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PREDICTIVE MAINTENANCE Market Size, Share, Growth and Global Industry Analysis By Type & Application, Regional Insights and Forecast to 2024-2032

Publisher Fortune Business Insights Pvt. Ltd.
Published Jan 01, 2025
Length 160 Pages
SKU # FOB19847125

Description

Growth Factors of PREDICTIVE MAINTENANCE Market

Predictive maintenance is revolutionizing industries by leveraging AI, IoT, and big data tanticipate equipment failures before they occur. This approach reduces downtime, improves efficiency, and extends asset lifespan. As businesses prioritize digital transformation, the predictive maintenance market is experiencing exponential growth.

Market Overview

The global predictive maintenance market was valued at $10.93 billion in 2024 and is projected treach $70.73 billion by 2032, growing at an impressive 26.5% CAGR. Industries such as manufacturing, energy, healthcare, and transportation are adopting predictive maintenance solutions treduce operational costs and enhance productivity.

Key Growth Drivers

1. Industry 4.0 and Digitalization

Smart factories and digital twins are driving the adoption of predictive maintenance. Companies are integrating IoT sensors, AI, and cloud computing tmonitor equipment health in real time.

2. Cost Savings and Efficiency

Traditional reactive maintenance leads tunexpected breakdowns and higher costs. Predictive maintenance enables proactive servicing, minimizing unplanned downtime and reducing expenses.

3. AI and Machine Learning Advancements

AI-driven analytics can process vast amounts of sensor data, identifying patterns that indicate potential failures. This improves accuracy and reliability in maintenance predictions.

4. IoT and Connectivity Growth

The rise of connected devices allows companies tcollect real-time equipment data, facilitating remote monitoring and predictive analytics.

5. Post-COVID Digital Acceleration

The pandemic pushed industries tinvest in digital solutions. Many companies shifted toward automated, remote maintenance tensure business continuity.

Challenges in Adoption

Despite rapid growth, challenges remain:
  • Skilled Workforce Shortage: Implementing predictive maintenance requires expertise in AI, IoT, and data analytics.
  • High Initial Investment: Small and medium enterprises (SMEs) may struggle with the cost of technology implementation.
  • Data Security Concerns: Increased connectivity raises cybersecurity risks, requiring robust protection measures.
Regional Insights
  • North America leads the market due tearly AI and IoT adoption.
  • Europe is investing in smart manufacturing, boosting demand.
  • Asia-Pacific is experiencing rapid industrialization, making predictive maintenance crucial for manufacturing and logistics.
Key Players and Market Strategies

Major companies like IBM, General Electric, Siemens, and SAP are driving innovation through partnerships and AI-powered solutions. They are investing in cloud-based predictive maintenance platforms tcater ta wider range of industries.

Future Outlook

The predictive maintenance market is set treshape industries by increasing automation, reducing operational risks, and improving cost efficiency. With continuous advancements in AI, IoT, and 5G, predictive maintenance will become a standard practice for businesses aiming tachieve maximum uptime and efficiency.

ATTRIBUTE DETAILS

Study Period 2019-2032

Base Year 2024

Estimated Year 2025

Forecast Period 2025-2032

Historical Period 2019-2023

Growth Rate CAGR of 26.5% from 2025 t2032

Unit Value (USD Billion)

Segmentation By Component
  • Hardware
  • Software
Integrated

Standalone

By Deployment
  • On-premise
  • Cloud-based
By Enterprise Type
  • Large Enterprises
  • Small and Mid-sized Enterprises (SMEs)
By Technology
  • IoT
  • Artificial Intelligence and Machine Learning
  • Digital Twin
  • Advance Analytics
  • Others (Modern Database, ERP, etc.)
By Application
  • Condition Monitoring
  • Predictive Analytics
  • Remote Monitoring
  • Asset Tracking
  • Maintenance Scheduling
By End-use
  • Military and Defense
  • Energy and Utilities
  • Manufacturing
  • Healthcare
  • IT and Telecom
  • Logistics and Transportation
  • Others (Chemicals, Paper and Printing and Agriculture, etc.)
By Region
  • North America (By Component, By Deployment, By Enterprise Type, By Technology, By Application, By End-Use, and By Country)
U.S.

Canada

Mexico
  • South America (By Component, By Deployment, By Enterprise Type, By Technology, By Application, By End-Use, and By Country)
Brazil

Argentina

Rest of South America
  • Europe (By Component, By Deployment, By Enterprise Type, By Technology, By Application, By End-Use, and By Country)
U.K.

