세계의 제조업 예지보전 시장 규모 : 구성 요소별, 배포별, 조직 규모별, 기술별, 수법별, 업계별, 지역 범위별 예측

Global Predictive Maintenance For Manufacturing Industry Market Size By Component, By Deployment, By Organization Size, By Technology, Technique, By Verticals, By Geographic Scope And Forecast

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한글목차

제조업 예지보전 시장 규모와 예측

제조업 예지보전 시장 규모는 2024년에 82억 6,000만 달러로 평가되었고, 2026-2032년의 CAGR은 24.49%를 나타내, 2032년에는 476억 4,000만 달러에 달할 것으로 예측됩니다.

• 제조업 예지보전은 데이터 분석 툴과 수법을 채용하여 운용 프로세스나 기계의 이상을 검출합니다.
• 이 기술은 기계 및 설비의 성능을 모니터링하기 위해 생산 현장에서 사용됩니다. 기계가 원활하고 효과적으로 가동되도록 보장합니다. 일반적인 용도로는 CNC 기계, 컨베이어 시스템, 로봇 암 모니터링 등이 있습니다.
• 제조업 예지보전은 IoT 센서, 데이터 분석 플랫폼, 머신러닝 알고리즘의 통합을 수반합니다.기본 기능에는 실시간 데이터 수집, 이상 감지, 예측 분석, 자동 경고 등이 있습니다. 자원 계획(ERP) 시스템과의 상호 작용, 의사 결정 지원 도구가 포함될 수 있습니다.

세계의 제조업 예지보전 시장 역학

세계의 제조업 예지보전 시장을 형성하고 있는 주요 시장 역학은 다음과 같습니다.

주요 시장 성장 촉진요인

• IoT와 센서 기술의 발전 : IoT와 센서 기술은 제조 분야에서 데이터 수집 및 분석에 변화를 가져왔습니다. 건전성을 실시간으로 모니터링합니다. 지속적으로 고해상도 데이터를 수집할 수 있기 때문에 보다 정확한 예지보전 모델이 가능해져, 계획외의 다운타임이 단축되어, 보전 스케줄이 최적화됩니다.
• 빅데이터 및 분석 채택 확대 : 빅데이터 분석의 도입이 진행됨에 따라 제조업체는 기계가 생성하는 대량의 데이터를 평가할 수 있게 되었습니다. 이와 같은 데이터 주도의 전략에 의해 제조자는 유지관리 스케줄, 자원의 할당, 프로세스의 강화에 대해서 충분한 정보에 근거한 의사 결정을 실시할 수 있어 그 결과, 업무 효율의 향상과 다운타임의 단축이 실현됩니다.
• 엔터프라이즈 시스템과의 통합 : 예지보전 솔루션을 ERP 및 CMMS와 같은 엔터프라이즈 시스템과 통합하여 산업 운영을 종합적으로 파악할 수 있습니다. 이 쉬운 인터페이스를 통해 제조업체는 유지 보수 활동을 생산 일정과 일치시키고 워크 플로우를 간소화하고 부서 간 협력 체제를 강화할 수 있습니다. 그 결과 전반적인 기업 목표를 충족하는 보다 효율적이고 신속한 유지보수 접근 방식이 실현됩니다.
• 기술 혁신과 AI의 통합 : AI와 머신러닝의 진보는 예지보전 시스템을 크게 개선했습니다. AI를 활용한 예측 모델은 대규모 데이터 세트를 조사하고, 미묘한 패턴을 감지하고, 보다 정확하게 고장을 예측할 수 있습니다. AI와 머신러닝 알고리즘의 지속적인 개선은 예측 보전의 정확성과 신뢰성을 향상시키고, 제조업 도입을 가속화할 것으로 예측됩니다.

