AI 활용 예지보전 시장 예측(-2032년) : 오퍼링별, 솔루션별, 도입 형태별, 조직 규모별, 기법별, 업계별, 지역별

The AI-driven predictive maintenance market is anticipated to grow from USD 2.61 billion in 2026 to USD 19.27 billion by 2032, at a CAGR of 39.5% between 2026 and 2032. The increasing focus on cost optimization and asset lifecycle extension across industries drives the market growth.

Organizations are under growing pressure to reduce maintenance costs while maximizing the efficiency and lifespan of critical equipment, leading to a shift from reactive and scheduled maintenance to data-driven approaches. AI-enabled predictive maintenance solutions help identify potential failures in advance, optimize spare parts inventory, and reduce unnecessary maintenance activities, thereby lowering operational expenditure and improving return on assets. This cost-efficiency advantage is encouraging widespread adoption across manufacturing, energy, transportation, and other asset-intensive sectors.

"Vibration analysis is expected to hold the largest share, by technique, in 2032."

Vibration analysis currently holds the largest share of the AI-driven predictive maintenance market and is expected to remain one of the leading segments during the forecast period. Its dominance can be attributed to its widespread adoption across industries for early fault detection and equipment monitoring. This technique is extensively used in rotating machinery such as motors, pumps, and turbines, where changes in vibration patterns help identify issues such as imbalance, misalignment, and component wear.

While other techniques, such as thermal imaging and oil analysis, are gaining traction, vibration analysis remains a foundational approach in predictive maintenance. As industries increasingly adopt proactive maintenance strategies and focus on operational efficiency, the demand for vibration analysis is expected to remain strong, thereby supporting its significant market share.

"The healthcare industry is estimated to record the highest CAGR during the forecast period."

The healthcare segment is projected to grow at the highest CAGR in the AI-driven predictive maintenance market, driven by the increasing adoption of AI to improve equipment reliability and patient care. Healthcare industries are increasingly using AI to monitor critical medical equipment such as imaging systems, diagnostic devices, and hospital infrastructure, helping detect potential issues early and reduce unplanned downtime. The growing use of connected devices and digital systems is generating large volumes of data, enabling more accurate and timely maintenance planning through real-time insights. In addition, the need to ensure continuous operations, reduce maintenance costs, and improve asset utilization is encouraging the adoption of predictive maintenance solutions across healthcare facilities. The integration of IoT and advanced analytics is further supporting this shift toward proactive maintenance strategies. As healthcare providers continue to focus on operational efficiency, service quality, and patient safety, the demand for AI-driven predictive maintenance solutions is expected to increase significantly during the forecast period.

"The Asia Pacific is expected to grow at the highest CAGR during the forecast period."

The Asia Pacific is expected to grow at the highest CAGR in the AI-driven predictive maintenance market, driven by rapid digital transformation and increasing AI adoption across industries. Countries such as China, Japan, South Korea, and India are investing in smart manufacturing, industrial automation, and digital infrastructure, driving demand for predictive maintenance solutions. Governments across the region are supporting technology adoption through initiatives focused on Industry 4.0, smart factories, and the development of the digital economy. The growing use of connected equipment and IoT devices is generating large volumes of operational data, enabling organizations to adopt AI-based solutions for real-time monitoring and early fault detection.

Extensive primary interviews were conducted with key industry experts in the AI-driven predictive maintenance to determine and verify the market size for various segments and subsegments gathered through secondary research. The breakdown of primary participants for the report is provided below:

The study includes insights from industry experts, ranging from component suppliers to Tier 1 companies and OEMs. The break-up of the primaries is as follows:

• By Company Type: Tier 1-40%, Tier 2-25%, and Tier 3-25%
• By Designation: C-level-40%, Directors-45%, and Others-15%
• By Region: North America-26%, Europe-28%, Asia Pacific-41%, and RoW-5%

The report profiles key players in the AI-driven predictive maintenance market, including their respective market rankings. Prominent players profiled in this report are IBM (US), Siemens (Germany), SAP SE (Germany), GE Vernova (US), C3.ai (US), ABB (Switzerland), Schneider Electric (France), Hitachi, Ltd. (Japan), Uptake Technologies Inc. (US), among others.

