市场调查报告书
商品编码
1471423
预测性维护市场:按组件、部署、最终用户划分 - 2024-2030 年全球预测Predictive Maintenance Market by Component (Services, Solutions), Deployment (On-Cloud, On-Premise), End-User - Global Forecast 2024-2030 |
※ 本网页内容可能与最新版本有所差异。详细情况请与我们联繫。
预测性维护市场规模预计2023年为106.4亿美元,2024年达到130.9亿美元,预计2030年将达到480.7亿美元,复合年增长率为24.02%。
预测性维护使用连网型连网技术来预测和防止设备因故障而停机。以最具成本效益的方式了解关键工厂系统的运作状态,包括振动监测、热成像和摩擦学,并在必要时根据这些历史资料安排所有维护活动。当纳入更广泛的维护管理计划时,预测性维护可以优化製程设备的可用性并显着降低维护成本。由于工业 4.0 以及政府不断提高的安全标准,工业流程自动化和机械化支出的增加正在推动预测性维护。即时监控和资产追踪技术也不断实施。另一方面,整合和执行的高成本和复杂性,以及对资料安全和隐私的担忧,阻碍了它们的采用。然而,透过人工智慧、物联网和检查技术的整合来改善预测性维护,以及基于数位双胞胎的预测维修系统的出现和发展,预计未来几年将增加其使用。
主要市场统计 | |
---|---|
基准年[2023] | 106.4亿美元 |
预测年份 [2024] | 130.9亿美元 |
预测年份 [2030] | 480.7亿美元 |
复合年增长率(%) | 24.02% |
组件预测维护解决方案的需求量很大,因为它们可以显着减少意外的机器故障
预测性维护託管服务意味着外包维护业务。公司依靠外部专家使用先进的分析来持续监控设备、预测潜在故障并执行必要的维护作业。专业服务分为咨询、支援和维护以及系统集成,提供量身定制的预测性维护方法。咨询服务在为实施预测性维护的最佳实践和策略提供建议方面发挥着重要作用。顾问评估公司目前的基础设施,预测潜在故障,提案降低风险的解决方案,并帮助公司做出有关预测性维护策略的明智决策。支援和维护提供预测维修系统的持续支援和维护。提供者提供解决问题的支援并定期更新系统以确保有效运行,最大限度地降低意外设备故障的风险。系统整合是预测性维护和与现有系统协调的重要服务。
此整合器使所有业务营运组件能够有效地进行无缝资料流通讯,以实现准确的预测和维护计划。整合预测维护解决方案是整合到现有基础设施和机械中的综合系统。这些解决方案旨在与其他操作技术无缝协作,提供全面的维护管理方法。跨多个点收集和分析资料,以深入了解设备何时发生故障以及系统如何互连和相互影响。另一方面,独立的预测维护解决方案旨在独立工作。它们通常专门用于监控特定设备或组件,本质上并不用于与其他系统整合。独立系统对于针对特定关注领域的公司或在致力于更大的整合系统之前试点预测性维护功能非常有用。
最终用户:增加汽车和运输业的采用,以确保车辆和运输系统的高效和安全运行。
在航太和国防领域,预测性维护透过及早发现潜在故障、优化机队性能和任务准备状态,确保飞机和国防设备的安全和准备状态。在汽车和运输领域,预测性维护透过预测和预防车辆零件的故障来提高车辆的可靠性和安全性,并降低维护成本。预测性维护提高了银行系统和基础设施的可靠性,最大限度地减少停机时间并实现无缝的金融业务。在建筑和房地产行业,预测性维护可以防止空调和电梯等关键建筑系统发生意外故障,确保居住者的安全和舒适。预测性维护可维持生产线和库存系统不间断运作,确保稳定的产品供应和客户满意度。预测性维护使教育机构的设施和技术设备保持最佳状态,并支援持续的学习环境。预测性维护可预测能源生产和分配系统的故障,确保不间断的服务提供并提高安全性。
在政府和公共部门,预测性维护提高了从交通到公共的公共基础设施和服务的可靠性,并提高了公民满意度和安全性。预测性维护可确保医疗设备和设施始终运作,从而改善患者护理和安全。在资讯科技和通讯领域,预测性维护可减少系统停机时间,并确保在日益数位化的世界中持续连接和服务可用性。在製造业中,预测性维护透过预测机器故障并优化营运效率和产品品质来最大限度地减少生产中断。在媒体和娱乐领域,预测性维护可确保广播和製作设备的可用性和可靠性,从而确保向观众无缝内容传送。预测性维护可确保交通和饭店设施的可靠性和安全性,从而提高客户体验和满意度。
区域洞察
在美洲,特别是在美国和加拿大,预测性维护技术正在被多种产业采用,包括製造、能源和运输。领先技术供应商的存在以及对创新和采用物联网 (IoT) 解决方案进行即时监控和分析的强烈关注推动了市场成长。亚太地区预测性维护市场成长迅速。这主要是由于中国、印度和日本等国家製造业的扩张。该地区的公司越来越认识到预测性维护工具可以减少停机时间并提高生产力。该地区各国政府也正在推动数位转型和先进技术在产业中的使用,进一步推动预测性维护市场的成长。物联网的采用以及人工智慧 (AI) 和机器学习 (ML) 的进步是推动亚太地区预测性维护市场的关键因素。预测性维护解决方案正在 EMEA(欧洲、中东和非洲)地区迅速采用,特别是在德国、英国和法国等国家。该地区对永续性和效率的关注也推动了预测性维护技术的广泛采用。特别是在石油和天然气领域,预测性维护在优化营运和减少环境影响方面发挥关键作用。
FPNV定位矩阵
FPNV定位矩阵对于评估预测性维护市场至关重要。我们检视与业务策略和产品满意度相关的关键指标,以对供应商进行全面评估。这种深入的分析使用户能够根据自己的要求做出明智的决策。根据评估,供应商被分为四个成功程度不同的像限:前沿(F)、探路者(P)、利基(N)和重要(V)。
市场占有率分析
市场占有率分析是一种综合工具,可以对预测维护市场中供应商的现状进行深入而深入的研究。全面比较和分析供应商在整体收益、客户群和其他关键指标方面的贡献,以便更好地了解公司的绩效及其在争夺市场占有率时面临的挑战。此外,该分析还提供了对该行业竞争特征的宝贵见解,包括在研究基准年观察到的累积、分散主导地位和合併特征等因素。详细程度的提高使供应商能够做出更明智的决策并制定有效的策略,从而在市场上获得竞争优势。
1. 市场渗透率:提供有关主要企业所服务的市场的全面资讯。
2. 市场开拓:我们深入研究利润丰厚的新兴市场,并分析其在成熟细分市场的渗透率。
3. 市场多元化:提供有关新产品发布、开拓地区、最新发展和投资的详细资讯。
4.竞争评估与资讯:对主要企业的市场占有率、策略、产品、认证、监管状况、专利状况、製造能力等进行全面评估。
5. 产品开发与创新:提供对未来技术、研发活动和突破性产品开发的见解。
1.预测性维护市场的市场规模与预测为何?
2.预测维护市场预测期内需要考虑投资的产品、细分市场、应用程式和领域有哪些?
3.预测性维护市场的技术趋势和法规结构是什么?
4.预测性维护市场主要厂商的市场占有率为何?
