Informe de análisis de tamaño, participación y tendencias del mercado de mantenimiento predictivo global: descripción general de la industria y pronóstico hasta 2031

Análisis del mercado de mantenimiento predictivo

El aumento del uso de tecnologías nuevas y emergentes para obtener información valiosa para la toma de decisiones ha contribuido al crecimiento de la industria. Diversos usuarios finales verticales tienen cada vez más necesidad de reducir costos y tiempos de inactividad, lo que ha estimulado el crecimiento del mercado.

Tamaño del mercado de mantenimiento predictivo

El tamaño del mercado global de mantenimiento predictivo se valoró en USD 6,72 mil millones en 2023 y se proyecta que alcance los USD 63,09 mil millones para 2031, con una CAGR del 32,30% durante el período de pronóstico de 2024 a 2031.

Alcance del informe y segmentación del mercado       

Definición del mercado de mantenimiento predictivo

Predictive maintenance software system is employed to watch the performance and condition of any instrumentation or machine whereas operational them. The software system observes the instrumentation victimisation advanced techniques that permits the upkeep of the machinery to be regular before any failure happens. prognosticative maintenance software system has found its application in varied fields like finding three-phase power imbalances from harmonic distortion, distinctive motor electrical phenomenon spikes, heating from dangerous bearings.

Predictive Maintenance Market Dynamics

This section deals with understanding the market drivers, advantages, opportunities, restraints and challenges. All of this is discussed in detail as below:

Drivers

• Increasing use of emerging technologies to gain valuable insights

The continuous developments in big data, machine-to- machine (M2M) communication, and artificial intelligence have created new possibilities for the disquisition of information deduced from artificial means. IoT bias induce a huge quantum of data from various sources, similar as detectors, cameras, and other connected bias. The data, still, doesn't give any value by itself unless anybody converts it into practicable, contextual information. Big data and data visualization ways enable druggies to gain new perceptivity through batch processing and offline analysis. Real- time data analysis and decision- timber are frequently done manually; but to make it scalable, it's preferred to be done automatically. The main part of AI technology is to probe huge volumes of data produced by various factors of the IoT ecosystem and transfigure the data into meaningful perceptivity. Enterprises are integrating AI into their predefined logical models to automate the data interpretation process and gain real- time perceptivity from the data generated from these IoT bias. AI provides enterprises with fabrics and tools to dissect real- time data and decide multiple use cases for IoT.

• Increased number of industries globally to induce greater demand and supply in emerging nations  

Growing number of small and medium scale enterprises all around the globe is one of the major factors fostering the growth of the market. In other words, increased number of banking, financial services, and insurance (BFSI), government and public sector, healthcare and life sciences, manufacturing, retail and e-commerce, telecommunication, and IT industries, is directly influencing the growth rate of the market.

Opportunities

• Real-time condition monitoring to assist in taking prompt actions

Advanced asset operation is decreasingly demanded across nearly every perpendicular. Result providers equipped with AI and ML can collect and turn the vast quantum of client- related data into meaningful perceptivity, as IoT generates a huge quantum of data from connected bias. AI can also be integrated with the IoT bias to optimize various aspects of service delivery, similar as prophetic conservation and quality assessment, without the need for any mortal intervention. AI- grounded IoT results are formerly being espoused in various diligence, and this would only grow as the technology matures. The nonstop developments in big data, M2M communication, enable condition monitoring in real- time. The real- time inputs from detectors, selectors, and other control parameters would not only prognosticate embryonic asset failures but also help companies cover in real- time and take prompt conduct.

Restrictions/Challenges

• Lack of skilled workforce

Trained workers are needed to handle the rearmost software systems to emplace AI- grounded IoT technologies and skillsets. Hence, being workers are needed to be trained on how to operate new and upgraded systems. Also, diligence are dynamic toward espousing new technologies; still, they're facing a deficit of largely professed pool and complete workers. As utmost of the global merchandisers are organizing prophetic conservation systems, the demand for a largely professed pool is adding. Companies need to acquire moxie in areas, similar as cybersecurity, networking, and operations. Also, they seek to use IoT data for prognosticating issues, precluding failures, optimizing operations, developing new products, furnishing advanced analytics faculty, which includes AI and ML. These technologies would play a critical part in the overall reduction of functional costs. Also, with enterprises integrating AI in IoT, there would be a growing need for functional intelligence- acquainted data critic brigades to handle huge quantities of data generated from IoT bias.

• Frequent maintenance and upgradation requirement to keep the systems updated

Enterprises are espousing AI- grounded IoT results for prophetic conservation and enhanced client experience. The merchandisers in the request must develop prophetic conservation systems considering two important factors, videlicet conservation and updates. An AI- grounded IoT system needs to be streamlined and maintained as per the changing business conditions to apply technological upgrades. The software also needs to be upgraded, as new factors are added. The new system must be integrated with the being one, as well as the fresh one. With an increase in the number of systems, the conservation cost also increases. Maintaining and upgrading AI- grounded IoT systems is going to be a grueling task for companies that offer results without any interruption.