KONE (Finland), PTC (US), Emerson Electric Co. (US), Honeywell International Inc. (US), Augury Ltd. (US), Nanoprecise (Canada), Oracle (US), SKF AB (Sweden), Falkonry (US), Capgemini (France), Hexagon AB (Sweden), Dynamox (Brazil), Bosch Global Software Technologies Private Limited (India), eMaint (US), and Rockwell Automation (US) are among the few other companies in the market.

Research Coverage:

This research report categorizes the AI-driven predictive maintenance market based on offering, solution, deployment mode, organization size, technique, industry, and region. The report describes the major drivers, restraints, challenges, and opportunities for the market and forecasts them through 2032. Apart from this, the report includes leadership mapping and analysis of all companies in the ecosystem.

Key Benefits of Buying the Report

The report will help market leaders/new entrants in this market by providing information on the closest approximations of the numbers for the overall market and its subsegments. This report will help stakeholders understand the competitive landscape and gain additional insights to better position their businesses and plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.

The report provides insights into the following pointers:

• Analysis of Key Drivers (increasing demand for real-time condition monitoring systems, increasing demand to reduce unplanned equipment downtime, growing adoption of Industry 4.0 and smart manufacturing, expansion of IoT-enabled connected assets and sensors)
• Product Development/Innovation: Detailed insights into upcoming technologies, research & development activities, and new product launches in the market
• Market Development: Comprehensive information about lucrative markets-the report analyzes the market across varied regions.
• Market Diversification: Exhaustive information about new products, untapped geographies, recent developments, and investments in the market
• Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading market players, such as IBM (US), Siemens (Germany), SAP SE (Germany), GE Vernova (US), and C3.ai (US)

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 STUDY OBJECTIVES
• 1.2 MARKET DEFINITION
• 1.3 STUDY SCOPE
  • 1.3.1 MARKETS COVERED AND REGIONAL SCOPE
  • 1.3.2 INCLUSIONS AND EXCLUSIONS
  • 1.3.3 YEARS CONSIDERED
  • 1.3.4 CURRENCY CONSIDERED
• 1.4 STAKEHOLDERS

2 EXECUTIVE SUMMARY

• 2.1 KEY INSIGHTS AND MARKET HIGHLIGHTS
• 2.2 KEY MARKET PARTICIPANTS: SHARE INSIGHTS AND STRATEGIC DEVELOPMENTS
• 2.3 DISRUPTIVE TRENDS SHAPING MARKET
• 2.4 HIGH-GROWTH SEGMENTS AND EMERGING FRONTIERS
• 2.5 SNAPSHOT: GLOBAL MARKET SIZE, GROWTH RATE, AND FORECAST

3 PREMIUM INSIGHTS

• 3.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI-DRIVEN PREDICTIVE MAINTENANCE MARKET
• 3.2 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY OFFERING
• 3.3 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY SOLUTION
• 3.4 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY DEPLOYMENT
• 3.5 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY ORGANIZATION SIZE
• 3.6 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY TECHNIQUE
• 3.7 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY INDUSTRY
• 3.8 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY REGION

4 MARKET OVERVIEW

• 4.1 INTRODUCTION
• 4.2 MARKET DYNAMICS
  • 4.2.1 DRIVERS
    • 4.2.1.1 Increasing demand for real-time condition monitoring systems
    • 4.2.1.2 Need to reduce unplanned equipment downtime
    • 4.2.1.3 Growing adoption of Industry 4.0 and smart manufacturing
    • 4.2.1.4 Expansion of IoT-enabled connected assets and sensors
  • 4.2.2 RESTRAINTS
    • 4.2.2.1 High initial capital investment for AI infrastructure and sensor deployment
    • 4.2.2.2 Cybersecurity risks in connected industrial environments
  • 4.2.3 OPPORTUNITIES
    • 4.2.3.1 Expansion of predictive maintenance-as-a-service (PdMaaS) models
    • 4.2.3.2 Partnerships between AI vendors and industrial OEMs
    • 4.2.3.3 Expansion of AI at the edge for low-latency predictive analytics
  • 4.2.4 CHALLENGES
    • 4.2.4.1 Lack of skilled workforce
    • 4.2.4.2 Continuous model upgradation
• 4.3 INTERCONNECTED MARKETS AND CROSS-SECTOR OPPORTUNITIES
  • 4.3.1 INTERCONNECTED MARKETS
  • 4.3.2 CROSS-SECTOR OPPORTUNITIES
• 4.4 STRATEGIC MOVES BY TIER 1/2/3 PLAYERS

