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시장보고서
상품코드
1871976

제조업용 예지보전 : 세계 시장 점유율과 순위, 총판매량 및 수요 예측(2025-2031년)

Predictive Maintenance In Manufacturing - Global Market Share and Ranking, Overall Sales and Demand Forecast 2025-2031
발행일: | 리서치사: QYResearch | 페이지 정보: 영문 | 배송안내 : 2-3일 (영업일 기준)

    
    
    




■ 보고서에 따라 최신 정보로 업데이트하여 보내드립니다. 배송일정은 문의해 주시기 바랍니다.

세계의 제조업용 예지보전 시장 규모는 2024년에 80억 2,000만 달러로 추정되며, 2025-2031년의 예측 기간에 CAGR 18.6%로 성장하며, 2031년까지 265억 9,700만 달러로 확대할 것으로 예측되고 있습니다.

제조업용 예지보전은 센서, IoT, 인공지능 등의 기술을 활용하여 설비의 실시간 상태를 모니터링하고 잠재적인 고장이 발생하기 전에 예측하는 지능형 유지보수 전략입니다. 운영 데이터를 분석하여 최적의 타이밍에 유지보수를 실시할 수 있으며, 계획되지 않은 다운타임을 줄이고, 수리 비용을 최소화하며, 생산 효율을 향상시키고, 설비 수명을 연장할 수 있습니다. 이는 스마트 제조 및 인더스트리 4.0 구상의 중요한 구성 요소입니다.

제조업계의 예측보전 분야의 세계 주요 기업으로는 SAP, 슈나이더 일렉트릭, 지멘스 등이 있습니다. 상위 3개사의 점유율은 약 19%를 차지하고 있습니다. 북미가 약 35% 점유율로 가장 큰 시장이며, 유럽, 아시아태평양이 그 뒤를 잇고 있습니다. 제품별로는 클라우드 기반 제품이 약 77%의 점유율로 가장 큰 비중을 차지하고 있습니다. 또한 용도별로는 산업/제조업용이 약 47%의 점유율로 가장 큰 응용 분야입니다.

시장 성장 촉진요인

IoT, AI, ML의 보급 확대: 제조업체들은 진동, 온도, 압력 등의 설비 파라미터를 지속적으로 모니터링하기 위해 IoT 센서와 AI/ML 분석 도입을 가속화하고 있습니다. 이를 통해 고장의 정확한 예측이 가능해져 적시에 유지보수 개입을 촉진하고, 사후 대응형에서 예방형으로 유지보수 모델을 전환하고 있습니다.

비용 절감 및 운영 효율성 향상: 예지보전은 계획되지 않은 다운타임과 불필요한 유지보수를 크게 줄여 10-40%의 비용 절감을 실현합니다. 또한 자산의 수명을 연장하고, 종합 설비 효율(OEE)을 향상시켜 생산 효율을 높입니다.

인더스트리 4.0과의 통합: 스마트 제조로의 진화는 예측 솔루션에 대한 수요를 촉진합니다. 예지보전은 디지털 팩토리에 필수적인 요소로, ERP, CMMS 등 기업 시스템과 연동하여 워크플로우를 효율화합니다.

클라우드 & 엣지 컴퓨팅을 통한 확장성: 클라우드 기반 플랫폼은 대규모 IT 인프라 없이도 확장 가능한 중앙집중형 분석이 가능합니다. 엣지 컴퓨팅은 장비 수준에서 실시간 의사결정을 지원하고, 지연과 대역폭의 필요성을 줄여주는 역할을 합니다.

규제 준수 및 자산 신뢰성: 자동차, 에너지, 항공우주와 같은 규제 산업에서 예지보전은 설비의 건전성을 사전에 관리하고 고장 위험을 줄여 안전 및 규제 준수 요건을 지원합니다.