주요 이슈

• 고액의 초기 투자 및 ROI 우려 : 예지보전 계획을 구현하려면 IoT 센서 및 데이터 분석 플랫폼 구매 및 설치, 기존 인프라 업그레이드 등 대규모 선행 투자가 필요합니다. 많은 제조업체, 특히 중소기업(SME)의 경우, 이러한 초기 비용은 큰 장애가 될 수 있습니다. 명확한 투자 수익률(ROI)을 보여주는 것은 다운타임 감소와 장비 수명 연장과 같은 예지보전의 이점이 반드시 명확하지 않기 때문에 어려울 수 있습니다. 제조업체는 비용 효과를 신중하게 평가하고 장기 절약과 단기 지출을 저울에 넣어야합니다.
• 사이버 보안 위험 : 예측 보전 시스템의 연결과 데이터 교환의 확대는 제조 업무에 사이버 보안 문제를 초래합니다. IoT 장비와 데이터 전송 네트워크는 사이버 공격의 대상이 되어 데이터 유출, 운영 중단, 장비 파괴 공작을 일으킬 수 있습니다. 민감한 데이터를 보호하고 예비보전(PdM) 시스템의 무결성을 보장하기 위해서는 강력한 사이버 보안 대책이 필요합니다.
• 확장성 문제 : 예지보전의 규모를 파일럿 프로젝트에서 모든 기기나 설비로의 본격적인 전개로 확대하는 것은 과제가 될 가능성이 있습니다. 그렇다고는 할 수 없습니다. 규모를 확대하려면 새로운 센서, 데이터 스토리지, 처리 능력에 대한 대규모 투자가 자주 필요합니다.
• 규제 및 규정 준수 문제 : 제조 기업은 산업 특유의 규칙과 요구 사항을 준수해야합니다. 제조업체는 항상 관련 법규의 최신 정보를 얻고 회사의 PdM 시스템이 필요한 기준을 모두 충족하는지 확인해야 합니다.

주요 동향

• 클라우드 기반의 예지보전 솔루션 : 클라우드 컴퓨팅은 예지보전 데이터를 저장, 처리 및 평가하는 방법을 변화시키고 있습니다. T 인프라에 엄청난 재정 지출 없이 강력한 컴퓨팅 리소스를 활용할 수 있습니다.
• 인간과 기계의 협업 강화 : 예지보전 기술의 채용은 인간과 기계의 공동 작업 방법을 변화시키고 있습니다. 페이스, 증강현실(AR), 가상현실(VR) 시스템에 의해 개선되어 엔지니어가 유지보수 작업을 달성할 수 있게 됩니다.
• 디지털 트윈의 사용 : 디지털 트윈은 물리적인 물체, 시스템, 프로세스를 가상적으로 표현한 것입니다. 반지하고 발생할 수 있는 고장을 감지하고 유지보수 일정을 최적화할 수 있습니다.
• 맞춤형 예지보전 솔루션 : 생산 설정과 요구사항이 크게 다르기 때문에 특정 수요에 적합한 맞춤형 예지보전 솔루션에 대한 수요가 높아지고 있습니다.

