½ÃÀ庸°í¼­
»óǰÄÚµå
1722421

¿¹Áöº¸Àü ½ÃÀå º¸°í¼­ : ÄÄÆ÷³ÍÆ®, ±â¹ý, Àü°³ À¯Çü, Á¶Á÷ ±Ô¸ð, ¾÷°èº°, Áö¿ªº°(2025-2033³â)

Predictive Maintenance Market Report by Component, Technique, Deployment Type, Organization Size, Industry Vertical, and Region 2025-2033

¹ßÇàÀÏ: | ¸®¼­Ä¡»ç: IMARC | ÆäÀÌÁö Á¤º¸: ¿µ¹® 134 Pages | ¹è¼Û¾È³» : 2-3ÀÏ (¿µ¾÷ÀÏ ±âÁØ)

    
    
    




¡Ø º» »óǰÀº ¿µ¹® ÀÚ·á·Î Çѱ۰ú ¿µ¹® ¸ñÂ÷¿¡ ºÒÀÏÄ¡ÇÏ´Â ³»¿ëÀÌ ÀÖÀ» °æ¿ì ¿µ¹®À» ¿ì¼±ÇÕ´Ï´Ù. Á¤È®ÇÑ °ËÅ並 À§ÇØ ¿µ¹® ¸ñÂ÷¸¦ Âü°íÇØÁֽñ⠹ٶø´Ï´Ù.

¼¼°è ¿¹Áöº¸Àü ½ÃÀå ±Ô¸ð´Â 2024³â 127¾ï ´Þ·¯¿¡ ´ÞÇß½À´Ï´Ù. ÇâÈÄ IMARC GroupÀº 2033³â¿¡´Â 806¾ï ´Þ·¯¿¡ µµ´ÞÇϰí, 2025-2033³â 22.8%ÀÇ ¿¬Æò±Õ ¼ºÀå·ü(CAGR)À» º¸ÀÏ °ÍÀ¸·Î ¿¹ÃøÇß½À´Ï´Ù. ±â°è °£(M2M) Åë½ÅÀÇ »ç¿ë È®´ë¿Í °í±Þ °Ë»ç¸¦ ¼öÇàÇϱâ À§ÇÑ ¿ø°Ý ¸ð´ÏÅ͸µ°úÀÇ ÅëÇÕÀÌ ½ÃÀåÀ» ÁÖµµÇϰí ÀÖ½À´Ï´Ù.

¿¹Áöº¸ÀüÀº ¿îÀü ÁßÀÎ ÀåºñÀÇ ¼º´ÉÀ» ¸ð´ÏÅ͸µÇϱâ À§ÇÑ »óÅ ¸ð´ÏÅ͸µ µµ±¸¿Í ½Ã½ºÅÛ¿¡ ÀÇÁ¸ÇÏ´Â ±â¼úÀ» ¸»ÇÕ´Ï´Ù. »ç¹°ÀÎÅͳÝ(IoT), ÀΰøÁö´É(AI), ÅëÇÕ ½Ã½ºÅÛÀ¸·Î ±¸¼ºµÇ¾î ´Ù¾çÇÑ ÀÚ»ê°ú ½Ã½ºÅÛÀ» ¿¬°áÇϰí Áß¿äÇÑ µ¥ÀÌÅ͸¦ °øÀ¯ ¹× ºÐ¼®ÇÕ´Ï´Ù. ¶ÇÇÑ ¿¹Áöº¸Àü ¼¾¼­, »ê¾÷¿ë Á¦¾îÀåÄ¡, ±â¾÷ÀÚ»ê°ü¸®(EAM) ¹× ±â¾÷ÀÚ¿ø°èȹ(ERP) ¼ÒÇÁÆ®¿þ¾î µîÀÇ ºñÁî´Ï½º ½Ã½ºÅÛÀ¸·Î ±¸¼ºµË´Ï´Ù. »óÅ ¸ð´ÏÅ͸µ ÀåÄ¡¸¦ Ȱ¿ëÇÏ¿© ÀÚ»êÀÇ ¼º´ÉÀ» °Ë»ç ¹× Æò°¡ÇÔÀ¸·Î½á ±â´ÉÇÕ´Ï´Ù. ¿Âµµ, Áøµ¿, Àüµµµµ µî ´Ù¾çÇÑ µ¥ÀÌÅ͸¦ ±â·ÏÇÏ¿© ¿£Áö´Ï¾î°¡ Àåºñ³ª ÀÚ»êÀÇ °íÀåÀ» ¿¹ÃøÇÏ°í »çÀü¿¡ ±³Ã¼³ª ¼ö¸®¸¦ ÇÒ ¼ö ÀÖµµ·Ï ÇÕ´Ï´Ù. À¯Áöº¸¼ö ºñ¿ë Àý°¨, Àåºñ º¸°ü ±â°£ ¿¬Àå, »ý»ê¼º Çâ»ó¿¡ ±â¿©ÇÕ´Ï´Ù. ¶ÇÇÑ, ¿¹Áöº¸ÀüÀº ¾ÈÀü ±ÔÁ¤ Áؼö ¹× ¼±Á¦Àû ½ÃÁ¤ Á¶Ä¡¸¦ Á¦°øÇϱ⠶§¹®¿¡ Àü ¼¼°èÀûÀ¸·Î ¼ö¿ä°¡ Áõ°¡Çϰí ÀÖ½À´Ï´Ù.

