市場調查報告書
商品編碼
1471423
預測性維護市場:按組件、部署、最終用戶分類 - 2024-2030 年全球預測Predictive Maintenance Market by Component (Services, Solutions), Deployment (On-Cloud, On-Premise), End-User - Global Forecast 2024-2030 |
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預測性維護市場規模預計2023年為106.4億美元,2024年達到130.9億美元,預計2030年將達到480.7億美元,複合年成長率為24.02%。
預測性維護使用連網型連網技術來預測和防止設備因故障而停機。以最具成本效益的方式了解關鍵工廠系統的運作狀態,包括振動監測、熱成像和摩擦學,並在必要時根據這些歷史資料安排所有維護活動。當納入更廣泛的維護管理計劃時,預測性維護可以最佳化製程設備的可用性並顯著降低維護成本。由於工業 4.0 以及政府不斷提高的安全標準,工業流程自動化和機械化支出的增加正在推動預測性維護。即時監控和資產追蹤技術也不斷實施。另一方面,整合和執行的高成本和複雜性,以及對資料安全和隱私的擔憂,阻礙了它們的採用。然而,透過人工智慧、物聯網和檢查技術的整合來改善預測性維護,以及基於數位雙胞胎的預測維修系統的出現和發展,預計未來幾年將增加其使用。
主要市場統計 | |
---|---|
基準年[2023] | 106.4億美元 |
預測年份 [2024] | 130.9億美元 |
預測年份 [2030] | 480.7億美元 |
複合年成長率(%) | 24.02% |
組件預測維護解決方案的需求量很大,因為它們可以顯著減少意外的機器故障
預測性維護託管服務意味著外包維護業務。公司依靠外部專家使用先進的分析來持續監控設備、預測潛在故障並執行必要的維護作業。專業服務分為諮詢、支援和維護以及系統整合,提供量身定做的預測性維護方法。諮詢服務在為實施預測性維護的最佳實踐和策略提供建議方面發揮著重要作用。顧問評估公司目前的基礎設施,預測潛在故障,提案降低風險的解決方案,並幫助公司做出有關預測性維護策略的明智決策。支援和維護提供預測維修系統的持續支援和維護。提供者提供解決問題的支援並定期更新系統以確保有效運行,最大限度地降低意外設備故障的風險。系統整合是預測性維護和與現有系統協調的重要服務。
此整合器使所有業務營運組件能夠有效地進行無縫資料流通訊,以實現準確的預測和維護計劃。整合預測維護解決方案是整合到現有基礎設施和機械中的綜合系統。這些解決方案旨在與其他操作技術無縫協作,提供全面的維護管理方法。跨多個點收集和分析資料,以深入了解設備何時發生故障以及系統如何互連和相互影響。另一方面,獨立的預測維護解決方案旨在獨立工作。它們通常專門用於監控特定設備或組件,本質上並不用於與其他系統整合。獨立系統對於針對特定關注領域的公司或在致力於更大的整合系統之前試點預測性維護功能非常有用。
最終用戶:增加汽車和運輸業的採用,以確保車輛和運輸系統的高效和安全運行。
在航太和國防領域,預測性維護透過及早發現潛在故障、最佳化機隊性能和任務準備狀態,確保飛機和國防設備的安全和準備狀態。在汽車和運輸領域,預測性維護透過預測和預防車輛零件的故障來提高車輛的可靠性和安全性,並降低維護成本。預測性維護提高了銀行系統和基礎設施的可靠性,最大限度地減少停機時間並實現無縫的金融業務。在建築和房地產行業,預測性維護可以防止空調和電梯等關鍵建築系統發生意外故障,確保居住者的安全和舒適。預測性維護可維持生產線和庫存系統不間斷運作,確保穩定的產品供應和客戶滿意度。預測性維護使教育機構的設施和技術設備保持最佳狀態,並支援持續的學習環境。預測性維護可預測能源生產和分配系統的故障,確保不間斷的服務提供並提高安全性。
在政府和公共部門,預測性維護提高了從交通到公共的公共基礎設施和服務的可靠性,並提高了公民滿意度和安全性。預測性維護可確保醫療設備和設施始終運作,從而改善患者護理和安全。在資訊科技和通訊領域,預測性維護可減少系統停機時間,並確保在日益數位化的世界中持續連接和服務可用性。在製造業中,預測性維護透過預測機器故障並最佳化營運效率和產品品質來最大限度地減少生產中斷。在媒體和娛樂領域,預測性維護可確保廣播和製作設備的可用性和可靠性,從而確保向觀眾無縫內容傳送。預測性維護可確保交通和飯店設施的可靠性和安全性,從而提高客戶體驗和滿意度。
區域洞察
在美洲,特別是在美國和加拿大,預測性維護技術正在被多種產業採用,包括製造、能源和運輸。領先技術供應商的存在以及對創新和採用物聯網 (IoT) 解決方案進行即時監控和分析的強烈關注推動了市場成長。亞太地區預測性維護市場成長迅速。這主要是由於中國、印度和日本等國家製造業的擴張。該地區的公司越來越認知到預測性維護工具可以減少停機時間並提高生產力。該地區各國政府也正在推動數位轉型和先進技術在產業中的使用,進一步推動預測性維護市場的成長。物聯網的採用以及人工智慧 (AI) 和機器學習 (ML) 的進步是推動亞太地區預測性維護市場的關鍵因素。預測性維護解決方案正在 EMEA(歐洲、中東和非洲)地區迅速採用,特別是在德國、英國和法國等國家。該地區對永續性和效率的關注也推動了預測性維護技術的廣泛採用。特別是在石油和天然氣領域,預測性維護在最佳化營運和減少環境影響方面發揮關鍵作用。
FPNV定位矩陣
FPNV定位矩陣對於評估預測性維護市場至關重要。我們檢視與業務策略和產品滿意度相關的關鍵指標,以對供應商進行全面評估。這種深入的分析使用戶能夠根據自己的要求做出明智的決策。根據評估,供應商被分為四個成功程度不同的像限:前沿(F)、探路者(P)、利基(N)和重要(V)。
市場佔有率分析
市場佔有率分析是一種綜合工具,可以對預測維護市場中供應商的現狀進行深入而深入的研究。全面比較和分析供應商在整體收益、客戶群和其他關鍵指標方面的貢獻,以便更好地了解公司的績效及其在爭奪市場佔有率時面臨的挑戰。此外,該分析還提供了對該行業競爭特徵的寶貴見解,包括在研究基準年觀察到的累積、分散主導地位和合併特徵等因素。詳細程度的提高使供應商能夠做出更明智的決策並制定有效的策略,從而在市場上獲得競爭優勢。
1. 市場滲透率:提供有關主要企業所服務的市場的全面資訊。
2. 市場開拓:我們深入研究利潤豐厚的新興市場,並分析其在成熟細分市場的滲透率。
3. 市場多元化:提供有關新產品發布、開拓地區、最新發展和投資的詳細資訊。
4.競爭評估與資訊:對主要企業的市場佔有率、策略、產品、認證、監管狀況、專利狀況、製造能力等進行全面評估。
5. 產品開發與創新:提供對未來技術、研發活動和突破性產品開發的見解。
1.預測性維護市場的市場規模與預測為何?
2.預測維護市場預測期內需要考慮投資的產品、細分市場、應用程式和領域有哪些?
3.預測性維護市場的技術趨勢和法規結構是什麼?
4.預測性維護市場主要廠商的市場佔有率為何?
5.進入預測性維護市場的合適型態和策略手段是什麼?
[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 | |
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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?