세계의 광업용 AI 시장(2024-2031년)

Global AI in Mining Market Size, Share & Trends Analysis Report by Component (Hardware, Software, and Service), by Technology (Machine Learning, Computer Vision, Natural Language Processing, Robotics, and Data Analytics), and by Application (Exploration, Geology, Ore Sorting, Equipment Maintenance, Safety and Risk Management, Autonomous Drilling, and Hauling) Forecast Period (2024-2031)

Artificial Intelligence (AI) in mining market is anticipated to grow at a significant CAGR of 14.8% during the forecast period (2024-2031). The market growth is attributed to the growing adoption of AI in the mining industry which prompts and improves decision-making about the health and safety of frontline workers. Reducing exposure to hazardous situations and changing the entire working process to be more process-oriented is further driving the growth of AI in mining market. According to the Artificial Intelligence Index Report 2023, the number of pattern recognition papers has roughly doubled while the number of machine learning papers has roughly quadrupled. Following those two topic areas, in 2021, the next most published AI fields of study were computer vision (30,075), algorithm (21,527), and data mining (19,181).

Market Dynamics

Increasing Amplification of Mining Efficiency with Automation

A strategic focus on workplace productivity efforts is creating dramatic changes in the dynamic mining operations sector. This coordinated effort uses innovative strategies and technologies to maximize worker productivity and operational procedures. Important considerations include keeping an eye on the output of the mining labor force, with careful attention paid to preventing task duplication. In addition to avoiding resource waste, this guarantees redundant work removal. An atmosphere for operations that is more synchronized and efficient is produced by streamlining the synchronization of miners and machinery. The location of miners and equipment can be tracked in real-time, which makes it easier to allocate and employ resources optimally and ensures that the proper resources are used effectively. The overall productivity is greatly increased by this strategic strategy.

Growing Adoption of AI for Predictive Analytics and Enhanced Efficiency

A major initiative aiming at utilizing predictive analytics for increased operational efficiency is AI integration into mining operations. Sophisticated AI algorithms are used to improve overall mining operations, optimize resource allocation, and forecast equipment faults. This includes geological modeling and exploration, in which AI is critical to data analysis to forecast the occurrence of precious minerals and enhance exploration tactics. Examining past safety events, seeing trends, and putting predictive analysis into practice for preventative safety measures are all part of personnel safety analysis. Real-time location systems (RTLS) are essential to this whole analytical framework as they offer accurate real-time data, which improves the precision and dependability of these analyses and eventually results in more sophisticated and environmentally friendly mining techniques.

Market Segmentation

• Based on the component, the market is segmented into hardware, software, and service.
• Based on the technology, the market is segmented into machine learning, computer vision, natural language processing, robotics, and data analytics.
• Based on the application, the market is segmented into exploration, geology, ore sorting, equipment maintenance, safety and risk management, autonomous drilling, and hauling.

Data Analytics is Projected to Hold the Largest Segment

The data analytics segment is expected to hold the largest share of the market. The primary factors supporting the growth include increasing demand for data analytics in mining to capture data from diverse systems used in underground and open cast mining, distill actionable insights for real-time planning, productivity and workforce management, and cost rationalization. To extract contextual information, make deductions, and forecast results, AI-based data analytics ingests and evaluates time series, device/sensor data, and business characteristics. Big data platforms, analytical engines, and mathematical algorithms uncover business possibilities and problems to shape strategies for faster reactions and process real-time data streams and complex events. For instance, Altair Engineering Inc. offers Altair RapidMiner data and machine learning pipelines with code-free to code-friendly experiences. The tool uses Altair RapidMiner to spot anomalies, trends, and outliers in seconds with real-time data, and share results across the organization using rich, powerful dashboards.

Safety and Risk Management Segment to Hold a Considerable Market Share

The safety and risk management segment are expected to hold a considerable market share. The factors supporting segment growth include AI-based sophisticated tools to monitor and analyze behavior and activities in real-time. Smart mines are adopting high-tech solutions to manage personnel and asset performance holistically and economically. Sophisticated enterprise software is growing indispensable for minimizing asset downtime, optimizing productivity, cutting operating costs, and attaining best practice safety management by enabling managers to respond instantly. For instance, Hitachi Energy Ltd. offers Smart mining with new cloud-based technology to improve safety and predictability by overseeing people and equipment on-site from back-office locations, accessing real-time data for incident prevention, and even using facial recognition and geo-fencing alarms to safeguard isolated workers.

Regional Outlook

Global AI in mining market is further segmented based on geography including North America (the US, and Canada), Europe (the UK, Italy, Spain, Germany, France, and the Rest of Europe), Asia-Pacific (India, China, Japan, South Korea, and Rest of Asia-Pacific), and the Rest of the World (the Middle East & Africa, and Latin America).

Growing Demand for AI in Mining in Asia-Pacific

• The regional growth is attributed to pivotal factors such as the increasing adoption of digital technologies and AI. Technological innovations like AI and machine learning are used to improve productivity, effectiveness, safety, environmental considerations, and other areas to keep mining a profitable industry.
• According to the International Centre for Sustainable Carbon, in April 2024, AI revolutionized coal mining operations in China. The role of intelligence mining is getting more popular, especially in the Shanxi province, where technological advances are leading the way. The province had 118 intelligent coal mines and 1,491 intelligent mining sites functioning.
• Using a successful adoption of AI technologies, the operation units of coal mining will attain high-performance rates featuring reduced process, resource consumption, and reduced risks that involve the whole operation. The high level of support for AI applications in the coal industry, given the powerful computing center supplied at more than 200 petaflops and connected to the industrial internet platform.