시장이 해결해야 할 과제

높은 초기 투자비용과 ROI의 불확실성: 예지보전을 도입하기 위해서는 센서, 분석 플랫폼, 데이터 통합, 교육에 대한 투자가 필요합니다. 특히 중소기업의 경우, ROI가 지연되거나 간접적이기 때문에 이러한 투자를 정당화하기 어려울 수 있습니다.

데이터 통합 및 품질 문제: 제조업체는 레거시 시스템이나 이기종 디바이스의 불일치하고 노이즈가 많은 데이터로 인해 어려움을 겪는 경우가 많습니다. 신뢰할 수 있는 예측을 위한 정확하고 일관된 데이터를 확보하는 것은 큰 장벽이 될 수 있습니다.

사이버 보안 취약성: 예측 시스템이 네트워크화된 센서와 클라우드 인프라에 의존하는 정도가 높아짐에 따라 운영은 사이버 위험에 노출됩니다. 데이터의 무결성과 프라이버시를 보호하는 것은 필수적이지만, 이를 위해서는 비용이 발생합니다.

숙련된 인력 부족: 효과적인 예지보전(PdM)을 도입하기 위해서는 데이터 사이언스, 머신러닝, 산업 시스템에 대한 전문 지식이 필요합니다. 이러한 기술은 부족하기 쉬우며, 새로운 전문가를 육성하고 채용하는 것은 복잡성과 비용을 증가시킵니다.

확장성 및 상호운용성 장벽: 파일럿 시스템을 다양한 기계 및 거점으로 확장할 때, 벤더별 포맷, 표준 프로토콜 부족, 장비 유형 간 일관성 유지 등의 문제가 종종 발생합니다.

변화에 대한 문화적 저항: 신뢰성의 문제, 일자리 손실에 대한 우려 또는 기존 방식에 대한 집착으로 인해 머신러닝 기반 유지보수 툴 도입에 신중한 태도를 보이는 제조업체가 여전히 존재합니다.

이 보고서는 제조 분야 예지보전 세계 시장에 대해 총매출액, 주요 기업의 시장 점유율 및 순위에 초점을 맞추고 지역/국가, 유형 및 용도별 분석을 종합적으로 제시하는 것을 목적으로 합니다.

제조 분야의 예지보전 시장 규모 추정 및 예측은 2024년을 기준 연도로 하여 2020-2031년 기간의 과거 데이터와 예측 데이터를 포함하는 매출액으로 제시되었습니다. 정량적, 정성적 분석을 통해 독자들이 비즈니스/성장 전략 수립, 시장 경쟁 평가, 현재 시장에서의 포지셔닝 분석, 그리고 제조업의 예지보전 관련 정보에 입각한 비즈니스 의사결정을 내릴 수 있도록 도와드립니다.

시장 세분화

기업별

  • IBM
  • Microsoft
  • SAP
  • GE Digital
  • Schneider
  • Hitachi
  • Siemens
  • Intel
  • RapidMiner
  • Rockwell Automation
  • Software AG
  • Cisco
  • Oracle
  • Fujitsu
  • Dassault Systemes
  • Augury Systems
  • TIBCO Software
  • Uptake
  • Honeywell
  • PTC
  • Huawei
  • ABB
  • AVEVA
  • SAS
  • SKF
  • Emerson
  • Mpulse
  • Maintenance Connection
  • Dingo
  • Particle
  • Bosch
  • C3.ai
  • Dell
  • Sigma Industrial Precision

유형별 부문

  • 클라우드 기반
  • 온프레미스

용도별 부문

  • 자동차
  • 전자기기 및 반도체
  • 소비재
  • 화학
  • 의약품
  • 기타

지역별

  • 북미
    • 미국
    • 캐나다
  • 아시아태평양
    • 중국
    • 일본
    • 한국
    • 동남아시아
    • 인도
    • 호주
    • 기타 아시아태평양
  • 유럽
    • 독일
    • 프랑스
    • 영국
    • 이탈리아
    • 네덜란드
    • 북유럽 국가
    • 기타 유럽
  • 라틴아메리카
    • 멕시코
    • 브라질
    • 기타 라틴아메리카
  • 중동 및 아프리카
    • 튀르키예
    • 사우디아라비아
    • 아랍에미리트
    • 기타 중동 및 아프리카
KSA 25.12.03