¿¹Áöº¸Àü ½ÃÀå µ¿Çâ :

ÇöÀç ¿¹Áöº¸Àü ¼ö¿ä°¡ Áõ°¡Çϰí ÀÖ´Â °ÍÀº ´Ù¾çÇÑ »ê¾÷ ÀÚ»êÀÇ ¿î¿µ ÀÚµ¿È­°¡ ÁøÇàµÇ°í Àֱ⠶§¹®À̸ç, ÀÌ´Â ½ÃÀå¿¡ ±àÁ¤ÀûÀÎ ¿µÇâÀ» ¹ÌÄ¡´Â ÁÖ¿ä ¿äÀÎ Áß ÇϳªÀÔ´Ï´Ù. ¶ÇÇÑ M2M(Machine-to-Machine) Åë½Å°ú Ŭ¶ó¿ìµå ±â¼úÀÇ È°¿ëÀÌ ÁøÇàµÇ¸é¼­ »ê¾÷ ÀÚ»ê°ú ºñÁî´Ï½º Àڻ꿡¼­ ¾òÀ» ¼ö ÀÖ´Â ´Ù¾çÇÑ Á¤º¸¸¦ Á¶»çÇÒ ¼ö ÀÖ°Ô µÇ¾î ½ÃÀå Àü¸ÁÀº ¾çÈ£ÇÑ ÀÓº£µðµå´Ï´Ù. ¶ÇÇÑ, ±â¼úÀÚ°¡ ÀûÀýÇÑ ÀýÂ÷¸¦ ÅëÇØ ¼ö¸®¸¦ °èȹÇϰí ÁغñÇϱâ À§ÇØ ¿¹Áöº¸ÀüÀ» äÅÃÇÏ´Â °æ¿ì°¡ Áõ°¡Çϰí ÀÖ½À´Ï´Ù. ÀÌ´Â »ý»ê ÁÖ±âÀÇ Áߴܰú °èȹµÇÁö ¾ÊÀº ´Ù¿îŸÀÓÀ» ¹æÁöÇϱâ À§ÇØ ¿¹Áöº¸ÀüÀ» äÅÃÇÏ´Â »ç·Ê°¡ Áõ°¡Çϸ鼭 ½ÃÀå ¼ºÀåÀ» °¡¼ÓÇϰí ÀÖ½À´Ï´Ù. À̿ʹ º°µµ·Î, °¡½ÃÀûÀÎ ÅõÀÚ¼öÀÍ·ü(ROI)À» âÃâÇϱâ À§ÇØ ±â¾÷µéÀÇ ¿¹Áöº¸Àü Ȱ¿ëÀÌ Áõ°¡Çϰí ÀÖ½À´Ï´Ù. ÀÌ¿Í ´õºÒ¾î, ³ëÈÄÈ­µÈ ´Ù¾çÇÑ »ê¾÷±â°èÀÇ ¼ö¸íÀ» ¿¬ÀåÇϱâ À§ÇÑ ÅõÀÚ°¡ Áõ°¡Çϰí ÀÖ´Â °Íµµ ½ÃÀå ¼ºÀå¿¡ ±â¿©Çϰí ÀÖ½À´Ï´Ù. ¶ÇÇÑ, ¿¹Áöº¸Àü°ú ¿ø°Ý ¸ð´ÏÅ͸µÀÇ ÅëÇÕÀÌ ÁøÇàµÇ¾î °íµµÀÇ °Ë»ç ¹× ºÎǰ/¼³ºñÀÇ °íÀå ¿¹ÃøÀÌ °¡´ÉÇØÁ³½À´Ï´Ù´Â Á¡µµ ½ÃÀå ¼ºÀåÀ» µÞ¹ÞħÇϰí ÀÖ½À´Ï´Ù. ¶ÇÇÑ, ÀÇ·á ÀÎÇÁ¶óÀÇ ½Å·Ú¼ºÀ» Çâ»ó½Ã۱â À§ÇØ ÇコÄÉ¾î ºÐ¾ß¿¡¼­ ¿¹Áöº¸Àü äÅÃÀÌ Áõ°¡Çϰí ÀÖ´Â °Íµµ ½ÃÀå ¼ºÀåÀ» °¡¼ÓÇϰí ÀÖ½À´Ï´Ù.