5 INDUSTRY TRENDS

• 5.1 INTRODUCTION
• 5.2 PORTER'S FIVE FORCES ANALYSIS
  • 5.2.1 THREAT OF NEW ENTRANTS
  • 5.2.2 THREAT OF SUBSTITUTES
  • 5.2.3 BARGAINING POWER OF SUPPLIERS
  • 5.2.4 BARGAINING POWER OF BUYERS
  • 5.2.5 INTENSITY OF COMPETITIVE RIVALRY
• 5.3 MACROECONOMIC OUTLOOK
  • 5.3.1 INTRODUCTION
  • 5.3.2 GDP TRENDS AND FORECAST
  • 5.3.3 TRENDS IN GLOBAL AI-DRIVEN PREDICTIVE MAINTENANCE MARKET
• 5.4 ECOSYSTEM ANALYSIS
• 5.5 PRICING ANALYSIS
  • 5.5.1 AVERAGE SELLING PRICE OF SOFTWARE, BY KEY PLAYER
  • 5.5.2 AVERAGE SELLING PRICE TREND, BY REGION, 2022-2025 (USD/MONTH)
• 5.6 TRADE ANALYSIS
  • 5.6.1 IMPORT SCENARIO (HS CODE 847150)
  • 5.6.2 EXPORT SCENARIO (HS CODE 847150)
• 5.7 KEY CONFERENCES AND EVENTS, 2026-2027
• 5.8 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS' BUSINESSES
• 5.9 INVESTMENT AND FUNDING SCENARIO, 2023-2025
• 5.10 CASE STUDY ANALYSIS
  • 5.10.1 KONE AND IBM IMPLEMENT AI-BASED PREDICTIVE MAINTENANCE FOR HUMLEGARDEN, ENHANCING OPERATIONAL EFFICIENCY
  • 5.10.2 SIEMENS ENABLES AI-DRIVEN PREDICTIVE MAINTENANCE FOR BLUESCOPE STEEL, IMPROVING MAINTENANCE EFFICIENCY AND DECISION-MAKING
  • 5.10.3 IBM ENABLES AI-DRIVEN PREDICTIVE MAINTENANCE FOR KONE, IMPROVING ELEVATOR RELIABILITY AND REDUCING DOWNTIME
• 5.11 IMPACT OF US TARIFFS-AI-DRIVEN PREDICTIVE MAINTENANCE MARKET
  • 5.11.1 INTRODUCTION
    • 5.11.1.1 Key tariff rates
  • 5.11.2 PRICE IMPACT ANALYSIS
  • 5.11.3 IMPACT OF COUNTRIES/REGIONS
    • 5.11.3.1 US
    • 5.11.3.2 Europe
    • 5.11.3.3 Asia Pacific
  • 5.11.4 IMPACT ON END-USE INDUSTRIES

6 TECHNOLOGICAL ADVANCEMENTS, PATENTS, INNOVATIONS, AND FUTURE APPLICATIONS

• 6.1 KEY EMERGING TECHNOLOGIES
  • 6.1.1 MACHINE LEARNING-BASED ANOMALY DETECTION TECHNOLOGY
  • 6.1.2 DIGITAL TWIN AND CONDITION MONITORING TECHNOLOGY
  • 6.1.3 EDGE AI-BASED REAL-TIME MONITORING TECHNOLOGY
  • 6.1.4 PRESCRIPTIVE ANALYTICS AND REMAINING USEFUL LIFE (RUL) ESTIMATION TECHNOLOGY
• 6.2 COMPLEMENTARY TECHNOLOGIES
  • 6.2.1 INDUSTRIAL INTERNET OF THINGS (IIOT) AND SENSOR NETWORKS
  • 6.2.2 CLOUD COMPUTING AND ENTERPRISE ASSET MANAGEMENT (EAM) INTEGRATION
• 6.3 TECHNOLOGY ROADMAP
• 6.4 PATENT ANALYSIS
• 6.5 FUTURE APPLICATIONS