자주 묻는 질문

  • 제조업용 예지보전 시장 규모는 어떻게 예측되나요?
  • 제조업계의 예측보전 분야에서 주요 기업은 어디인가요?
  • 예지보전의 주요 성장 촉진 요인은 무엇인가요?
  • 예지보전 시장이 직면한 주요 과제는 무엇인가요?
  • 제조업용 예지보전의 주요 응용 분야는 무엇인가요?

The global market for Predictive Maintenance In Manufacturing was estimated to be worth US$ 8020 million in 2024 and is forecast to a readjusted size of US$ 26597 million by 2031 with a CAGR of 18.6% during the forecast period 2025-2031.

Predictive Maintenance in Manufacturing is an intelligent maintenance strategy that uses technologies such as sensors, IoT, and artificial intelligence to monitor the real-time condition of equipment and predict potential failures before they occur. Analyzing operational data it enables maintenance to be performed at the optimal time, reducing unplanned downtime, minimizing repair costs, improving production efficiency, and extending equipment lifespan. It is a key component of smart manufacturing and Industry 4.0 initiatives.

Global key predictive maintenance in manufacturing players include SAP, Schneider and Siemens etc. The top 3 companies hold a share about 19%. North America is the largest market with a share about 35%, followed by Europe and Asia-Pacific. In terms of product, cloud based product is the largest segment with a share about 77%. And in terms of applications, the largest application is industrial and manufacturing with a share about 47%.

Market Drivers

Widespread Adoption of IoT, AI, and ML: Manufacturers are increasingly deploying IoT sensors and AI/ML analytics to continuously monitor equipment parameters like vibration, temperature, and pressure. This enables accurate predictions of failures and facilitates timely maintenance interventions, shifting the maintenance model from reactive to proactive.

Cost Reduction & Operational Efficiency: Predictive Maintenance significantly reduces unplanned downtime and unnecessary maintenance, resulting in cost savings of 10-40%. It also extends asset lifespan, boosts overall equipment effectiveness (OEE), and enhances production efficiency.

Industry 4.0 Integration: The evolution toward smart manufacturing fosters demand for predictive solutions. Predictive Maintenance is becoming integral to digital factories, integrated with ERP, CMMS, and other enterprise systems to streamline workflows.

Cloud & Edge Computing Enable Scalability: Cloud-based platforms facilitate scalable, centralized analytics without heavy IT infrastructure. Edge computing further supports real-time decision-making at the equipment level, reducing latency and bandwidth needs.

Regulatory Compliance & Asset Reliability: In regulated industries like automotive, energy, and aerospace, predictive maintenance supports safety and compliance requirements by proactively managing equipment health and reducing failure risk.

Market Challenges

High Upfront Investment & ROI Uncertainty: Implementing PdM requires investment in sensors, analytic platforms, data integration, and training. Especially for SMEs, justifying these investments can be difficult due to delayed or indirect ROI.

Data Integration & Quality Issues: Manufacturers often struggle with disparate, noisy data from legacy systems and heterogeneous devices. Ensuring accurate, consistent data for reliable predictions is a significant hurdle.

Cybersecurity Vulnerabilities: As predictive systems increasingly rely on networked sensors and cloud infrastructure, they expose operations to cyber risks. Protecting data integrity and privacy is essential-and costly.

Skilled Workforce Shortage: Effective PdM deployment demands expertise in data science, ML, and industrial systems-skills that are often lacking, and training or hiring new specialists adds complexity and cost.

Scalability & Interoperability Barriers: Scaling pilot systems across diverse machines and sites often encounters issues like vendor-specific formats, lack of standard protocols, and maintenance of consistency across equipment types.

