Yantai Bonway Manufacturer

Real-time analysis and decision-making will be made easier by developments in AI and IoT integration, which will drive the Predictive Maintenance Market in the future. Innovation will be fueled by prognostics, industry-specific solutions, and interoperability, while Predictive Analytics as a Service (PAaaS) models will make advanced predictive maintenance skills more accessible to all.

CHICAGO, Marzo 29, 2024 / PRNewswire / - El Mercado de mantenimiento predictivo se estima que crece a partir de USD 10.6 billones en 2024 a USD 47.8 billones in 2029, at a CAGR of 35.1% during the forecast period, according to a new report by MarketsandMarkets™. The driving factors for the Predictive Maintenance Market include the widespread adoption of emerging technologies like IoT sensors and data analytics, enabling real-time monitoring of equipment health. The integration of machine learning (ML) and artificial intelligence (AI) algorithms allows for predictive analysis of potential failures, reducing downtime and optimizing asset performance. Organizations are increasingly focused on cost savings and operational efficiency, driving the need for proactive maintenance strategies to minimize unplanned downtime and maximize productivity. Additionally, regulatory requirements and the shift towards predictive analytics-driven decision-making further contribute to the growth of the Predictive Maintenance Market.

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By component, the services segment to account for higher CAGR during the forecast period.

The services segment plays a crucial role in the Predictive Maintenance Market, serving as a core component essential for the efficient operation of software solutions. Many companies are turning to intelligent devices, robust AI systems, and Industrial Internet of Things (IIoT) solutions to monitor the health and productivity of critical equipment, aiming to minimize costly production shutdowns. Remote monitoring of machinery and equipment has become a significant priority for organizations grappling with challenges in detecting machinery failures. The adoption of predictive maintenance services, including IoT, has become imperative to mitigate the risks and failures of machines across various industries. Within the services segment, managed and professional services are considered vital for enhancing overall process efficiency.

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By Technique, Vibration Analysis is expected to hold the largest market size for the year 2024.

Vibration analysis is a crucial technique employed primarily for high-speed rotating equipment in predictive maintenance strategies. It enables technicians to monitor the vibrations of machines using handheld analyzers or real-time sensors integrated into the equipment itself. Machines operating optimally exhibit specific vibration patterns, which can be compared against known standards. However, as components like bearings and shafts wear down or develop faults, they generate distinct vibration patterns, signaling potential issues. By continuously monitoring equipment vibrations, trained technicians can identify deviations from normal patterns and diagnose problems early on. The range of issues detectable through vibration analysis is extensive and includes misalignment, bent shafts, unbalanced components, loose mechanical parts, and motor irregularities.

By Vertical, Automotive & Transportation is projected to grow at the highest CAGR during the forecast period.

As automotive technology progresses rapidly, traditional fault detection methods are inadequate for ensuring vehicle smoothness. However, modern automobiles are equipped with various sensors, instruments, and cameras that generate diverse data. Leveraging this data, past service records, and employing AI and ML, predictive maintenance in the automotive & transportation sector emerges as a powerful solution to enhance vehicle performance and minimize downtime. The surge in intelligent technologies has spurred predictive maintenance investments in transportation, particularly accelerated by the Covid-19 crisis, where consumer preferences shifted towards individual mobility due to health and safety concerns, leading to an increased demand for cars. This demand surge, coupled with slowed new vehicle production, is driving the resurgence of the used car market. Predictive maintenance plays a crucial role in reducing the lifespan of used cars and preventing unexpected downtimes. Solutions like IBM’s monitoring for connected vehicles and collaborations between automakers and tech companies like Ford, CARUSO, and HIGH MOBILITY showcase the industry’s commitment to leveraging predictive maintenance for improved operations and customer services.

Oriente Medio & África is expected to grow at the second-highest CAGR during the forecast period.

El Oriente Medio & África (MEA) lacks technological development as well as primary business growth in many verticals. Slow economic growth and geopolitical conditions are the major hurdles to the growth of the Predictive Maintenance Market in the region. Moreover, it generates the majority of the revenues from natural resources. The government policy in the Emiratos Árabes Unidos (UAE) is supportive of the industry with the vision to be one of the most technologically advanced nations by 2022. The proliferation of telecom and IT-enabled industry in the African countries is steering the growth of AI-based IoT companies in the region. The major reasons that are said to influence the growth of the Predictive Maintenance Market in the region are the increasing investments in data center infrastructures and the growing number of high-growth start-ups. Only a few countries, such as the UAE, Israely Qatar, across the region, are advancing in this market at an economical pace. The UAE, Israely Qatar have demonstrated a strong commitment toward the development and implementation of AI and IoT technologies.

Top Key Companies in Predictive Maintenance Market:

The major predictive maintenance hardware, solution and service providers include IBM (US), ABB (Suiza), Schneider Electric (Francia), AWS (EE. UU.), Google (US), Microsoft (US), Hitachi (Japón), SAP (Alemania), SAS Institute (US), Software AG (Alemania), TIBCO Software (US), Altair (US), Oracle (US), Splunk (US), C3.ai (US), Emerson (US), GE (EE. UU.), Honeywell (US), Siemens (Alemania), PTC (US), Dingo (Australia), Uptake (US), Samotics (Países Bajos), WaveScan (Singapur), Quadrical Ai (Ubicación: Canadá), 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.

Desarrollos recientes:

• In 2024 de enero, 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 Diciembre 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 Junio 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 Abril de 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 2023 de enero, 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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Predictive Maintenance Market Advantages:

• Through proactive equipment maintenance based on data-driven insights, the prevention of expensive unplanned downtime, and the reduction of repair costs, predictive maintenance assists organisations in cutting maintenance costs.
• Predictive maintenance increases equipment availability and uptime by anticipating possible equipment faults before they happen, guaranteeing continuous operations and maximising output.
• By locating and resolving underlying problems that may compromise the dependability and efficiency of equipment, predictive maintenance extends the life of assets and lowers the need for untimely replacements.
• Predictive maintenance reduces the likelihood of mishaps, injuries, and interruptions to operations brought on by malfunctioning equipment by anticipating possible equipment breakdowns.
• By scheduling maintenance jobs more effectively, predictive maintenance helps businesses minimise interruptions to existing operations and match maintenance duties with production schedules.
• Using data analytics and machine learning algorithms, predictive maintenance produces actionable insights that help with equipment investments, resource allocation, and maintenance strategy selection.
• Predictive maintenance ensures continuous functioning and customer satisfaction by proactively addressing equipment concerns, minimising unplanned downtime and production losses.
• Status-based maintenance techniques, which optimise maintenance resources and cut down on pointless inspections and repairs by triggering maintenance actions based on real-time equipment status data, are made possible by predictive maintenance.

Objetivos del informe

• To describe and forecast the Predictive Maintenance Market, in terms of value,
• by component, technology, technique, organization size, and vertical
• To describe and forecast the Predictive Maintenance Market, in terms of value,
• by region—North America, Europa, Asia-Pacífico, Oriente Medio & África y América Latina
• To provide detailed information regarding major factors influencing the market growth (drivers, restraints, opportunities, and challenges)
• To strategically analyze micromarkets1 with respect to individual growth trends, prospects, and contribution to the overall Predictive Maintenance Market
• To profile key players and comprehensively analyze their market positions in terms of ranking and core competencies2, along with detailing the competitive landscape for market leaders
• To analyze competitive developments such as joint ventures, mergers and acquisitions, product developments, and ongoing research and development (R&D) in the Predictive Maintenance Market
• To provide the illustrative segmentation, analysis, and projection of the main regional markets

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