목차

제1장 세계의 제조업 예지보전 시장 도입

• 시장 도입
• 조사 범위
• 전제조건

제2장 주요 요약

제3장 VERIFIED MARKET RESEARCH의 조사 방법

• 데이터 마이닝
• 밸리데이션
• 1차 자료
• 데이터 소스 일람

제4장 세계의 제조업 예지보전 시장 전망

• 개요
• 시장 역학
  • 성장 촉진요인
  • 성장 억제요인
  • 기회
• Porter's Five Forces 모델
• 밸류체인 분석

제5장 세계의 제조업 예지보전 시장 : 구성 요소별

• 개요
• 솔루션
  • 통합형
  • 독립형
• 서비스
  • 전문
  • 매니지드
• 하드웨어

제6장 세계의 제조업 예지보전 시장 : 배포별

• 개요
• 클라우드 기반
• 온프레미스

제7장 세계의 제조업 예지보전 시장 : 산업별

• 개요
• 정부 및 방위
• 제조업
• 에너지 및 유틸리티
• 운송 및 물류
• 헬스케어 및 생명과학

제8장 세계의 제조업 예지보전 시장 : 기술별

• 개요
• 인공지능(AI)
• 사물인터넷(IoT) 플랫폼
• 센서
• 기타

제9장 세계의 제조업 예지보전 시장 : 수법별

• 개요
• 오일 분석
• 진동 분석
• 음향 모니터링
• 모터 회로 분석
• 기타

제10장 세계의 제조업 예지보전 시장 : 조직 규모별

• 개요
• 중소기업
• 대기업

제11장 세계의 제조업 예지보전 시장 : 지역별

• 개요
• 북미
  • 미국
  • 캐나다
  • 멕시코
• 유럽
  • 독일
  • 영국
  • 프랑스
  • 기타 유럽
• 아시아태평양
  • 중국
  • 일본
  • 인도
  • 기타 아시아태평양
• 기타
  • 라틴아메리카
  • 중동 및 아프리카

제12장 세계의 제조업 예지보전 시장 경쟁 구도

• 개요
• 각사 시장 랭킹
• 주요 개발 전략

제13장 기업 프로파일

• IBM
• SAS Institute
• Robert Bosch GmbH
• Software AG
• Rockwell Automation
• eMaint Enterprises
• Schneider Electric
• General Electric
• Siemens
• PTC

제14장 부록

• 관련 조사

KTH

영문 목차

영문목차

Predictive Maintenance For Manufacturing Industry Market Size And Forecast

Predictive Maintenance For Manufacturing Industry Market size was valued at USD 8.26 Billion in 2024 and is projected to reach USD 47.64 Billion by 2032, growing at a CAGR of 24.49% from 2026 to 2032.

• Predictive Maintenance For Manufacturing Industry employs data analysis tools and methodologies to detect anomalies in operational processes and machinery. It seeks to anticipate when maintenance should be conducted, reducing unplanned downtime and optimizing maintenance plans. This strategy is based on condition-monitoring technology and the analysis of historical and real-time data from sensors installed in machinery.
• This technology is used in production to monitor the performance of machines and equipment. Predictive algorithms can anticipate probable failures by gathering data on temperature, vibration, noise, and other operational characteristics. This enables maintenance personnel to handle concerns proactively, ensuring that machines operate smoothly and effectively. Common uses include monitoring CNC machines, conveyor systems, and robotic arms. This method helps to prevent unplanned outages, increase equipment lifespan, and improve overall productivity and safety.
• Predictive maintenance in the manufacturing industry entails the integration of IoT sensors, data analytics platforms and machine learning algorithms. Key features include real-time data collection, anomaly detection, predictive analytics, and automatic warnings. Advanced predictive maintenance systems may additionally include dashboards for visualizing equipment status, interaction with enterprise resource planning (ERP) systems, and decision-support tools. Furthermore, these technologies allow for remote monitoring, historical data trend analysis, and automatic maintenance scheduling, all of which contribute to a more efficient and dependable production process.

Global Predictive Maintenance For Manufacturing Industry Market Dynamics

The key market dynamics that are shaping the global Predictive Maintenance For Manufacturing Industry Market include:

Key Market Drivers:

• Advancements in IoT and Sensor Technology: IoT and sensor technology have transformed data collection and analysis in manufacturing. These technologies provide real-time monitoring of equipment health, including vital factors like temperature, vibration, and pressure. The capacity to collect continuous, high-resolution data enables more accurate predictive maintenance models, which reduces unplanned downtime and optimizes the maintenance schedule.
• Increasing Adoption of Big Data and Analytics: Manufacturers may now evaluate large amounts of data generated by their machines thanks to the growing adoption of big data analytics. Advanced analytics tools and machine learning algorithms can detect patterns and predict equipment failures with great accuracy. This data-driven strategy enables manufacturers to make informed decisions about maintenance schedules, resource allocation, and process enhancements, resulting in increased operational efficiency and reduced downtime.
• Integration with Enterprise Systems: Integrating predictive maintenance solutions with enterprise systems, including ERP and CMMS, offers a comprehensive perspective of industrial operations. This effortless interface allows manufacturers to align maintenance activities with production schedules, streamline workflows, and increase departmental cooperation. The result is a more efficient and responsive maintenance approach that meets overall corporate objectives.
• Technological Innovations and AI Integration: Advancements in AI and machine learning have greatly improved predictive maintenance systems. AI-powered prediction models can examine large datasets, detect subtle patterns, and anticipate failures more accurately. Continuous improvements in AI and machine learning algorithms are projected to improve the precision and dependability of predictive maintenance, accelerating its adoption in the manufacturing industry.