North America Holds Major Market Share

North America holds a significant share owing to numerous prominent AI in mining companies and providers such as Google LLC, IBM Corp., and Microsoft Corp. Salesforce Inc. in the region. The market growth is attributed to the increasing adoption of AI-driven automation which enhances programmable logic devices and digital control systems to increase precision and consistency in mining operations. Market players in the region offering data mining ensure the success of complicated data projects by providing efficient, transparent, and predictable pricing, adaptable infrastructure-as-a-service capabilities, and professionals to assist clients at every stage. For instance, in January 2023, Nextpoint, in cloud-based e-discovery and litigation support software, released its data-mining suite of tactical ECA tools for legal teams looking to mitigate risk, save time and money, and tackle their ever-growing data volumes in eDiscovery. Data mining delivers the highest data processing speeds (30x faster), best-in-class data security, and real-time analytics and reporting that empower legal teams to reduce the scope of their review and make strategic decisions earlier in a case.

Market Players Outlook

The major companies serving the AI in mining market include ABB Ltd., Google LLC, IBM Corp., Microsoft Corp., and Siemens AG among others. The market players are increasingly focusing on business expansion and product development by applying strategies such as collaborations, mergers, and acquisitions to stay competitive.

Recent Development

• In August 2023, United States Steel Corp. and Google Cloud announced a new collaboration to build applications using Google Cloud's generative artificial intelligence ("gen AI") technology to drive efficiencies and improve employee experiences in the largest iron ore mine in North America. When operational, MineMind allows US Steel technicians to reduce the amount of time to complete a work order by an estimated 20.0%.

Table of Contents

1. Report Summary

• Current Industry Analysis and Growth Potential Outlook
• 1.1. Research Methods and Tools
• 1.2. Market Breakdown
  • 1.2.1. By Segments
  • 1.2.2. By Region

2. Market Overview and Insights

• 2.1. Scope of the Report
• 2.2. Analyst Insight & Current Market Trends
  • 2.2.1. Key Market Trends
  • 2.2.2. Recommendations
  • 2.2.3. Conclusion

3. Competitive Landscape

• 3.1. Key Company Analysis
• 3.2. ABB Ltd.
  • 3.2.1. Overview
  • 3.2.2. Financial Analysis
  • 3.2.3. SWOT Analysis
  • 3.2.4. Recent Developments
• 3.3. Google LLC
  • 3.3.1. Overview
  • 3.3.2. Financial Analysis
  • 3.3.3. SWOT Analysis
  • 3.3.4. Recent Developments
• 3.4. IBM Corp.
  • 3.4.1. Overview
  • 3.4.2. Financial Analysis
  • 3.4.3. SWOT Analysis
  • 3.4.4. Recent Developments
• 3.5. Microsoft Corp.
  • 3.5.1. Overview
  • 3.5.2. Financial Analysis
  • 3.5.3. SWOT Analysis
  • 3.5.4. Recent Developments
• 3.6. Siemens AG
  • 3.6.1. Overview
  • 3.6.2. Financial Analysis
  • 3.6.3. SWOT Analysis
  • 3.6.4. Recent Developments
• 3.7. Key Strategy Analysis

4. Market Segmentation

• 4.1. Global AI in Mining Market by Component
  • 4.1.1. Hardware
  • 4.1.2. Software
  • 4.1.3. Service
• 4.2. Global AI in Mining Market by Technology
  • 4.2.1. Machine Learning
  • 4.2.2. Computer Vision
  • 4.2.3. Natural Language Processing
  • 4.2.4. Robotics
  • 4.2.5. Data Analytics
• 4.3. Global AI in Mining Market by Application
  • 4.3.1. Exploration
  • 4.3.2. Geology
  • 4.3.3. Ore Sorting
  • 4.3.4. Equipment Maintenance
  • 4.3.5. Safety And Risk Management
  • 4.3.6. Autonomous Drilling
  • 4.3.7. Hauling

5. Regional Analysis

• 5.1. North America
  • 5.1.1. United States
  • 5.1.2. Canada
• 5.2. Europe
  • 5.2.1. UK
  • 5.2.2. Germany
  • 5.2.3. Italy
  • 5.2.4. Spain
  • 5.2.5. France
  • 5.2.6. Rest of Europe
• 5.3. Asia-Pacific
  • 5.3.1. China
  • 5.3.2. India
  • 5.3.3. Japan
  • 5.3.4. South Korea
  • 5.3.5. Rest of Asia-Pacific
• 5.4. Rest of the World
  • 5.4.1. Latin America
  • 5.4.2. Middle East and Africa

6. Company Profiles

• 6.1. Accenture PLC
• 6.2. Autonomous Solutions, Inc. (ASI)
• 6.3. Caterpillar Inc.
• 6.4. Cortex Logic
• 6.5. Dassault Systemes S.E.
• 6.6. General Electronic Co.
• 6.7. GoldSpot Discoveries, Inc.
• 6.8. Hexagon AB
• 6.9. Hitachi Construction Machinery Co., Ltd.
• 6.10. Infosys, Ltd.
• 6.11. Komatsu Corp.
• 6.12. Metso Corp.
• 6.13. MICROMINE
• 6.14. Minerva Intelligence, Inc.
• 6.15. Motion Metrics International Corp.
• 6.16. Newtrax Technologies, Inc.
• 6.17. Petra Data Science
• 6.18. Rio Tinto Group
• 6.19. Sandvik AB
• 6.20. SAP SE
• 6.21. Schneider Electric SE
• 6.22. Tenova S.p.A.
• 6.23. Wipro Ltd.