º» º¸°í¼­¿¡¼­ ´Ù·é ÁÖ¿ä Áú¹®

  • 2024³â ¼¼°è ¿¹Áöº¸Àü ½ÃÀå ±Ô¸ð´Â?
  • 2025-2033³â ¿¹Áöº¸Àü ¼¼°è ½ÃÀå ¼ºÀå·ü Àü¸ÁÀº?
  • ¿¹Áöº¸Àü ¼¼°è ½ÃÀåÀ» À̲ô´Â ÁÖ¿ä ¿äÀÎÀº?
  • Äڷγª19°¡ ¿¹Áöº¸Àü ¼¼°è ½ÃÀå¿¡ ¹ÌÄ¡´Â ¿µÇâÀº?
  • ¿¹Áöº¸Àü ¼¼°è ½ÃÀåÀÇ ±¸¼º¿ä¼Òº° ºÐ·ù´Â?
  • ¿¹Áöº¸Àü ¼¼°è ½ÃÀå¿¡¼­ÀÇ ±â¼úº° ±¸ºÐÀº?
  • Àü°³ À¯Çü¿¡ µû¸¥ ¿¹Áöº¸Àü ¼¼°è ½ÃÀå ºÐ¼®Àº?
  • Á¶Á÷ ±Ô¸ð¿¡ µû¸¥ ¿¹Áöº¸Àü ¼¼°è ½ÃÀå ÇöȲÀº?
  • ¾÷Á¾º° ¿¹Áöº¸Àü ¼¼°è ½ÃÀå ÇöȲÀº?
  • ¿¹Áöº¸Àü ¼¼°è ½ÃÀåÀÇ ÁÖ¿ä Áö¿ªÀº?
  • ¿¹Áöº¸Àü ¼¼°è ½ÃÀåÀÇ ÁÖ¿ä ±â¾÷Àº?

¸ñÂ÷

Á¦1Àå ¼­¹®

Á¦2Àå Á¶»ç ¹üÀ§¿Í Á¶»ç ¹æ¹ý

  • Á¶»ç ¸ñÀû
  • ÀÌÇØ°ü°èÀÚ
  • µ¥ÀÌÅÍ ¼Ò½º
    • 1Â÷ Á¤º¸
    • 2Â÷ Á¤º¸
  • ½ÃÀå ÃßÁ¤
    • º¸ÅÒ¾÷ Á¢±Ù
    • Åé´Ù¿î Á¢±Ù
  • Á¶»ç ¹æ¹ý

Á¦3Àå ÁÖ¿ä ¿ä¾à

Á¦4Àå ¼­·Ð

  • °³¿ä
  • ÁÖ¿ä ¾÷°è µ¿Çâ

Á¦5Àå ¼¼°èÀÇ ¿¹Áöº¸Àü ½ÃÀå

  • ½ÃÀå °³¿ä
  • ½ÃÀå ½ÇÀû
  • COVID-19ÀÇ ¿µÇâ
  • ½ÃÀå ¿¹Ãø

Á¦6Àå ½ÃÀå ºÐ¼® : ÄÄÆ÷³ÍÆ®º°

  • ¼Ö·ç¼Ç
  • ¼­ºñ½º

Á¦7Àå ½ÃÀå ºÐ¼® : ±â¹ýº°

  • Áøµ¿ ¸ð´ÏÅ͸µ
  • Àü±â ½ÃÇè
  • ¿ÀÀÏ ºÐ¼®
  • ÃÊÀ½ÆÄ °¡½º ´©Ãâ °ËÃâ±â
  • ¼îÅ© ÆÞ½º
  • Àû¿Ü¼±
  • ±âŸ