Key Challenges:

• High Initial Investment and ROI Concerns: Implementing a predictive maintenance plan requires major upfront investments, such as purchasing and installing IoT sensors, data analytics platforms, and maybe upgrading existing infrastructure. For many manufacturers, particularly small and medium-sized firms (SMEs), these initial expenses might be a significant obstacle. Showing a clear return on investment (ROI) can be difficult because the benefits of predictive maintenance, such as reduced downtime and increased equipment lifespan, are not always obvious. Manufacturers must carefully assess the cost-benefit ratio and weigh long-term savings against short-term expenses.
• Cybersecurity Risks: Predictive maintenance systems' growing connection and data interchange offer cybersecurity issues for manufacturing operations. IoT devices and data transmission networks are subject to cyberattacks, which can result in data breaches, operational disruptions, and equipment sabotage. Strong cybersecurity measures are required to secure sensitive data and ensure the integrity of predictive maintenance (PdM) systems.
• Scalability Issues: Scaling predictive maintenance from pilot projects to full-scale deployment across all equipment and facilities might pose challenges. Different machines may necessitate unique sensors and data analytics methodologies, and what works in one area of the operation may not be directly applicable in another. Scaling up frequently necessitates large investments in new sensors, data storage, and processing power. Manufacturers must create scalable solutions that can be applied to a variety of equipment and operational conditions while ensuring consistency and reliability throughout the system.
• Regulatory and Compliance Issues: Manufacturing companies must adhere to industry-specific rules and requirements. These rules must be followed by predictive maintenance systems to assure operational safety, quality and dependability. However, negotiating the complicated world of regulatory regulations can be difficult, particularly when introducing new technologies. Manufacturers must stay current on relevant legislation and verify that their PdM systems meet all necessary criteria. This may necessitate additional documentation, reporting, and validation procedures, increasing the complexity and cost of implementation.

Key Trends:

• Cloud-based Predictive Maintenance Solutions: Cloud computing is changing the way predictive maintenance data is stored, processed, and evaluated. Cloud-based PdM solutions have various benefits, including scalability, adaptability, and cost-effectiveness. These technologies enable manufacturers to use strong computing resources without requiring large financial expenditure in IT infrastructure. Cloud platforms make it easier to aggregate and analyze huge datasets from various sources, resulting in more detailed insights about equipment performance and failure patterns.
• Enhanced Human-Machine Collaboration: The adoption of predictive maintenance technologies is changing the way humans and machines collaborate. Advanced PdM systems provide detailed insights and recommendations, allowing maintenance teams to make better decisions. Human-machine collaboration is improved by intuitive user interfaces, augmented reality (AR), and virtual reality (VR) systems that help technicians accomplish maintenance jobs. AR and VR can provide step-by-step instructions, display complex data, and mimic repair methods, hence increasing the efficiency and accuracy of maintenance activities.
• Use of Digital Twins: A digital twin is a virtual representation of a physical object, system, or process. In predictive maintenance, digital twins are utilized to mimic and assess equipment behavior under various scenarios. Manufacturers can create a digital twin of a machine to monitor its performance in real time, detect possible faults, and optimize maintenance schedules. Digital twins allow for extensive investigation and testing of many situations without affecting actual operations. This technology is gaining acceptance because it enables more precise and effective predictive maintenance strategies.
• Customized Predictive Maintenance Solutions: As production settings and requirements vary greatly, there is an increasing demand for customized predictive maintenance solutions that are suited to specific demands. Generic PdM solutions may fail to solve each manufacturer's specific difficulties and operational settings. Customized solutions include the individual types of equipment, operating conditions, and business objectives, resulting in more relevant and actionable data.