Cultural Resistance to Change: Some manufacturers remain cautious about adopting ML-based maintenance tools due to trust issues, fear of job displacement, or preference for traditional methods.

This report aims to provide a comprehensive presentation of the global market for Predictive Maintenance In Manufacturing, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Predictive Maintenance In Manufacturing by region & country, by Type, and by Application.

The Predictive Maintenance In Manufacturing market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2024 as the base year, with history and forecast data for the period from 2020 to 2031. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Predictive Maintenance In Manufacturing.

Market Segmentation

By Company

  • IBM
  • Microsoft
  • SAP
  • GE Digital
  • Schneider
  • Hitachi
  • Siemens
  • Intel
  • RapidMiner
  • Rockwell Automation
  • Software AG
  • Cisco
  • Oracle
  • Fujitsu
  • Dassault Systemes
  • Augury Systems
  • TIBCO Software
  • Uptake
  • Honeywell
  • PTC
  • Huawei
  • ABB
  • AVEVA
  • SAS
  • SKF
  • Emerson
  • Mpulse
  • Maintenance Connection
  • Dingo
  • Particle
  • Bosch
  • C3.ai
  • Dell
  • Sigma Industrial Precision

Segment by Type

  • Cloud Based
  • On-premises

Segment by Application

  • Automotive
  • Electronics and Semiconductor
  • Consumer Goods
  • Chemical
  • Pharmaceutical
  • Others

By Region

  • North America
    • United States
    • Canada
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • Southeast Asia
    • India
    • Australia
    • Rest of Asia-Pacific
  • Europe
    • Germany
    • France
    • U.K.
    • Italy
    • Netherlands
    • Nordic Countries
    • Rest of Europe
  • Latin America
    • Mexico
    • Brazil
    • Rest of Latin America
  • Middle East & Africa
    • Turkey
    • Saudi Arabia
    • UAE
    • Rest of MEA

Chapter Outline

Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.

Chapter 2: Detailed analysis of Predictive Maintenance In Manufacturing company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.

Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.

Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.

Chapter 5: Revenue of Predictive Maintenance In Manufacturing in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.

Chapter 6: Revenue of Predictive Maintenance In Manufacturing in country level. It provides sigmate data by Type, and by Application for each country/region.

Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.

Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.

Chapter 9: Conclusion.

Table of Contents

1 Market Overview

  • 1.1 Predictive Maintenance In Manufacturing Product Introduction
  • 1.2 Global Predictive Maintenance In Manufacturing Market Size Forecast (2020-2031)
  • 1.3 Predictive Maintenance In Manufacturing Market Trends & Drivers
    • 1.3.1 Predictive Maintenance In Manufacturing Industry Trends
    • 1.3.2 Predictive Maintenance In Manufacturing Market Drivers & Opportunity
    • 1.3.3 Predictive Maintenance In Manufacturing Market Challenges
    • 1.3.4 Predictive Maintenance In Manufacturing Market Restraints
  • 1.4 Assumptions and Limitations
  • 1.5 Study Objectives
  • 1.6 Years Considered

2 Competitive Analysis by Company

  • 2.1 Global Predictive Maintenance In Manufacturing Players Revenue Ranking (2024)
  • 2.2 Global Predictive Maintenance In Manufacturing Revenue by Company (2020-2025)
  • 2.3 Key Companies Predictive Maintenance In Manufacturing Manufacturing Base Distribution and Headquarters
  • 2.4 Key Companies Predictive Maintenance In Manufacturing Product Offered
  • 2.5 Key Companies Time to Begin Mass Production of Predictive Maintenance In Manufacturing
  • 2.6 Predictive Maintenance In Manufacturing Market Competitive Analysis
    • 2.6.1 Predictive Maintenance In Manufacturing Market Concentration Rate (2020-2025)
    • 2.6.2 Global 5 and 10 Largest Companies by Predictive Maintenance In Manufacturing Revenue in 2024
    • 2.6.3 Global Top Companies by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in Predictive Maintenance In Manufacturing as of 2024)
  • 2.7 Mergers & Acquisitions, Expansion