7 REGULATORY LANDSCAPE

• 7.1 INTRODUCTION
  • 7.1.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
  • 7.1.2 STANDARDS
    • 7.1.2.1 ISO 55000
    • 7.1.2.2 ISO 17359
    • 7.1.2.3 IEC 62443
    • 7.1.2.4 ISO/IEC 27001
    • 7.1.2.5 ISO 14224
    • 7.1.2.6 NIST AI Risk Management Framework (AI RMF)

8 CUSTOMER LANDSCAPE AND BUYER BEHAVIOR

• 8.1 DECISION-MAKING PROCESS
• 8.2 KEY STAKEHOLDERS INVOLVED IN BUYING PROCESS AND THEIR EVALUATION CRITERIA
  • 8.2.1 KEY STAKEHOLDERS IN BUYING PROCESS
  • 8.2.2 BUYING CRITERIA
• 8.3 ADOPTION BARRIERS AND INTERNAL CHALLENGES
• 8.4 UNMET NEEDS FROM VARIOUS END USERS

9 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY OFFERING

• 9.1 INTRODUCTION
• 9.2 SOFTWARE
  • 9.2.1 RISING ADOPTION OF AI-POWERED ASSET ANALYTICS PLATFORMS DRIVING GROWTH OF PREDICTIVE MAINTENANCE SOFTWARE
• 9.3 SERVICES
  • 9.3.1 GROWING DEMAND FOR AI IMPLEMENTATION AND INTEGRATION EXPERTISE TO ACCELERATE ADOPTION OF PREDICTIVE MAINTENANCE SERVICES

10 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY SOLUTION

• 10.1 INTRODUCTION
• 10.2 INTEGRATED SOLUTIONS
  • 10.2.1 INCREASING DEMAND FOR UNIFIED ASSET MONITORING AND ANALYTICS PLATFORMS TO BOOST ADOPTION
• 10.3 STANDALONE SOLUTIONS
  • 10.3.1 NEED FOR SPECIALIZED CONDITION MONITORING TOOLS SUPPORTS UPTAKE OF STANDALONE SOLUTIONS

11 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY DEPLOYMENT MODE

• 11.1 INTRODUCTION
• 11.2 CLOUD-BASED
  • 11.2.1 RISING DEMAND FOR REAL-TIME MONITORING AND SCALABLE ANALYTICS TO DRIVE DEMAND FOR CLOUD-BASED PREDICTIVE MAINTENANCE
• 11.3 ON-PREMISES
  • 11.3.1 NEED FOR DATA CONTROL AND SECURE INDUSTRIAL INFRASTRUCTURE TO PROPEL MARKET GROWTH

12 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY ORGANIZATION SIZE

• 12.1 INTRODUCTION
• 12.2 LARGE ENTERPRISES
  • 12.2.1 HIGH COST OF DOWNTIME AND NEED FOR ENTERPRISE-WIDE MONITORING ARE ACCELERATING DEMAND
• 12.3 SMALL AND MEDIUM-SIZED ENTERPRISES
  • 12.3.1 INCREASING AVAILABILITY OF SCALABLE AND CLOUD-BASED PREDICTIVE MAINTENANCE PLATFORMS TO SUPPORT ADOPTION

