytics solutions. The ROI from avoiding costly failure consequences is compelling organizations across industries to invest in predictive maintenance technologies and services.
Lack of Skilled Workforce is a Major Market Restraint
One of the key challenges faced by the predictive maintenance market is the lack of availability of technicians and engineers with the required skills to implement these technologies. Traditional equipment maintenance personnel may not have the data science and analytical skills needed for tasks like data collection, inspection, monitoring, and diagnostics. While predictive maintenance vendors offer training programs, the shortage of talent with the blended mechanical and technical skills persists. This skill gap acts as a restraint for wider adoption of predictive maintenance systems as many companies struggle to find qualified staff to deploy and support such solutions. Extensive training requirements also increase initial investment costs, discouraging some potential customers.
Data Security and Privacy Concerns are Hindering Market Growth
Another major restraint for the predictive maintenance market is growing concerns around data security, privacy and cybersecurity. As these solutions collect vast amounts of sensitive operational data from equipment, there are risks of confidential business information or intellectual property getting into the wrong hands. Industrial facilities also worry about security vulnerabilities that could potentially expose critical infrastructure systems to attacks. Any data breach or security lapse not only damages customer trust but may also invite penalties. Regulatory compliance related to digital asset management is also an ongoing challenge. Unless predictive maintenance vendors have robust security measures with strict access controls and encryption, some companies will be hesitant about widely deploying these technologies due to privacy and security risks.
Remote Diagnostics and Monitoring Opening New Revenue Streams
One of the key opportunities for predictive maintenance providers is the rising demand for remote diagnostic and monitoring services. With many industries transitioning to remote asset management due to travel restrictions, there is growth in the “servitization” of maintenance - from traditional break-fix model to condition-based monitoring delivered as-a-service. Vendors providing cloud-based predictive maintenance platforms with mobile inspectors are seeing greater traction. Expanding remote service capabilities allows vendors to offer maintenance as-a-subscription to more customers and open new revenue streams from global clients regardless of location. This shifts revenues from one-time tool/license sales to long-term managed service revenues.
Integration with IIoT is Driving New Use Cases and Market Expansion
Another significant opportunity for predictive maintenance companies lies in tighter integration of these solutions with industrial internet of things (IIoT) technologies like smart sensors, cloud platforms, and AI/ML systems. This convergence of OT and IT systems is opening up new predictive use cases beyond core machinery. As more ‘non-critical’ assets get connected sensors, vendors can develop algorithms for equipment like HVAC, clean utilities that were traditionally not monitored. The volume of connectable assets is expanding dramatically with IIoT, presenting a huge growth runway for predictive analytics providers to develop solutions targeting a wider scope of industrial equipment and developing new predictive maintenance revenue models in process.
Link - https://www.coherentmarketinsights.com/market-insight/predictive-maintenance-market-2548
Key Developments:
- In December 2023, ABB, a global leader in electrification and automation with over 140 years of excellence and a commitment to driving industrial transformation, 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. This upgrade aims to boost operational efficiency by enabling faster, more intuitive configuration, commissioning, diagnostics, and maintenance of devices, while supporting smarter maintenance and improved engineering efficiency.
- In June 2023, Qatar Airways, one of the world's top airlines known for its commitment to excellence and customer service, partnered with Google Cloud, a leading provider of cloud computing and artificial intelligence solutions, to develop innovative data and AI solutions specifically tailored for the airline industry. This collaboration will focus on enhancing areas such as predictive maintenance, passenger experience, and cargo operations, with the goal of elevating efficiency and customer satisfaction within the airline sector. By leveraging Google Cloud's advanced data analytics, AI, and machine learning capabilities, Qatar Airways aims to optimize its operations and deliver a more personalized and streamlined experience for its passengers.
- In April 2023, TrendMiner, a leading provider of advanced industrial analytics and a subsidiary of Software AG, launched an updated version of its predictive maintenance software, the Digital Twin Manager. This release includes enhanced support for cloud data sources from Amazon Web Services (AWS) and Microsoft, as well as interactive search functionality, enabling users to make data-driven decisions more efficiently. The Digital Twin Manager allows industrial companies to create digital process twins, providing deeper insights into operational performance and facilitating predictive maintenance efforts.
- In January 2023, AVEVA, a global leader in industrial software founded in 1967 and known for its digital twin technology and solutions across the entire asset lifecycle from engineering to operations and maintenance, finalized its acquisition by Schneider Electric, a leading global industrial company specializing in digital automation and energy management. AVEVA's strategic objective is to emerge as the top Software as a Service (SaaS) provider in software and industrial information, with a focus on transitioning to a subscription-only business model.
Key Player:
ABB Ltd., Cisco Systems, Inc., Emerson Electric Co., General Electric Company, Hewlett Packard Enterprise, Hitachi, Ltd., IBM Corporation, Microsoft Corporation, Oracle Corporation, PTC Inc., Rockwell Automation Inc., SAP SE, Schneider Electric SE, Siemens AG, and Uptake Technologies Inc.