Global Predictive Maintenance For Manufacturing Industry Market Regional Analysis

Here is a more detailed regional analysis of the global Predictive Maintenance For Manufacturing Industry Market:

North America:

• North America's dominance in the manufacturing predictive maintenance market. The region benefits from a well-developed industrial environment, with a high concentration of production facilities in industries such as automotive, aerospace, electronics, and pharmaceuticals.
• These industries were early adopters of predictive maintenance systems, motivated by the need to reduce downtime, increase productivity, and maintain a competitive edge in the global market. The vibrant industrial ecosystem in North America promotes innovation and collaboration among industry participants, technology providers, and research institutes, resulting in rapid advancement and acceptance of predictive maintenance solutions.
• North America is at the forefront of technological innovation, particularly in the areas of artificial intelligence, machine learning, and the Internet of Things. The region is home to some of the world's best technology businesses and research organizations that specialize in advanced predictive analytics algorithms and IoT platforms designed for industrial applications.
• Furthermore, the availability of a trained workforce with experience in data science, engineering, and industrial automation has accelerated the region's adoption of predictive maintenance solutions. As manufacturers grasp the strategic relevance of predictive maintenance in improving operating efficiency, lowering costs, and increasing competitiveness, the demand for novel PdM technology grows, fueling North America's dominance in the industry.

Asia Pacific:

• The Asia Pacific region is expected to see significant expansion in the predictive maintenance industry in the near future. This spike is mostly driven by the region's growing industrialization, with countries such as China, India, and South Korea emerging as significant manufacturing centers. As these countries invest extensively in infrastructure development and industrial expansion, there is a stronger emphasis on implementing new technology to improve operational efficiency and productivity in manufacturing processes.
• Furthermore, the region's increased emphasis on upgrading its industrial sector coincides with an increase in demand for predictive maintenance solutions to prevent equipment breakdowns and save downtime.
• The Asia Pacific area has a large pool of technical expertise, which contributes to the quick adoption of cutting-edge technology like cloud-based predictive maintenance solutions. The growth of cloud computing platforms enables firms in the region to use scalable and cost-effective predictive maintenance solutions, allowing for real-time monitoring and analysis of equipment performance.
• As more businesses in the Asia Pacific recognize the transformative power of predictive maintenance in optimizing maintenance schedules, lowering costs, and improving overall operational performance, the market for PdM solutions is expected to grow exponentially, cementing the region's position as a key player in the global predictive maintenance market.

Global Predictive Maintenance For Manufacturing Industry Market: Segmentation Analysis

The Global Predictive Maintenance For Manufacturing Industry Market is Segmented on the basis of Component, Deployment, Verticals, Technology, Technique, Organization Size, And Geography.

Predictive Maintenance For Manufacturing Industry Market, By Component

• Solutions
• Integrated
• Standalone
• Services
• Professional
• Managed
• Hardware

Based on Component, The market is segmented into Solutions, Services, and Hardware. The solutions segment is projected to hold majority of the share in the market. This dominance is primarily due to there is constant requirement of using predictive analytics and data-driven information to speed up as well as improve maintenance process. The use of solutions in businesses is projected to help in cost saving and streamline maintenance in the manufacturing industry.

Predictive Maintenance For Manufacturing Industry Market, By Deployment

• Cloud-Based
• On Premise

Based on Deployment, The market is segmented into Cloud-based and On Premise. The predictive maintenance market for manufacturing is dominated by cloud-based solutions. Their scalability, low cost, and remote access make them suitable for enterprises of all sizes. While on-premise solutions continue to be deployed, their growth rate is slowing. The high upfront expenditures and maintenance strain of on-premise equipment are pushing the migration to cloud-based solutions.

Predictive Maintenance For Manufacturing Industry Market, By Verticals

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

Based on Verticals, the market is segmented into Government And Defense, Manufacturing, Energy And Utilities, Transportation And Logistics, and Healthcare And Life Sciences. The manufacturing sector has the largest proportion of the predictive maintenance market. Manufacturers stand to benefit significantly from proactive maintenance, which reduces downtime, optimizes production processes, and saves money. The energy and utilities sector is expected to see the most rapid adoption of predictive maintenance solutions. This is motivated by the desire for dependable and efficient electricity generation and distribution. Predictive maintenance can assist prevent equipment failures that cause power outages and interruptions.