5.进入预测性维护市场的合适型态和策略手段是什么?
[190 Pages Report] The Predictive Maintenance Market size was estimated at USD 10.64 billion in 2023 and expected to reach USD 13.09 billion in 2024, at a CAGR 24.02% to reach USD 48.07 billion by 2030.
Predictive maintenance involves using intelligent connected technologies to predict and prevent equipment downtime due to failures. The operating condition of critical plant systems is obtained using the most cost-effective methods, such as vibration monitoring, thermography, and tribology, and all maintenance activities are then scheduled based on this actual data as needed. Predictive maintenance optimizes the availability of process equipment and significantly lowers maintenance costs when incorporated into an extensive maintenance management program. Increasing spending on automating and mechanizing industrial processes with Industry 4.0, coupled with rising safety standards enforced by governments in different countries, encourages predictive maintenance. Real-time monitoring and asset-tracking technologies are also being continuously deployed. On the contrary, high costs and complexity of integration and execution, and concern over data security and privacy, impedes their adoption. However, AI, IoT, and inspection technology integration to improve predictive maintenance and digital twin-based predictive maintenance systems' emergence and development are expected to proliferate their utilization in the coming years.
KEY MARKET STATISTICS | |
---|---|
Base Year [2023] | USD 10.64 billion |
Estimated Year [2024] | USD 13.09 billion |
Forecast Year [2030] | USD 48.07 billion |
CAGR (%) | 24.02% |
Component: Rising demand for predictive maintenance solutions due to their ability to significantly reduce unexpected machinery breakdowns
Managed services in predictive maintenance involve the outsourcing of maintenance operations. Companies rely on external experts to continuously monitor equipment using advanced analytics, foresee potential failures, and perform necessary maintenance actions. Professional services are divided into consulting, support & maintenance, and system integration, offering a tailored approach to predictive maintenance. Consulting services play a crucial role by advising organizations on the best practices and strategies for implementing predictive maintenance. Consultants assess the company's current infrastructure, predict potential failures, and recommend solutions to mitigate risks, helping businesses make informed decisions on their predictive maintenance strategies. Support & maintenance involves ongoing assistance and upkeep of predictive maintenance systems. Providers offer support to resolve issues and regularly update the systems to ensure they operate effectively, minimizing the risk of unexpected equipment failures. System Integration services are crucial for combining predictive maintenance technology with existing systems.