3 Segmentation by Type

  • 3.1 Introduction by Type
    • 3.1.1 Cloud Based
    • 3.1.2 On-premises
  • 3.2 Global Predictive Maintenance In Manufacturing Sales Value by Type
    • 3.2.1 Global Predictive Maintenance In Manufacturing Sales Value by Type (2020 VS 2024 VS 2031)
    • 3.2.2 Global Predictive Maintenance In Manufacturing Sales Value, by Type (2020-2031)
    • 3.2.3 Global Predictive Maintenance In Manufacturing Sales Value, by Type (%) (2020-2031)

4 Segmentation by Application

  • 4.1 Introduction by Application
    • 4.1.1 Automotive
    • 4.1.2 Electronics and Semiconductor
    • 4.1.3 Consumer Goods
    • 4.1.4 Chemical
    • 4.1.5 Pharmaceutical
    • 4.1.6 Others
  • 4.2 Global Predictive Maintenance In Manufacturing Sales Value by Application
    • 4.2.1 Global Predictive Maintenance In Manufacturing Sales Value by Application (2020 VS 2024 VS 2031)
    • 4.2.2 Global Predictive Maintenance In Manufacturing Sales Value, by Application (2020-2031)
    • 4.2.3 Global Predictive Maintenance In Manufacturing Sales Value, by Application (%) (2020-2031)

5 Segmentation by Region

  • 5.1 Global Predictive Maintenance In Manufacturing Sales Value by Region
    • 5.1.1 Global Predictive Maintenance In Manufacturing Sales Value by Region: 2020 VS 2024 VS 2031
    • 5.1.2 Global Predictive Maintenance In Manufacturing Sales Value by Region (2020-2025)
    • 5.1.3 Global Predictive Maintenance In Manufacturing Sales Value by Region (2026-2031)
    • 5.1.4 Global Predictive Maintenance In Manufacturing Sales Value by Region (%), (2020-2031)
  • 5.2 North America
    • 5.2.1 North America Predictive Maintenance In Manufacturing Sales Value, 2020-2031
    • 5.2.2 North America Predictive Maintenance In Manufacturing Sales Value by Country (%), 2024 VS 2031
  • 5.3 Europe
    • 5.3.1 Europe Predictive Maintenance In Manufacturing Sales Value, 2020-2031
    • 5.3.2 Europe Predictive Maintenance In Manufacturing Sales Value by Country (%), 2024 VS 2031
  • 5.4 Asia Pacific
    • 5.4.1 Asia Pacific Predictive Maintenance In Manufacturing Sales Value, 2020-2031
    • 5.4.2 Asia Pacific Predictive Maintenance In Manufacturing Sales Value by Region (%), 2024 VS 2031
  • 5.5 South America
    • 5.5.1 South America Predictive Maintenance In Manufacturing Sales Value, 2020-2031
    • 5.5.2 South America Predictive Maintenance In Manufacturing Sales Value by Country (%), 2024 VS 2031
  • 5.6 Middle East & Africa
    • 5.6.1 Middle East & Africa Predictive Maintenance In Manufacturing Sales Value, 2020-2031
    • 5.6.2 Middle East & Africa Predictive Maintenance In Manufacturing Sales Value by Country (%), 2024 VS 2031