Á¦8Àå ½ÃÀå ºÐ¼® : Àü°³ À¯Çüº°

  • Ŭ¶ó¿ìµå ±â¹Ý
  • On-Premise

Á¦9Àå ½ÃÀå ºÐ¼® : Á¶Á÷ ±Ô¸ðº°

  • Áß¼Ò±â¾÷
  • ´ë±â¾÷

Á¦10Àå ½ÃÀå ºÐ¼® : ¾÷°èº°

  • Á¦Á¶
  • ¿¡³ÊÁö ¹× À¯Æ¿¸®Æ¼
  • Ç×°ø¿ìÁÖ ¹× ¹æÀ§
  • ¿î¼Û ¹× ¹°·ù
  • Á¤ºÎ
  • ÇコÄɾî
  • ±âŸ

Á¦11Àå ½ÃÀå ºÐ¼® : Áö¿ªº°

  • ºÏ¹Ì
    • ¹Ì±¹
    • ij³ª´Ù
  • ¾Æ½Ã¾ÆÅÂÆò¾ç
    • Áß±¹
    • ÀϺ»
    • Àεµ
    • Çѱ¹
    • È£ÁÖ
    • Àεµ³×½Ã¾Æ
    • ±âŸ
  • À¯·´
    • µ¶ÀÏ
    • ÇÁ¶û½º
    • ¿µ±¹
    • ÀÌÅ»¸®¾Æ
    • ½ºÆäÀÎ
    • ·¯½Ã¾Æ
    • ±âŸ
  • ¶óƾ¾Æ¸Þ¸®Ä«
    • ºê¶óÁú
    • ¸ß½ÃÄÚ
    • ±âŸ
  • Áßµ¿ ¹× ¾ÆÇÁ¸®Ä«
    • ½ÃÀå ³»¿ª : ±¹°¡º°

Á¦12Àå SWOT ºÐ¼®

  • °³¿ä
  • °­Á¡
  • ¾àÁ¡
  • ±âȸ
  • À§Çù

Á¦13Àå ¹ë·ùüÀÎ ºÐ¼®

Á¦14Àå PorterÀÇ Five Forces ºÐ¼®

  • °³¿ä
  • ¹ÙÀ̾îÀÇ ±³¼··Â
  • °ø±Þ ±â¾÷ÀÇ ±³¼··Â
  • °æÀï Á¤µµ
  • ½Å±Ô ÁøÃâ¾÷üÀÇ À§Çù
  • ´ëüǰÀÇ À§Çù

Á¦15Àå °¡°Ý ºÐ¼®

Á¦16Àå °æÀï ±¸µµ

  • ½ÃÀå ±¸Á¶
  • ÁÖ¿ä ±â¾÷
  • ÁÖ¿ä ±â¾÷ °³¿ä
    • Asystom
    • C3.ai Inc.
    • General Electric Company
    • Google LLC(Alphabet Inc.)
    • Hitachi Ltd.
    • International Business Machines Corporation
    • Microsoft Corporation
    • PTC Inc.
    • SAP SE
    • Software AG
    • Tibco Software Inc.
    • Uptake Technologies Inc.
LSH 25.05.29

The global predictive maintenance market size reached USD 12.7 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 80.6 Billion by 2033, exhibiting a growth rate (CAGR) of 22.8% during 2025-2033. The growing use of machine-to-machine (M2M) communication, coupled with the rising integration with remote monitoring to conduct advanced inspections, is primarily propelling the market.

Predictive maintenance refers to the technique that relies on condition-monitoring tools and systems to monitor the performance of equipment during operation. It comprises the internet of things (IoT), artificial intelligence (AI), and integrated systems to connect different assets and systems and share and analyze crucial data. It also consists of predictive maintenance sensors, industrial controls, and business systems, such as enterprise asset management (EAM) and enterprise resource planning (ERP) software. It functions by utilizing condition monitoring equipment to examine and evaluate the performance of assets. It records a wide range of data, such as temperature, vibrations, and conductivity, which enables an engineer to predict the failure of equipment or assets while allowing them to be replaced or repaired in advance. It helps reduce maintenance costs, increase the shelf life of equipment, and improve productivity. Furthermore, as predictive maintenance provides safety compliance and preemptive corrective actions, its demand is increasing around the world.