This  predictive maintenance market report provides details of new recent developments, trade regulations, import-export analysis, production analysis, value chain optimization, market share, impact of domestic and localized market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on the predictive maintenance market contact Data Bridge Market Research for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

COVID-19 Impact on Predictive Maintenance Market

COVID– 19 has encyclopaedically changed the dynamics of business operations. Though the COVID-19 outbreak has thrown light on sins in business models across verticals, it has offered several openings to digitalize and expand their business across regions as the relinquishment and integration of technologies similar as AI, analytics, IoT, and blockchain has increased in the lockdown period. The retail and manufacturing sectors faced a significant dip in business performance during the first and alternate diggings of 2020. Still, with the vacuity of vaccines and considerable control achieved over the epidemic, these sectors are anticipated to witness rising investments throughout the cast period as prophetic conservation results grow in elevation across different business functions.

Predictive Maintenance Market Scope

The predictive maintenance market is segmented on the basis of component, deployment mode, organization size, vertical, stakeholder. The growth amongst these segments will help you analyze meagre growth segments in the industries and provide the users with a valuable market overview and market insights to help them make strategic decisions for identifying core market applications.

Component

• Solutions
  • Integrated
  • Standalone

• Service
  • Managed Services
  • Professional Services
    • System Integration
    • Support and Maintenance
    • Consulting

System Integration

• Support and Maintenance
• Consulting

 Deployment Mode

• On-premises
• Cloud
  • Public Cloud
  • Private Cloud
  • Hybrid Cloud

 Organization Size

• Large Enterprises
• Small and Medium-sized Enterprises (SMEs)

 Vertical

• Government and Defense
• Manufacturing
• Energy and Utilities
• Transportation and Logistics
• Healthcare and Life Sciences

Stakeholder

• MRO
• OEM/ODM
• Technology Integrators

Predictive Maintenance Market Regional Analysis

The  predictive maintenance market is analysed and market size insights and trends are provided by country, component, deployment mode, organization size, vertical, stakeholder as referenced above.

The countries covered in the  predictive maintenance market report are U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.

North America is predicted to own the most important market share within the prognostic maintenance market. Key factors affirmative the expansion of the prognostic maintenance market in North America embrace the increasing technological advancements within the region. The growing range of prognostic maintenance players across regions is predicted to more drive market growth. However, Asia Pacific will show a steady rise in the adoption of predictive maintenance due to the emerging economies, technological advancement and the need to adopt latest technological innovations for achieving optimum output through proper maintenance of assets.

The country section of the report also provides individual market impacting factors and changes in market regulation that impact the current and future trends of the market. Data points like down-stream and upstream value chain analysis, technical trends and porter's five forces analysis, case studies are some of the pointers used to forecast the market scenario for individual countries. Also, the presence and availability of global brands and their challenges faced due to large or scarce competition from local and domestic brands, impact of domestic tariffs and trade routes are considered while providing forecast analysis of the country data.   

Predictive Maintenance Market Share

The predictive maintenance market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, global presence, production sites and facilities, production capacities, company strengths and weaknesses, product launch, product width and breadth, application dominance. The above data points provided are only related to the companies' focus related to predictive maintenance market.

Predictive Maintenance Market Leaders Operating in the Market Are:

• Microsoft(US)
• IBM(US)
• SAP(Germany)
• SAS Institute Inc. (US)
• Software AG (Germany)
• TIBCO Software Inc.(US)
• Hewlett Packard Enterprise Development LP (US)
• Altair Engineering Inc. (US)
• Splunk Inc. (US)
• Oracle (US)
• Google (US)
• Amazon Web Services, Inc. (US)
• General Electric (US)
• Schneider Electric (France)
• Hitachi, Ltd. (Japan)
• PTC (US)
• RapidMiner, Inc. (US)
• Operational Excellence (OPEX) Group Ltd, (UK)
• Dingo (Australia)
• Factory5 (Russia)

Últimos avances en el mercado del mantenimiento predictivo

• En julio de 2021, Schneider Electric lanzó EcoStruxure TriconexTM Safety View, el primer software de operación de alarmas y derivaciones con certificación de seguridad y ciberseguridad binaria de Asiduity que permite a los conductores ver tanto el estado de la derivación que afecta la posición de reducción de amenazas en el lugar, como las advertencias críticas necesarias para operar la fábrica de manera segura cuando los peligros son altos.
• En mayo de 2021, SAS Institute lanzó su plataforma SAS Viya para respaldar la base del éxito lógico y de los datos al incorporar nuevos resultados de operaciones de datos en su importante plataforma nativa SASViya.

SKU-14001