Germany

France

Italy

Spain

Russia

Benelux

Nordics

Rest of Europe
  • Middle East & Africa (By Component, By Deployment, By Enterprise Type, By Technology, By Application, By End-Use, and By Country)
Turkey

Israel

GCC

North Africa

South Africa

Rest of Middle East & Africa
  • Asia Pacific (By Component, By Deployment, By Enterprise Type, By Technology, By Application, By End-Use, and By Country)
China

India

Japan

South Korea

ASEAN

Oceania

Rest of Asia Pacific

Companies Profiled in the Report IBM Corporation (U.S.), General Electric (U.S.), Siemens (Germany), C3.ai, Inc. (U.S.), PTC (U.S.), Rockwell Automation (U.S.), Hitachi Ltd. (Japan), UpKeep (U.S.), Augury Ltd. (U.S.), The Soothsayer (P-Dictor) (Thailand), etc.

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Table of Contents

160 Pages
1. Introduction
1.1. Definition, By Segment
1.2. Research Methodology/Approach
1.3. Data Sources
2. Executive Summary
3. Market Dynamics
3.1. Macro and Micro Economic Indicators
3.2. Drivers, Restraints, Opportunities and Trends
3.3. Impact of Generative AI
4. Competition Landscape
4.1. Business Strategies Adopted by Key Players
4.2. Consolidated SWOT Analysis of Key Players
4.3. Global Predictive Maintenance Key Players Market Share/Ranking, 2024
5. Global Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2019-2032
5.1. Key Findings
5.2. By Component (USD)
5.2.1. Hardware
5.2.2. Software
5.2.2.1. Integrated
5.2.2.2. Standalone
5.3. By Deployment (USD)
5.3.1. On-premise
5.3.2. Cloud-based
5.4. By Enterprise Type (USD)
5.4.1. Large Enterprises
5.4.2. Small and Mid-sized Enterprises (SMEs)
5.5. By Technology (USD)
5.5.1. IoT
5.5.2. Artificial Intelligence and Machine Learning
5.5.3. Digital Twin
5.5.4. Advance Analytics
5.5.5. Others (Modern Database, ERP, etc.)
5.6. By Application (USD)
5.6.1. Condition Monitoring
5.6.2. Predictive Analytics
5.6.3. Remote Monitoring
5.6.4. Asset Tracking
5.6.5. Maintenance Scheduling
5.7. By End-Use (USD)
5.7.1. Military and Defense
5.7.2. Energy and Utilities
5.7.3. Manufacturing
5.7.4. Healthcare
5.7.5. IT and Telecom
5.7.6. Logistics and Transportation
5.7.7. Others (Chemicals, Paper and Printing, and Agriculture, etc.)
5.8. By Region (USD)
5.8.1. North America
5.8.2. South America
5.8.3. Europe
5.8.4. Middle East & Africa
5.8.5. Asia Pacific
6. North America Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2019-2032
6.1. Key Findings
6.2. By Component (USD)
6.2.1. Hardware
6.2.2. Software
6.2.2.1. Integrated
6.2.2.2. Standalone
6.3. By Deployment (USD)
6.3.1. On-premise
6.3.2. Cloud-based
6.4. By Enterprise Type (USD)
6.4.1. Large Enterprises
6.4.2. Small and Mid-sized Enterprises (SMEs)
6.5. By Technology (USD)
6.5.1. IoT
6.5.2. Artificial Intelligence and Machine Learning
6.5.3. Digital Twin
6.5.4. Advance Analytics
6.5.5. Others (Modern Database, ERP, etc.)
6.6. By Application (USD)
6.6.1. Condition Monitoring
6.6.2. Predictive Analytics
6.6.3. Remote Monitoring
6.6.4. Asset Tracking
6.6.5. Maintenance Scheduling
6.7. By End-Use (USD)
6.7.1. Military and Defense
6.7.2. Energy and Utilities
6.7.3. Manufacturing
6.7.4. Healthcare
6.7.5. IT and Telecom
6.7.6. Logistics and Transportation
6.7.7. Others (Chemicals, Paper and Printing, and Agriculture, etc.)
6.8. By Country (USD)
6.8.1. United States
6.8.2. Canada
6.8.3. Mexico
7. South America Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2019-2032
7.1. Key Findings
7.2. By Component (USD)
7.2.1. Hardware
7.2.2. Software
7.2.2.1. Integrated
7.2.2.2. Standalone
7.3. By Deployment (USD)
7.3.1. On-premise
7.3.2. Cloud-based
7.4. By Enterprise Type (USD)
7.4.1. Large Enterprises
7.4.2. Small and Mid-sized Enterprises (SMEs)
7.5. By Technology (USD)
7.5.1. IoT
7.5.2. Artificial Intelligence and Machine Learning
7.5.3. Digital Twin