Predictive Maintenance Market Trends:

At present, the rising demand for predictive maintenance due to the increasing automation of operations of various industrial assets represents one of the primary factors influencing the market positively. Besides this, the growing utilization of machine-to-machine (M2M) communication and cloud technology to investigate a wide array of information derived from industrial and business assets is offering a favorable market outlook. Additionally, there is an increase in the adoption of predictive maintenance by technicians to plan and prepare for a repair by taking appropriate steps. This, along with the rising employment of predictive maintenance to prevent the disruption of production cycles and the occurrence of unplanned downtime, is propelling the growth of the market. Apart from this, there is a rise in the utilization of predictive maintenance by businesses to generate a tangible return on investment (ROI). This, coupled with the increasing investment in extending the lifespan of various aging industrial machinery, is contributing to the growth of the market. In addition, the rising integration of predictive maintenance with remote monitoring to conduct advanced inspections and predict component and equipment failures is supporting the market growth. Moreover, the increasing employment of predictive maintenance in the healthcare sector to improve the reliability of healthcare infrastructure is bolstering the market growth.

Key Market Segmentation:

Component Insights:

  • Solution
  • Service

Technique Insights:

  • Vibration Monitoring
  • Electrical Testing
  • Oil Analysis
  • Ultrasonic Leak Detectors
  • Shock Pulse
  • Infrared
  • Others

Deployment Type Insights:

  • Cloud-based
  • On-premises

Organization Size Insights:

  • Small and Medium-sized Enterprises
  • Large Enterprises

Industry Vertical Insights:

  • Manufacturing
  • Energy and Utilities
  • Aerospace and Defense
  • Transportation and Logistics
  • Government
  • Healthcare
  • Others

Regional Insights:

  • North America
  • United States
  • Canada
  • Asia-Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Indonesia
  • Others
  • Europe
  • Germany
  • France
  • United Kingdom
  • Italy
  • Spain
  • Russia
  • Others
  • Latin America
  • Brazil
  • Mexico
  • Others
  • Middle East and Africa
  • The report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Europe (the United Kingdom, Germany, France, Italy, Spain, Russia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America (the United States and Canada) was the largest market for predictive maintenance. Some of the factors driving the North America predictive maintenance market included the growing demand for remote monitoring facilities, rising technological advancements in business automation processes, increasing number of solution and service vendors, etc.

Competitive Landscape:

  • The report has also provided a comprehensive analysis of the competitive landscape in the global predictive maintenance market. Competitive analysis such as market structure, market share by key players, player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided. Some of the companies covered include Asystom, C3.ai Inc., General Electric Company, Google LLC (Alphabet Inc.), Hitachi Ltd., International Business Machines Corporation, Microsoft Corporation, PTC Inc., SAP SE, Software AG, Tibco Software Inc., Uptake Technologies Inc., etc. Kindly note that this only represents a partial list of companies, and the complete list has been provided in the report.

Key Questions Answered in This Report

  • 1.What was the size of the global predictive maintenance market in 2024?
  • 2.What is the expected growth rate of the global predictive maintenance market during 2025-2033?
  • 3.What are the key factors driving the global predictive maintenance market?
  • 4.What has been the impact of COVID-19 on the global predictive maintenance market?
  • 5.What is the breakup of the global predictive maintenance market based on the component?
  • 6.What is the breakup of the global predictive maintenance market based on the technique?
  • 7.What is the breakup of the global predictive maintenance market based on deployment type?
  • 8.What is the breakup of the global predictive maintenance market based on the organization size?
  • 9.What is the breakup of the global predictive maintenance market based on the industry vertical?
  • 10.What are the key regions in the global predictive maintenance market?
  • 11.Who are the key players/companies in the global predictive maintenance market?