6 Segmentation by Key Countries/Regions

  • 6.1 Key Countries/Regions Predictive Maintenance In Manufacturing Sales Value Growth Trends, 2020 VS 2024 VS 2031
  • 6.2 Key Countries/Regions Predictive Maintenance In Manufacturing Sales Value, 2020-2031
  • 6.3 United States
    • 6.3.1 United States Predictive Maintenance In Manufacturing Sales Value, 2020-2031
    • 6.3.2 United States Predictive Maintenance In Manufacturing Sales Value by Type (%), 2024 VS 2031
    • 6.3.3 United States Predictive Maintenance In Manufacturing Sales Value by Application, 2024 VS 2031
  • 6.4 Europe
    • 6.4.1 Europe Predictive Maintenance In Manufacturing Sales Value, 2020-2031
    • 6.4.2 Europe Predictive Maintenance In Manufacturing Sales Value by Type (%), 2024 VS 2031
    • 6.4.3 Europe Predictive Maintenance In Manufacturing Sales Value by Application, 2024 VS 2031
  • 6.5 China
    • 6.5.1 China Predictive Maintenance In Manufacturing Sales Value, 2020-2031
    • 6.5.2 China Predictive Maintenance In Manufacturing Sales Value by Type (%), 2024 VS 2031
    • 6.5.3 China Predictive Maintenance In Manufacturing Sales Value by Application, 2024 VS 2031
  • 6.6 Japan
    • 6.6.1 Japan Predictive Maintenance In Manufacturing Sales Value, 2020-2031
    • 6.6.2 Japan Predictive Maintenance In Manufacturing Sales Value by Type (%), 2024 VS 2031
    • 6.6.3 Japan Predictive Maintenance In Manufacturing Sales Value by Application, 2024 VS 2031
  • 6.7 South Korea
    • 6.7.1 South Korea Predictive Maintenance In Manufacturing Sales Value, 2020-2031
    • 6.7.2 South Korea Predictive Maintenance In Manufacturing Sales Value by Type (%), 2024 VS 2031
    • 6.7.3 South Korea Predictive Maintenance In Manufacturing Sales Value by Application, 2024 VS 2031
  • 6.8 Southeast Asia
    • 6.8.1 Southeast Asia Predictive Maintenance In Manufacturing Sales Value, 2020-2031
    • 6.8.2 Southeast Asia Predictive Maintenance In Manufacturing Sales Value by Type (%), 2024 VS 2031
    • 6.8.3 Southeast Asia Predictive Maintenance In Manufacturing Sales Value by Application, 2024 VS 2031
  • 6.9 India
    • 6.9.1 India Predictive Maintenance In Manufacturing Sales Value, 2020-2031
    • 6.9.2 India Predictive Maintenance In Manufacturing Sales Value by Type (%), 2024 VS 2031
    • 6.9.3 India Predictive Maintenance In Manufacturing Sales Value by Application, 2024 VS 2031