7.5.4. Advance Analytics
7.5.5. Others (Modern Database, ERP, etc.)
7.6. By Application (USD)
7.6.1. Condition Monitoring
7.6.2. Predictive Analytics
7.6.3. Remote Monitoring
7.6.4. Asset Tracking
7.6.5. Maintenance Scheduling
7.7. By End-Use (USD)
7.7.1. Military and Defense
7.7.2. Energy and Utilities
7.7.3. Manufacturing
7.7.4. Healthcare
7.7.5. IT and Telecom
7.7.6. Logistics and Transportation
7.7.7. Others (Chemicals, Paper and Printing, and Agriculture, etc.)
7.8. By Country (USD)
7.8.1. Brazil
7.8.2. Argentina
7.8.3. Rest of South America
8. Europe Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2019-2032
8.1. Key Findings
8.2. By Component (USD)
8.2.1. Hardware
8.2.2. Software
8.2.2.1. Integrated
8.2.2.2. Standalone
8.3. By Deployment (USD)
8.3.1. On-premise
8.3.2. Cloud-based
8.4. By Enterprise Type (USD)
8.4.1. Large Enterprises
8.4.2. Small and Mid-sized Enterprises (SMEs)
8.5. By Technology (USD)
8.5.1. IoT
8.5.2. Artificial Intelligence and Machine Learning
8.5.3. Digital Twin
8.5.4. Advance Analytics
8.5.5. Others (Modern Database, ERP, etc.)
8.6. By Application (USD)
8.6.1. Condition Monitoring
8.6.2. Predictive Analytics
8.6.3. Remote Monitoring
8.6.4. Asset Tracking
8.6.5. Maintenance Scheduling
8.7. By End-Use (USD)
8.7.1. Military and Defense
8.7.2. Energy and Utilities
8.7.3. Manufacturing
8.7.4. Healthcare
8.7.5. IT and Telecom
8.7.6. Logistics and Transportation
8.7.7. Others (Chemicals, Paper and Printing, and Agriculture, etc.)
8.8. By Country (USD)
8.8.1. United Kingdom
8.8.2. Germany
8.8.3. France
8.8.4. Italy
8.8.5. Spain
8.8.6. Russia
8.8.7. Benelux
8.8.8. Nordics
8.8.9. Rest of Europe
9. Middle East & Africa Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2019-2032
9.1. Key Findings
9.2. By Component (USD)
9.2.1. Hardware
9.2.2. Software
9.2.2.1. Integrated
9.2.2.2. Standalone
9.3. By Deployment (USD)
9.3.1. On-premise
9.3.2. Cloud-based
9.4. By Enterprise Type (USD)
9.4.1. Large Enterprises
9.4.2. Small and Mid-sized Enterprises (SMEs)
9.5. By Technology (USD)
9.5.1. IoT
9.5.2. Artificial Intelligence and Machine Learning
9.5.3. Digital Twin
9.5.4. Advance Analytics
9.5.5. Others (Modern Database, ERP, etc.)
9.6. By Application (USD)
9.6.1. Condition Monitoring
9.6.2. Predictive Analytics
9.6.3. Remote Monitoring
9.6.4. Asset Tracking
9.6.5. Maintenance Scheduling
9.7. By End-Use (USD)
9.7.1. Military and Defense
9.7.2. Energy and Utilities
9.7.3. Manufacturing
9.7.4. Healthcare
9.7.5. IT and Telecom
9.7.6. Logistics and Transportation
9.7.7. Others (Chemicals, Paper and Printing, and Agriculture, etc.)
9.8. By Country (USD)
9.8.1. Turkey
9.8.2. Israel
9.8.3. GCC
9.8.4. North Africa
9.8.5. South Africa
9.8.6. Rest of MEA
10. Asia Pacific Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2019-2032
10.1. Key Findings
10.2. By Component (USD)
10.2.1. Hardware
10.2.2. Software
10.2.2.1. Integrated
10.2.2.2. Standalone
10.3. By Deployment (USD)
10.3.1. On-premise
10.3.2. Cloud-based
10.4. By Enterprise Type (USD)
10.4.1. Large Enterprises
10.4.2. Small and Mid-sized Enterprises (SMEs)
10.5. By Technology (USD)
10.5.1. IoT
10.5.2. Artificial Intelligence and Machine Learning
10.5.3. Digital Twin
10.5.4. Advance Analytics
10.5.5. Others (Modern Database, ERP, etc.)
10.6. By Application (USD)
10.6.1. Condition Monitoring
10.6.2. Predictive Analytics
10.6.3. Remote Monitoring
10.6.4. Asset Tracking
10.6.5. Maintenance Scheduling
10.7. By End-Use (USD)
10.7.1. Military and Defense
10.7.2. Energy and Utilities
10.7.3. Manufacturing
10.7.4. Healthcare
10.7.5. IT and Telecom
10.7.6. Logistics and Transportation
10.7.7. Others (Chemicals, Paper and Printing, and Agriculture, etc.)
10.8. By Country (USD)
10.8.1. China
10.8.2. India
10.8.3. Japan
10.8.4. South Korea
10.8.5. ASEAN
10.8.6. Oceania
10.8.7. Rest of Asia Pacific