Table of Contents

1 Preface

2 Scope and Methodology

  • 2.1 Objectives of the Study
  • 2.2 Stakeholders
  • 2.3 Data Sources
    • 2.3.1 Primary Sources
    • 2.3.2 Secondary Sources
  • 2.4 Market Estimation
    • 2.4.1 Bottom-Up Approach
    • 2.4.2 Top-Down Approach
  • 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

  • 4.1 Overview
  • 4.2 Key Industry Trends

5 Global Predictive Maintenance Market

  • 5.1 Market Overview
  • 5.2 Market Performance
  • 5.3 Impact of COVID-19
  • 5.4 Market Forecast

6 Market Breakup by Component

  • 6.1 Solution
    • 6.1.1 Market Trends
    • 6.1.2 Market Forecast
  • 6.2 Service
    • 6.2.1 Market Trends
    • 6.2.2 Market Forecast

7 Market Breakup by Technique

  • 7.1 Vibration Monitoring
    • 7.1.1 Market Trends
    • 7.1.2 Market Forecast
  • 7.2 Electrical Testing
    • 7.2.1 Market Trends
    • 7.2.2 Market Forecast
  • 7.3 Oil Analysis
    • 7.3.1 Market Trends
    • 7.3.2 Market Forecast
  • 7.4 Ultrasonic Leak Detectors
    • 7.4.1 Market Trends
    • 7.4.2 Market Forecast
  • 7.5 Shock Pulse
    • 7.5.1 Market Trends
    • 7.5.2 Market Forecast
  • 7.6 Infrared
    • 7.6.1 Market Trends
    • 7.6.2 Market Forecast
  • 7.7 Others
    • 7.7.1 Market Trends
    • 7.7.2 Market Forecast

8 Market Breakup by Deployment Type

  • 8.1 Cloud-based
    • 8.1.1 Market Trends
    • 8.1.2 Market Forecast
  • 8.2 On-premises
    • 8.2.1 Market Trends
    • 8.2.2 Market Forecast

9 Market Breakup by Organization Size

  • 9.1 Small and Medium-sized Enterprises
    • 9.1.1 Market Trends
    • 9.1.2 Market Forecast
  • 9.2 Large Enterprises
    • 9.2.1 Market Trends
    • 9.2.2 Market Forecast

10 Market Breakup by Industry Vertical

  • 10.1 Manufacturing
    • 10.1.1 Market Trends
    • 10.1.2 Market Forecast
  • 10.2 Energy and Utilities
    • 10.2.1 Market Trends
    • 10.2.2 Market Forecast
  • 10.3 Aerospace and Defense
    • 10.3.1 Market Trends
    • 10.3.2 Market Forecast
  • 10.4 Transportation and Logistics
    • 10.4.1 Market Trends
    • 10.4.2 Market Forecast
  • 10.5 Government
    • 10.5.1 Market Trends
    • 10.5.2 Market Forecast
  • 10.6 Healthcare
    • 10.6.1 Market Trends
    • 10.6.2 Market Forecast
  • 10.7 Others
    • 10.7.1 Market Trends
    • 10.7.2 Market Forecast

11 Market Breakup by Region

  • 11.1 North America
    • 11.1.1 United States
      • 11.1.1.1 Market Trends
      • 11.1.1.2 Market Forecast
    • 11.1.2 Canada
      • 11.1.2.1 Market Trends
      • 11.1.2.2 Market Forecast
  • 11.2 Asia-Pacific
    • 11.2.1 China
      • 11.2.1.1 Market Trends
      • 11.2.1.2 Market Forecast
    • 11.2.2 Japan
      • 11.2.2.1 Market Trends
      • 11.2.2.2 Market Forecast
    • 11.2.3 India
      • 11.2.3.1 Market Trends
      • 11.2.3.2 Market Forecast
    • 11.2.4 South Korea
      • 11.2.4.1 Market Trends
      • 11.2.4.2 Market Forecast
    • 11.2.5 Australia
      • 11.2.5.1 Market Trends
      • 11.2.5.2 Market Forecast
    • 11.2.6 Indonesia
      • 11.2.6.1 Market Trends
      • 11.2.6.2 Market Forecast
    • 11.2.7 Others
      • 11.2.7.1 Market Trends
      • 11.2.7.2 Market Forecast
  • 11.3 Europe
    • 11.3.1 Germany
      • 11.3.1.1 Market Trends
      • 11.3.1.2 Market Forecast
    • 11.3.2 France
      • 11.3.2.1 Market Trends
      • 11.3.2.2 Market Forecast
    • 11.3.3 United Kingdom
      • 11.3.3.1 Market Trends
      • 11.3.3.2 Market Forecast
    • 11.3.4 Italy
      • 11.3.4.1 Market Trends
      • 11.3.4.2 Market Forecast
    • 11.3.5 Spain
      • 11.3.5.1 Market Trends
      • 11.3.5.2 Market Forecast
    • 11.3.6 Russia
      • 11.3.6.1 Market Trends
      • 11.3.6.2 Market Forecast
    • 11.3.7 Others
      • 11.3.7.1 Market Trends
      • 11.3.7.2 Market Forecast
  • 11.4 Latin America
    • 11.4.1 Brazil
      • 11.4.1.1 Market Trends
      • 11.4.1.2 Market Forecast
    • 11.4.2 Mexico
      • 11.4.2.1 Market Trends
      • 11.4.2.2 Market Forecast
    • 11.4.3 Others
      • 11.4.3.1 Market Trends
      • 11.4.3.2 Market Forecast
  • 11.5 Middle East and Africa
    • 11.5.1 Market Trends
    • 11.5.2 Market Breakup by Country
    • 11.5.3 Market Forecast