13 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY TECHNIQUE

• 13.1 INTRODUCTION
• 13.2 VIBRATION ANALYSIS
  • 13.2.1 RISING NEED FOR EARLY DETECTION OF MECHANICAL FAILURES IN ROTATING EQUIPMENT DRIVES MARKET
• 13.3 INFRARED THERMOGRAPHY
  • 13.3.1 DEMAND FOR NON-CONTACT AND SAFE INSPECTION METHODS PROPELS ADOPTION OF INFRARED THERMOGRAPHY
• 13.4 ACOUSTIC MONITORING
  • 13.4.1 NEED FOR EARLY FAULT DETECTION AND NON-INVASIVE MONITORING TO SUPPORT USAGE
• 13.5 OIL ANALYSIS
  • 13.5.1 INCREASING USE OF HEAVY MACHINERY AND CRITICAL ASSETS IN MINING, ENERGY, ETC., BOOSTS DEMAND
• 13.6 MOTOR CIRCUIT ANALYSIS
  • 13.6.1 EXPANSION OF AUTOMATED INDUSTRIAL EQUIPMENT PROPELS USE OF MOTOR CIRCUIT ANALYSIS TECHNIQUES
• 13.7 OTHER TECHNIQUES

14 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY INDUSTRY

• 14.1 INTRODUCTION
• 14.2 ENERGY & UTILITIES
  • 14.2.1 GROWING NEED FOR CONTINUOUS MONITORING OF CRITICAL ENERGY INFRASTRUCTURE DRIVES PREDICTIVE MAINTENANCE ADOPTION
• 14.3 MANUFACTURING
  • 14.3.1 EXPANSION OF SMART MANUFACTURING AND AUTOMATION DRIVING PREDICTIVE MAINTENANCE ADOPTION
• 14.4 TRANSPORTATION
  • 14.4.1 INCREASING INFRASTRUCTURE MODERNIZATION AND DEMAND FOR OPERATIONAL RELIABILITY DRIVING PREDICTIVE MAINTENANCE ADOPTION
• 14.5 AEROSPACE & DEFENSE
  • 14.5.1 INCREASING EMPHASIS ON MISSION READINESS AND SYSTEM RELIABILITY TO PROPEL MARKET GROWTH
• 14.6 MINING & HEAVY EQUIPMENT
  • 14.6.1 RISING FOCUS ON EQUIPMENT UPTIME IN HARSH OPERATING CONDITIONS SUPPORTS ADOPTION
• 14.7 HEALTHCARE
  • 14.7.1 CONTINUOUS EQUIPMENT AVAILABILITY BECOMING CRITICAL FOR EFFICIENT HEALTHCARE OPERATION
• 14.8 TELECOMMUNICATIONS
  • 14.8.1 GROWING COMPLEXITY OF NETWORK INFRASTRUCTURE TO DRIVE DEMAND
• 14.9 OTHER INDUSTRIES

15 AI-DRIVEN PREDICTIVE MAINTENANCE MARKET, BY REGION

• 15.1 INTRODUCTION
• 15.2 NORTH AMERICA
  • 15.2.1 US
    • 15.2.1.1 Strong presence of highly developed industrial AI and analytics ecosystem to drive demand
  • 15.2.2 CANADA
    • 15.2.2.1 Growing demand for operational efficiency and regulatory compliance to spur market growth
  • 15.2.3 MEXICO
    • 15.2.3.1 Expansion of automotive manufacturing and industrial automation supporting adoption
• 15.3 EUROPE
  • 15.3.1 GERMANY
    • 15.3.1.1 Leadership in industry 4.0 and advanced manufacturing driving adoption of AI-driven predictive maintenance
  • 15.3.2 UK
    • 15.3.2.1 Industry 4.0 initiatives and advanced aerospace manufacturing driving adoption of AI-driven predictive maintenance
  • 15.3.3 FRANCE
    • 15.3.3.1 Accelerating industrial digitalization and aerospace expansion driving market growth
  • 15.3.4 ITALY
    • 15.3.4.1 Strong industrial machinery sector and manufacturing modernization driving predictive maintenance adoption
  • 15.3.5 REST OF EUROPE
• 15.4 ASIA PACIFIC
  • 15.4.1 CHINA
    • 15.4.1.1 Large-scale industrial production and smart factory initiatives driving adoption of AI-driven predictive maintenance
  • 15.4.2 JAPAN
    • 15.4.2.1 Increasing need to address aging industrial infrastructure and workforce shortages to spur market demand
  • 15.4.3 SOUTH KOREA
    • 15.4.3.1 Smart manufacturing and industrial automation driving adoption in South Korea
  • 15.4.4 INDIA
    • 15.4.4.1 Industrial digitalization and expansion of industrial automation supporting predictive maintenance adoption
  • 15.4.5 REST OF ASIA PACIFIC
• 15.5 REST OF THE WORLD (ROW)
  • 15.5.1 MIDDLE EAST & AFRICA
    • 15.5.1.1 GCC Countries
    • 15.5.1.2 Rest of Middle East & Africa
  • 15.5.2 SOUTH AMERICA
    • 15.5.2.1 Expansion of industrial production and energy infrastructure supporting predictive maintenance adoption