11. Company Profiles for Top 10 Players (Based on data availability in public domain and/or on paid databases)
11.1. IBM Corporation
11.1.1. Overview
11.1.1.1. Key Management
11.1.1.2. Headquarters
11.1.1.3. Offerings/Business Segments
11.1.2. Key Details (Key details are consolidated data and not product/service specific)
11.1.2.1. Employee Size
11.1.2.2. Past and Current Revenue
11.1.2.3. Geographical Share
11.1.2.4. Business Segment Share
11.1.2.5. Recent Developments
11.2. General Electric
11.2.1. Overview
11.2.1.1. Key Management
11.2.1.2. Headquarters
11.2.1.3. Offerings/Business Segments
11.2.2. Key Details (Key details are consolidated data and not product/service specific)
11.2.2.1. Employee Size
11.2.2.2. Past and Current Revenue
11.2.2.3. Geographical Share
11.2.2.4. Business Segment Share
11.2.2.5. Recent Developments
11.3. Siemens
11.3.1. Overview
11.3.1.1. Key Management
11.3.1.2. Headquarters
11.3.1.3. Offerings/Business Segments
11.3.2. Key Details (Key details are consolidated data and not product/service specific)
11.3.2.1. Employee Size
11.3.2.2. Past and Current Revenue
11.3.2.3. Geographical Share
11.3.2.4. Business Segment Share
11.3.2.5. Recent Developments
11.4. C3.ai, Inc.
11.4.1. Overview
11.4.1.1. Key Management
11.4.1.2. Headquarters
11.4.1.3. Offerings/Business Segments
11.4.2. Key Details (Key details are consolidated data and not product/service specific)
11.4.2.1. Employee Size
11.4.2.2. Past and Current Revenue
11.4.2.3. Geographical Share
11.4.2.4. Business Segment Share
11.4.2.5. Recent Developments
11.5. Rockwell Automation
11.5.1. Overview
11.5.1.1. Key Management
11.5.1.2. Headquarters
11.5.1.3. Offerings/Business Segments
11.5.2. Key Details (Key details are consolidated data and not product/service specific)
11.5.2.1. Employee Size
11.5.2.2. Past and Current Revenue
11.5.2.3. Geographical Share
11.5.2.4. Business Segment Share
11.5.2.5. Recent Developments
11.6. PTC
11.6.1. Overview
11.6.1.1. Key Management
11.6.1.2. Headquarters
11.6.1.3. Offerings/Business Segments
11.6.2. Key Details (Key details are consolidated data and not product/service specific)
11.6.2.1. Employee Size
11.6.2.2. Past and Current Revenue
11.6.2.3. Geographical Share
11.6.2.4. Business Segment Share
11.6.2.5. Recent Developments
11.7. Hitachi, Ltd.
11.7.1. Overview
11.7.1.1. Key Management
11.7.1.2. Headquarters
11.7.1.3. Offerings/Business Segments
11.7.2. Key Details (Key details are consolidated data and not product/service specific)
11.7.2.1. Employee Size
11.7.2.2. Past and Current Revenue
11.7.2.3. Geographical Share
11.7.2.4. Business Segment Share
11.7.2.5. Recent Developments
11.8. UpKeep
11.8.1. Overview
11.8.1.1. Key Management
11.8.1.2. Headquarters
11.8.1.3. Offerings/Business Segments
11.8.2. Key Details (Key details are consolidated data and not product/service specific)
11.8.2.1. Employee Size
11.8.2.2. Past and Current Revenue
11.8.2.3. Geographical Share
11.8.2.4. Business Segment Share
11.8.2.5. Recent Developments
11.9. Augury Ltd.
11.9.1. Overview
11.9.1.1. Key Management
11.9.1.2. Headquarters
11.9.1.3. Offerings/Business Segments
11.9.2. Key Details (Key details are consolidated data and not product/service specific)
11.9.2.1. Employee Size
11.9.2.2. Past and Current Revenue
11.9.2.3. Geographical Share
11.9.2.4. Business Segment Share
11.9.2.5. Recent Developments
11.10. The Soothsayer (P-Dictor)
11.10.1. Overview
11.10.1.1. Key Management
11.10.1.2. Headquarters
11.10.1.3. Offerings/Business Segments
11.10.2. Key Details (Key details are consolidated data and not product/service specific)
11.10.2.1. Employee Size
11.10.2.2. Past and Current Revenue
11.10.2.3. Geographical Share
11.10.2.4. Business Segment Share
11.10.2.5. Recent Developments

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