12 SWOT Analysis

  • 12.1 Overview
  • 12.2 Strengths
  • 12.3 Weaknesses
  • 12.4 Opportunities
  • 12.5 Threats

13 Value Chain Analysis

14 Porters Five Forces Analysis

  • 14.1 Overview
  • 14.2 Bargaining Power of Buyers
  • 14.3 Bargaining Power of Suppliers
  • 14.4 Degree of Competition
  • 14.5 Threat of New Entrants
  • 14.6 Threat of Substitutes

15 Price Analysis

16 Competitive Landscape

  • 16.1 Market Structure
  • 16.2 Key Players
  • 16.3 Profiles of Key Players
    • 16.3.1 Asystom
      • 16.3.1.1 Company Overview
      • 16.3.1.2 Product Portfolio
    • 16.3.2 C3.ai Inc.
      • 16.3.2.1 Company Overview
      • 16.3.2.2 Product Portfolio
      • 16.3.2.3 Financials
    • 16.3.3 General Electric Company
      • 16.3.3.1 Company Overview
      • 16.3.3.2 Product Portfolio
      • 16.3.3.3 Financials
      • 16.3.3.4 SWOT Analysis
    • 16.3.4 Google LLC (Alphabet Inc.)
      • 16.3.4.1 Company Overview
      • 16.3.4.2 Product Portfolio
      • 16.3.4.3 SWOT Analysis
    • 16.3.5 Hitachi Ltd.
      • 16.3.5.1 Company Overview
      • 16.3.5.2 Product Portfolio
      • 16.3.5.3 Financials
      • 16.3.5.4 SWOT Analysis
    • 16.3.6 International Business Machines Corporation
      • 16.3.6.1 Company Overview
      • 16.3.6.2 Product Portfolio
      • 16.3.6.3 Financials
      • 16.3.6.4 SWOT Analysis
    • 16.3.7 Microsoft Corporation
      • 16.3.7.1 Company Overview
      • 16.3.7.2 Product Portfolio
      • 16.3.7.3 Financials
      • 16.3.7.4 SWOT Analysis
    • 16.3.8 PTC Inc.
      • 16.3.8.1 Company Overview
      • 16.3.8.2 Product Portfolio
      • 16.3.8.3 Financials
      • 16.3.8.4 SWOT Analysis
    • 16.3.9 SAP SE
      • 16.3.9.1 Company Overview
      • 16.3.9.2 Product Portfolio
      • 16.3.9.3 Financials
      • 16.3.9.4 SWOT Analysis
    • 16.3.10 Software AG
      • 16.3.10.1 Company Overview
      • 16.3.10.2 Product Portfolio
      • 16.3.10.3 Financials
    • 16.3.11 Tibco Software Inc.
      • 16.3.11.1 Company Overview
      • 16.3.11.2 Product Portfolio
      • 16.3.11.3 SWOT Analysis
    • 16.3.12 Uptake Technologies Inc.
      • 16.3.12.1 Company Overview
      • 16.3.12.2 Product Portfolio
»ùÇà ¿äû ¸ñ·Ï
0 °ÇÀÇ »óǰÀ» ¼±Åà Áß
¸ñ·Ï º¸±â
Àüü»èÁ¦