7 Company Profiles

  • 7.1 IBM
    • 7.1.1 IBM Profile
    • 7.1.2 IBM Main Business
    • 7.1.3 IBM Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.1.4 IBM Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.1.5 IBM Recent Developments
  • 7.2 Microsoft
    • 7.2.1 Microsoft Profile
    • 7.2.2 Microsoft Main Business
    • 7.2.3 Microsoft Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.2.4 Microsoft Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.2.5 Microsoft Recent Developments
  • 7.3 SAP
    • 7.3.1 SAP Profile
    • 7.3.2 SAP Main Business
    • 7.3.3 SAP Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.3.4 SAP Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.3.5 SAP Recent Developments
  • 7.4 GE Digital
    • 7.4.1 GE Digital Profile
    • 7.4.2 GE Digital Main Business
    • 7.4.3 GE Digital Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.4.4 GE Digital Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.4.5 GE Digital Recent Developments
  • 7.5 Schneider
    • 7.5.1 Schneider Profile
    • 7.5.2 Schneider Main Business
    • 7.5.3 Schneider Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.5.4 Schneider Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.5.5 Schneider Recent Developments
  • 7.6 Hitachi
    • 7.6.1 Hitachi Profile
    • 7.6.2 Hitachi Main Business
    • 7.6.3 Hitachi Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.6.4 Hitachi Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.6.5 Hitachi Recent Developments
  • 7.7 Siemens
    • 7.7.1 Siemens Profile
    • 7.7.2 Siemens Main Business
    • 7.7.3 Siemens Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.7.4 Siemens Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.7.5 Siemens Recent Developments
  • 7.8 Intel
    • 7.8.1 Intel Profile
    • 7.8.2 Intel Main Business
    • 7.8.3 Intel Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.8.4 Intel Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.8.5 Intel Recent Developments
  • 7.9 RapidMiner
    • 7.9.1 RapidMiner Profile
    • 7.9.2 RapidMiner Main Business
    • 7.9.3 RapidMiner Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.9.4 RapidMiner Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.9.5 RapidMiner Recent Developments
  • 7.10 Rockwell Automation
    • 7.10.1 Rockwell Automation Profile
    • 7.10.2 Rockwell Automation Main Business
    • 7.10.3 Rockwell Automation Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.10.4 Rockwell Automation Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.10.5 Rockwell Automation Recent Developments
  • 7.11 Software AG
    • 7.11.1 Software AG Profile
    • 7.11.2 Software AG Main Business
    • 7.11.3 Software AG Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.11.4 Software AG Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.11.5 Software AG Recent Developments
  • 7.12 Cisco
    • 7.12.1 Cisco Profile
    • 7.12.2 Cisco Main Business
    • 7.12.3 Cisco Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.12.4 Cisco Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.12.5 Cisco Recent Developments
  • 7.13 Oracle
    • 7.13.1 Oracle Profile
    • 7.13.2 Oracle Main Business
    • 7.13.3 Oracle Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.13.4 Oracle Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.13.5 Oracle Recent Developments
  • 7.14 Fujitsu
    • 7.14.1 Fujitsu Profile
    • 7.14.2 Fujitsu Main Business
    • 7.14.3 Fujitsu Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.14.4 Fujitsu Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.14.5 Fujitsu Recent Developments
  • 7.15 Dassault Systemes
    • 7.15.1 Dassault Systemes Profile
    • 7.15.2 Dassault Systemes Main Business
    • 7.15.3 Dassault Systemes Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.15.4 Dassault Systemes Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.15.5 Dassault Systemes Recent Developments
  • 7.16 Augury Systems
    • 7.16.1 Augury Systems Profile
    • 7.16.2 Augury Systems Main Business
    • 7.16.3 Augury Systems Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.16.4 Augury Systems Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.16.5 Augury Systems Recent Developments
  • 7.17 TIBCO Software
    • 7.17.1 TIBCO Software Profile
    • 7.17.2 TIBCO Software Main Business
    • 7.17.3 TIBCO Software Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.17.4 TIBCO Software Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.17.5 TIBCO Software Recent Developments
  • 7.18 Uptake
    • 7.18.1 Uptake Profile
    • 7.18.2 Uptake Main Business
    • 7.18.3 Uptake Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.18.4 Uptake Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.18.5 Uptake Recent Developments
  • 7.19 Honeywell
    • 7.19.1 Honeywell Profile
    • 7.19.2 Honeywell Main Business
    • 7.19.3 Honeywell Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.19.4 Honeywell Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.19.5 Honeywell Recent Developments