Integrators ensure that all business operations' components communicate effectively, enabling seamless data flow for accurate predictions and maintenance scheduling. Integrated predictive maintenance solutions are comprehensive systems often embedded within the existing infrastructure or machinery. These solutions are designed to work seamlessly with other operational technologies, providing a holistic approach to maintenance management. They collect and analyze data across various points, offering insights on when a piece of equipment might fail and how the systems interconnect and impact each other. On the other hand, standalone predictive maintenance solutions are designed to function independently. They are typically focused on specific equipment or component monitoring and are not inherently designed to integrate with other systems. Standalone systems benefit companies targeting particular areas of concern or piloting predictive maintenance capabilities before committing to a larger integrated system.
End-User: Rising adoption in the automotive & transportation sector to ensure vehicles and transport systems operate efficiently and safely.
In aerospace & defense, predictive maintenance ensures the safety and readiness of aircraft and defense equipment through early detection of potential failures, optimizing fleet performance and mission readiness. In automotive & transportation, predictive maintenance improves vehicle reliability and safety while reducing maintenance costs by predicting and preventing vehicle component failures. Predictive maintenance enhances the reliability of banking systems and infrastructure, minimizing downtime and ensuring seamless financial operations. In building, construction & real estate, predictive maintenance prevents unforeseen breakdowns of critical building systems, such as HVAC and elevators, ensuring occupants' safety and comfort. Predictive maintenance ensures uninterrupted operation of production lines and inventory systems, leading to consistent product availability and customer satisfaction. Predictive maintenance keeps educational institutions' facilities and technological equipment in optimal condition, supporting a continuous learning environment. Predictive maintenance predicts energy production and distribution system failures, ensuring uninterrupted service delivery and enhanced safety.
In the government & public sector, predictive maintenance enhances the reliability of public infrastructure and services, from transportation to utilities, improving citizen satisfaction and safety. Predictive maintenance ensures medical equipment and facilities are always operational, enhancing patient care and safety. In information technology & telecommunication, predictive maintenance reduces system downtimes, ensuring continuous connectivity and service availability in our increasingly digital world. In manufacturing, predictive maintenance minimizes production interruptions by anticipating machinery breakdowns and optimizing operational efficiency and product quality. In media & entertainment, predictive maintenance guarantees broadcasting and production equipment availability and reliability, ensuring seamless content delivery to audiences. Predictive maintenance ensures the reliability and safety of transportation modes and hospitality facilities, enhancing customer experiences and satisfaction.
Regional Insights
In the Americas, particularly in the United States and Canada, predictive maintenance technologies are significantly adopted across various industries, such as manufacturing, energy, and transportation. The market growth is fueled by the presence of major technology providers and a strong focus on innovation and adopting Internet of Things (IoT) solutions for real-time monitoring and analysis. The Asia-Pacific region showcases rapid growth in the predictive maintenance market, primarily due to the expanding manufacturing sector in countries including China, India, and Japan. There's a growing awareness among businesses in the region about the benefits of using predictive maintenance tools for reducing downtime and increasing productivity. In addition, governments in the region are promoting digital transformation and the usage of advanced technologies in industries, further propelling predictive maintenance market growth. The adoption of IoT and advancements in artificial intelligence (AI) and machine learning (ML) are key factors driving the predictive maintenance market in the Asia-Pacific region. In the EMEA region, Europe showcases rapid adoption of predictive maintenance solutions, especially in countries such as Germany, the UK, and France. The region's emphasis on sustainability and efficiency has also increased the adoption of predictive maintenance technologies. In the Middle East and Africa, the market is emerging and showcases promising growth potential, especially in the oil & gas sector, where predictive maintenance can be crucial in optimizing operations and reducing environmental impacts.
FPNV Positioning Matrix
The FPNV Positioning Matrix is pivotal in evaluating the Predictive Maintenance Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).
Market Share Analysis
The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the Predictive Maintenance Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.
Key Company Profiles
The report delves into recent significant developments in the Predictive Maintenance Market, highlighting leading vendors and their innovative profiles. These include Altair Engineering Inc., Amazon Web Services, Inc., Asystom, C3.ai, Inc., Databricks, Inc., DINGO Software Pty. Ltd., Fiix Inc. by Rockwell Automation, Inc., General Electric Company, Hitachi, Ltd., Honeywell International Inc., Infineon Technologies AG, Intel Corporation, International Business Machines Corporation, Limble Solutions, LLC, Micro Focus International PLC by Open Text Corporation, Microsoft Corporation, NVIDIA Corporation, Operational Excellence (OPEX) Group Ltd., Oracle Corporation, Robert Bosch GmbH, SAP SE, Schneider Electric SE, Siemens AG, Software AG, SparkCognition, Inc., Splunk Inc., TIBCO Software Inc. by Cloud Software Group, Inc., TWI Ltd., and Uptake Technologies Inc..
Market Segmentation & Coverage
1. Market Penetration: It presents comprehensive information on the market provided by key players.
2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.
3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.
4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.
5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.
1. What is the market size and forecast of the Predictive Maintenance Market?
2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the Predictive Maintenance Market?
3. What are the technology trends and regulatory frameworks in the Predictive Maintenance Market?
4. What is the market share of the leading vendors in the Predictive Maintenance Market?
5. Which modes and strategic moves are suitable for entering the Predictive Maintenance Market?