16 COMPETITIVE LANDSCAPE

• 16.1 INTRODUCTION
• 16.2 KEY PLAYERS, STRATEGIES/RIGHT TO WIN (2022-2026)
• 16.3 REVENUE ANALYSIS, 2022-2025
• 16.4 MARKET SHARE ANALYSIS, 2025
• 16.5 COMPANY VALUATION AND FINANCIAL METRICS
• 16.6 BRAND COMPARISON
• 16.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2025
  • 16.7.1 STARS
  • 16.7.2 EMERGING LEADERS
  • 16.7.3 PERVASIVE PLAYERS
  • 16.7.4 PARTICIPANTS
  • 16.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2025
    • 16.7.5.1 Company footprint
    • 16.7.5.2 Region footprint
    • 16.7.5.3 Offering footprint
    • 16.7.5.4 Deployment mode footprint
    • 16.7.5.5 Application footprint
• 16.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2025
  • 16.8.1 PROGRESSIVE COMPANIES
  • 16.8.2 RESPONSIVE COMPANIES
  • 16.8.3 DYNAMIC COMPANIES
  • 16.8.4 STARTING BLOCKS
  • 16.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2025
    • 16.8.5.1 Detailed list of key startups/SMEs
    • 16.8.5.2 Competitive benchmarking of key startups/SMEs
• 16.9 COMPETITIVE SCENARIO
  • 16.9.1 PRODUCT LAUNCHES
  • 16.9.2 DEALS