  • 7.20 PTC
    • 7.20.1 PTC Profile
    • 7.20.2 PTC Main Business
    • 7.20.3 PTC Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.20.4 PTC Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.20.5 PTC Recent Developments
  • 7.21 Huawei
    • 7.21.1 Huawei Profile
    • 7.21.2 Huawei Main Business
    • 7.21.3 Huawei Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.21.4 Huawei Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.21.5 Huawei Recent Developments
  • 7.22 ABB
    • 7.22.1 ABB Profile
    • 7.22.2 ABB Main Business
    • 7.22.3 ABB Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.22.4 ABB Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.22.5 ABB Recent Developments
  • 7.23 AVEVA
    • 7.23.1 AVEVA Profile
    • 7.23.2 AVEVA Main Business
    • 7.23.3 AVEVA Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.23.4 AVEVA Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.23.5 AVEVA Recent Developments
  • 7.24 SAS
    • 7.24.1 SAS Profile
    • 7.24.2 SAS Main Business
    • 7.24.3 SAS Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.24.4 SAS Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.24.5 SAS Recent Developments
  • 7.25 SKF
    • 7.25.1 SKF Profile
    • 7.25.2 SKF Main Business
    • 7.25.3 SKF Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.25.4 SKF Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.25.5 SKF Recent Developments
  • 7.26 Emerson
    • 7.26.1 Emerson Profile
    • 7.26.2 Emerson Main Business
    • 7.26.3 Emerson Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.26.4 Emerson Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.26.5 Emerson Recent Developments
  • 7.27 Mpulse
    • 7.27.1 Mpulse Profile
    • 7.27.2 Mpulse Main Business
    • 7.27.3 Mpulse Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.27.4 Mpulse Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.27.5 Mpulse Recent Developments
  • 7.28 Maintenance Connection
    • 7.28.1 Maintenance Connection Profile
    • 7.28.2 Maintenance Connection Main Business
    • 7.28.3 Maintenance Connection Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.28.4 Maintenance Connection Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.28.5 Maintenance Connection Recent Developments
  • 7.29 Dingo
    • 7.29.1 Dingo Profile
    • 7.29.2 Dingo Main Business
    • 7.29.3 Dingo Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.29.4 Dingo Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.29.5 Dingo Recent Developments
  • 7.30 Particle
    • 7.30.1 Particle Profile
    • 7.30.2 Particle Main Business
    • 7.30.3 Particle Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.30.4 Particle Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.30.5 Particle Recent Developments
  • 7.31 Bosch
    • 7.31.1 Bosch Profile
    • 7.31.2 Bosch Main Business
    • 7.31.3 Bosch Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.31.4 Bosch Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.31.5 Bosch Recent Developments
  • 7.32 C3.ai
    • 7.32.1 C3.ai Profile
    • 7.32.2 C3.ai Main Business
    • 7.32.3 C3.ai Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.32.4 C3.ai Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.32.5 C3.ai Recent Developments
  • 7.33 Dell
    • 7.33.1 Dell Profile
    • 7.33.2 Dell Main Business
    • 7.33.3 Dell Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.33.4 Dell Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.33.5 Dell Recent Developments
  • 7.34 Sigma Industrial Precision
    • 7.34.1 Sigma Industrial Precision Profile
    • 7.34.2 Sigma Industrial Precision Main Business
    • 7.34.3 Sigma Industrial Precision Predictive Maintenance In Manufacturing Products, Services and Solutions
    • 7.34.4 Sigma Industrial Precision Predictive Maintenance In Manufacturing Revenue (US$ Million) & (2020-2025)
    • 7.34.5 Sigma Industrial Precision Recent Developments

8 Industry Chain Analysis

  • 8.1 Predictive Maintenance In Manufacturing Industrial Chain
  • 8.2 Predictive Maintenance In Manufacturing Upstream Analysis
    • 8.2.1 Key Raw Materials
    • 8.2.2 Raw Materials Key Suppliers
    • 8.2.3 Manufacturing Cost Structure
  • 8.3 Midstream Analysis
  • 8.4 Downstream Analysis (Customers Analysis)
  • 8.5 Sales Model and Sales Channels
    • 8.5.1 Predictive Maintenance In Manufacturing Sales Model
    • 8.5.2 Sales Channel
    • 8.5.3 Predictive Maintenance In Manufacturing Distributors

9 Research Findings and Conclusion

10 Appendix

  • 10.1 Research Methodology
    • 10.1.1 Methodology/Research Approach
      • 10.1.1.1 Research Programs/Design
      • 10.1.1.2 Market Size Estimation
      • 10.1.1.3 Market Breakdown and Data Triangulation
    • 10.1.2 Data Source
      • 10.1.2.1 Secondary Sources
      • 10.1.2.2 Primary Sources
  • 10.2 Author Details
  • 10.3 Disclaimer
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