Predictive Maintenance For Manufacturing Industry Market, By Technology

• Artificial Intelligence (AI)
• Internet of Things (IoT) Platform
• Sensors
• Others

Based on Technology, The market is segmented into Sensors, Internet of Things (IoT) Platforms, Artificial Intelligence (AI), and Others. The artificial intelligence segment is projected to dominate the market over the forecast period. The ease in training predictive maintenance models using historical data is surging the use of AI technology. Thus, the failure analysis helps understand the service demand and lower machine damage, repair costing, and optimize necessary components.

Predictive Maintenance For Manufacturing Industry Market, By Technique

• Oil Analysis
• Vibration Analysis
• Acoustic Monitoring
• Motor Circuit Analysis
• Others

Based on Technique, The market is segmented into Oil Analysis, Vibration Analysis, Acoustic Monitoring, Motor Circuit Analysis, and Others. Vibration analysis segment is projected to dominate the market over the forecast period. This technology helps detect the connectivity of sensors with the centralized system and offer real-time data. In addition to this, the oil analysis segment is projected to exhibit rapid growth as there is constant need for analysis of lubrication in the machinery in the manufacturing industry.

Predictive Maintenance For Manufacturing Industry Market, By Organization Size

• Small And Medium Enterprises
• Large Enterprises

Based on Organization Size, The market is segmented into Small And Medium Enterprises and Large Enterprises. The demand for large enterprise for handling the manufacturing, distribution, and selling products across wider range of supply chain is surging use of real-time tracking and maintenance technologies. Thus, the integration of predictive maintenance for manufacturing in the larger enterprises is projected to rise over the years.

Predictive Maintenance For Manufacturing Industry Market, By Geography

• North America
• Europe
• Asia Pacific
• Rest of the World

Based on Geography, The Global Predictive Maintenance For Manufacturing Industry Market is segmented into North America, Europe, Asia Pacific, and the Rest of the World. North America leads the market. This dominance can be attributed to a number of causes, including the strong presence of large manufacturing businesses, early adoption of advanced technologies such as AI and IoT, and government measures to promote industrial automation. The Asia-Pacific region is expected to experience the most rapid growth in the future years. This rapid expansion is being driven by causes such as rapid industrialization, increased government investment in infrastructure development, and a growing emphasis on enhancing operational efficiency in manufacturing.

Key Players

The "Global Predictive Maintenance For Manufacturing Industry Market" study report will provide valuable insight with an emphasis on the global market. The major players in the market are IBM, SAS Institute, ABB Ltd, Microsoft Corporation, Robert Bosch GmbH, Software AG, Rockwell Automation, eMaint Enterprises, Schneider Electric, Siemens, PTC, and General Electric. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis.

• Predictive Maintenance For Manufacturing Industry Market Recent Developments
• In June 2023, Predictive maintenance is at the forefront of digitalization initiatives in packaging and processing, and use is growing rapidly. This is according to PMMI Business Intelligence's 2023 research, "Sustainability and Technology - The Future of Packaging and Processing." In a poll of industry stakeholders performed for the report, 71% stated they used predictive maintenance technology, compared to 37% for collaborative robots, the next most popular digitalization endeavor.
• In April 2024, Predictive maintenance: Al's role in reducing production downtime Al uses powerful machine learning models to predict equipment faults.