17 COMPANY PROFILES

• 17.1 KEY PLAYERS
  • 17.1.1 IBM
    • 17.1.1.1 Business overview
    • 17.1.1.2 Products/solutions/services offered
    • 17.1.1.3 Recent developments
      • 17.1.1.3.1 Product launches
    • 17.1.1.4 MnM view
      • 17.1.1.4.1 Key strengths/right to win
      • 17.1.1.4.2 Strategic choices made
      • 17.1.1.4.3 Weaknesses and competitive threats
  • 17.1.2 SIEMENS
    • 17.1.2.1 Business overview
    • 17.1.2.2 Products/solutions/services offered
    • 17.1.2.3 Recent developments
      • 17.1.2.3.1 Product launches
      • 17.1.2.3.2 Deals
    • 17.1.2.4 MnM view
      • 17.1.2.4.1 Key strengths/right to win
      • 17.1.2.4.2 Strategic choices made
      • 17.1.2.4.3 Weaknesses and competitive threats
  • 17.1.3 SAP SE
    • 17.1.3.1 Business overview
    • 17.1.3.2 Products/solutions/services offered
    • 17.1.3.3 MnM view
      • 17.1.3.3.1 Key strengths/right to win
      • 17.1.3.3.2 Strategic choices made
      • 17.1.3.3.3 Weaknesses and competitive threats
  • 17.1.4 GE VERNOVA
    • 17.1.4.1 Business overview
    • 17.1.4.2 Products/solutions/services offered
    • 17.1.4.3 Recent developments
      • 17.1.4.3.1 Product launches
      • 17.1.4.3.2 Deals
      • 17.1.4.3.3 Other developments
    • 17.1.4.4 MnM view
      • 17.1.4.4.1 Key strengths/right to win
      • 17.1.4.4.2 Strategic choices made
      • 17.1.4.4.3 Weaknesses and competitive threats
  • 17.1.5 C3.AI
    • 17.1.5.1 Business overview
    • 17.1.5.2 Products/solutions/services offered
    • 17.1.5.3 Recent developments
      • 17.1.5.3.1 Product enhancements
      • 17.1.5.3.2 Deals
      • 17.1.5.3.3 Other developments
    • 17.1.5.4 MnM view
      • 17.1.5.4.1 Key strengths/right to win
      • 17.1.5.4.2 Strategic choices made
      • 17.1.5.4.3 Weaknesses and competitive threats
  • 17.1.6 ABB
    • 17.1.6.1 Business overview
    • 17.1.6.2 Products/solutions/services offered
    • 17.1.6.3 Recent developments
      • 17.1.6.3.1 Deals
  • 17.1.7 SCHNEIDER ELECTRIC
    • 17.1.7.1 Business overview
    • 17.1.7.2 Products/solutions/services offered
    • 17.1.7.3 Recent developments
      • 17.1.7.3.1 Product launches
      • 17.1.7.3.2 Deals
  • 17.1.8 HITACHI, LTD.
    • 17.1.8.1 Business overview
    • 17.1.8.2 Products/solutions/services offered
    • 17.1.8.3 Recent developments
      • 17.1.8.3.1 Product launches
      • 17.1.8.3.2 Deals
  • 17.1.9 L&T TECHNOLOGY SERVICES LIMITED
    • 17.1.9.1 Business overview
    • 17.1.9.2 Products/solutions/services offered
    • 17.1.9.3 Recent developments
      • 17.1.9.3.1 Product launches
      • 17.1.9.3.2 Other deals
  • 17.1.10 UPTAKE TECHNOLOGIES INC.
    • 17.1.10.1 Business overview
    • 17.1.10.2 Products/solutions/services offered
    • 17.1.10.3 Recent developments
      • 17.1.10.3.1 Product launches
• 17.2 OTHER PLAYERS
  • 17.2.1 KONE
  • 17.2.2 PTC
  • 17.2.3 EMERSON ELECTRIC CO.
  • 17.2.4 HONEYWELL INTERNATIONAL INC.
  • 17.2.5 AUGURY LTD.
  • 17.2.6 NANOPRECISE
  • 17.2.7 ORACLE
  • 17.2.8 SKF
  • 17.2.9 FALKONRY
  • 17.2.10 CAPGEMINI
  • 17.2.11 HEXAGON AB
  • 17.2.12 DYNAMOX
  • 17.2.13 BOSCH GLOBAL SOFTWARE TECHNOLOGIES PRIVATE LIMITED
  • 17.2.14 EMAINT
  • 17.2.15 ROCKWELL AUTOMATION

18 RESEARCH METHODOLOGY

• 18.1 RESEARCH DATA
• 18.2 SECONDARY AND PRIMARY RESEARCH
  • 18.2.1 SECONDARY DATA
    • 18.2.1.1 Key data from secondary sources
    • 18.2.1.2 List of key secondary sources
  • 18.2.2 PRIMARY DATA
    • 18.2.2.1 Key data from primary sources
    • 18.2.2.2 List of primary interview participants
    • 18.2.2.3 Breakdown of primaries
    • 18.2.2.4 Key industry insights
• 18.3 MARKET SIZE ESTIMATION
  • 18.3.1 BOTTOM-UP APPROACH
  • 18.3.2 TOP-DOWN APPROACH
  • 18.3.3 MARKET SIZE CALCULATION FOR BASE YEAR
• 18.4 MARKET FORECAST APPROACH
  • 18.4.1 BOTTOM-UP APPROACH
  • 18.4.2 TOP-DOWN APPROACH
• 18.5 DATA TRIANGULATION
• 18.6 FACTOR ANALYSIS
• 18.7 RESEARCH ASSUMPTIONS
• 18.8 RESEARCH LIMITATIONS
• 18.9 RISK ANALYSIS

19 APPENDIX

• 19.1 DISCUSSION GUIDE
• 19.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
• 19.3 CUSTOMIZATION OPTIONS
• 19.4 RELATED REPORTS
• 19.5 AUTHOR DETAILS