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET

• 1.1 Introduction of the Market
• 1.2 Scope of Report
• 1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

• 3.1 Data Mining
• 3.2 Validation
• 3.3 Primary Interviews
• 3.4 List of Data Sources

4 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET OUTLOOK

• 4.1 Overview
• 4.2 Market Dynamics
  • 4.2.1 Drivers
  • 4.2.2 Restraints
  • 4.2.3 Opportunities
• 4.3 Porters Five Force Model
• 4.4 Value Chain Analysis

5 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY COMPONENT

• 5.1 Overview
• 5.2 Solutions
  • 5.2.1 Integrated
  • 5.2.2 Standalone
• 5.3 Services
  • 5.3.1 Professional
  • 5.3.2 Managed
• 5.4 Hardware

6 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY DEPLOYMENT

• 6.1 Overview
• 6.2 Cloud-based
• 6.3 On Premise

7 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY VERTICALS

• 7.1 Overview
• 7.2 Government And Defense
• 7.3 Manufacturing
• 7.4 Energy And Utilities
• 7.5 Transportation And Logistics
• 7.6 Healthcare And Life Sciences

8 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY TECHNOLOGY

• 8.1 Overview
• 8.2 Artificial Intelligence (AI)
• 8.3 Internet of Things (IoT) Platform
• 8.4 Sensors
• 8.5 Others

9 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY TECHNIQUE

• 9.1 Overview
• 9.2 Oil Analysis
• 9.3 Vibration Analysis
• 9.4 Acoustic Monitoring
• 9.5 Motor Circuit Analysis
• 9.6 Others

10 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY ORGANIZATION SIZE

• 10.1 Overview
• 10.1 Small & Medium Enterprises
• 10.1 Large Enterprises

11 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY GEOGRAPHY

• 11.1 Overview
• 11.2 North America
  • 11.2.1 U.S.
  • 11.2.2 Canada
  • 11.2.3 Mexico
• 11.3 Europe
  • 11.3.1 Germany
  • 11.3.2 U.K.
  • 11.3.3 France
  • 11.3.4 Rest of Europe
• 11.4 Asia Pacific
  • 11.4.1 China
  • 11.4.2 Japan
  • 11.4.3 India
  • 11.4.4 Rest of Asia Pacific
• 11.5 Rest of the World
  • 11.5.1 Latin America
  • 11.5.2 Middle East and Africa

12 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET COMPETITIVE LANDSCAPE

• 12.1 Overview
• 12.2 Company Market Ranking
• 12.3 Key Development Strategies

13 COMPANY PROFILES

• 13.1 IBM
  • 13.1.1 Overview
  • 13.1.2 Financial Performance
  • 13.1.3 Product Outlook
  • 13.1.4 Key Developments
• 13.2 SAS Institute
  • 13.2.1 Overview
  • 13.2.2 Financial Performance
  • 13.2.3 Product Outlook
  • 13.2.4 Key Developments
• 13.3 Robert Bosch GmbH
  • 13.3.1 Overview
  • 13.3.2 Financial Performance
  • 13.3.3 Product Outlook
  • 13.3.4 Key Developments
• 13.4 Software AG
  • 13.4.1 Overview
  • 13.4.2 Financial Performance
  • 13.4.3 Product Outlook
  • 13.4.4 Key Developments
• 13.5 Rockwell Automation
  • 13.5.1 Overview
  • 13.5.2 Financial Performance
  • 13.5.3 Product Outlook
  • 13.5.4 Key Developments
• 13.6 eMaint Enterprises
  • 13.6.1 Overview
  • 13.6.2 Financial Performance
  • 13.6.3 Product Outlook
  • 13.6.4 Key Developments
• 13.7 Schneider Electric
  • 13.7.1 Overview
  • 13.7.2 Financial Performance
  • 13.7.3 Product Outlook
  • 13.7.4 Key Development
• 13.8 General Electric
  • 13.8.1 Overview
  • 13.8.2 Financial Performance
  • 13.8.3 Product Outlook
  • 13.8.4 Key Developments
• 13.9 Siemens
  • 13.9.1 Overview
  • 13.9.2 Financial Performance
  • 13.9.3 Product Outlook
  • 13.9.4 Key Developments
• 13.10 PTC
  • 13.10.1 Overview
  • 13.10.2 Financial Performance
  • 13.10.3 Product Outlook
  • 13.10.4 Key Development

14 Appendix

• 14.1 Related Research

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