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市場調查報告書

商品編碼

1460706

製造業市場中的人工智慧 - 全球產業規模、佔有率、趨勢、機會和預測，按產品、技術、應用、產業、地區、競爭細分，2019-2029F

AI in Manufacturing Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Offering, By Technology, By Application, By Industry, By Region, By Competition, 2019-2029F

出版日期: 2024年04月03日 | 出版商:

TechSci Research | 英文 181 Pages | 商品交期: 2-3個工作天內

價格

簡介目錄

2023 年全球人工智慧在製造業市場的價值為 54 億美元，預計在預測期內將強勁成長，到 2029 年複合CAGR為37.8%。全球人工智慧在製造業市場正在經歷由多種因素共同重塑的顯著成長產業格局。隨著對營運效率的日益重視，製造商正在轉向人工智慧 (AI) 解決方案來最佳化生產流程並提高整體生產力。包括機器學習和預測分析在內的人工智慧技術正在部署以簡化營運、減少停機時間並提高資源利用率。人工智慧的整合使製造商能夠從大量資料集中獲得可行的見解，促進明智的決策，並為更敏捷和更具適應性的生產環境做出貢獻。

此外，對預測性維護解決方案的需求是推動人工智慧在製造業中採用的關鍵驅動力。由人工智慧演算法支援的預測性維護使製造商能夠預測設備故障並主動安排維護活動，從而最大限度地減少計劃外停機時間並最佳化資產利用率。品質最佳化是人工智慧取得重大進展的另一個關鍵方面，透過即時監控和分析幫助製造商提高產品品質。

隨著全球製造業經歷數位轉型，人工智慧正在成為一項關鍵技術，使製造商能夠在動態市場中保持競爭力。人工智慧的不斷進步，加上人們對其變革潛力的認知不斷增強，正在推動人工智慧在製造業市場的上升軌跡，為企業提供卓越營運和永續成長的途徑。

主要市場促進因素

提升營運效率

市場規模預測
- 按價值
市佔率預測
- 透過提供（硬體、軟體和服務）
- 按技術（電腦視覺、機器學習、自然語言處理）
- 依應用（製程控制、生產計畫、預測性維修機械檢查）
- 按行業（汽車、醫療器材、半導體電子）
- 按地區（北美、歐洲、南美、中東非洲、亞太地區）
按公司分類 (2023)
市場地圖

第 8 章：北美製造業人工智慧市場展望

市場規模預測
- 按價值
市佔率預測
- 透過提供
- 依技術
- 按申請
- 按行業分類
- 按國家/地區
北美：國家分析
- 美國製造業人工智慧市場展望
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類
- 加拿大製造業人工智慧市場展望
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類
- 墨西哥製造業人工智慧市場展望
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類

第 9 章：歐洲人工智慧在製造業市場的展望

市場規模預測
- 按價值
市佔率預測
- 透過提供
- 依技術
- 按申請
- 按行業分類
- 按國家/地區
歐洲：國家分析
- 德國人工智慧在製造業市場展望
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類
- 法國人工智慧在製造業市場展望
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類
- 英國製造業人工智慧市場展望
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類
- 義大利製造業人工智慧市場展望
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類
- 西班牙製造業人工智慧市場展望
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類
- 比利時人工智慧在製造業市場展望
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類

第 10 章：南美洲製造業人工智慧市場展望

市場規模預測
- 按價值
市佔率預測
- 透過提供
- 依技術
- 按申請
- 按行業分類
- 按國家/地區
南美洲：國家分析
- 巴西
- 哥倫比亞
- 阿根廷
- 智利
- 秘魯

第 11 章：中東非洲製造業中的人工智慧市場展望

市場規模預測
- 按價值
市佔率預測
- 透過提供
- 依技術
- 按申請
- 按行業分類
- 按國家/地區
中東非洲：國家分析
- 沙烏地阿拉伯人工智慧在製造業市場的展望
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類
- 阿拉伯聯合大公國人工智慧在製造業市場前景
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類
- 南非人工智慧在製造業市場的展望
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類
- 土耳其人工智慧在製造業市場前景
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類
- 以色列人工智慧在製造業市場展望
  - 市場規模預測
    - 按價值
  - 市佔率預測
    - 透過提供
    - 依技術
    - 按申請
    - 按行業分類

第 12 章：亞太地區製造業人工智慧市場展望

市場規模預測
- 按價值
市佔率預測
- 透過提供
- 依技術
- 按申請
- 按行業分類
- 按國家/地區
亞太地區：國家分析
- 中國
- 印度
- 日本
- 韓國
- 澳洲
- 印尼
- 越南

第 13 章：市場動態

促進要素
挑戰

第 14 章：市場趨勢與發展

第 15 章：公司簡介

Siemens AG
General Electric Company (GE)
International Business Machines Corporation (IBM)
SAP SE
Rockwell Automation, Inc.
Google LLC
NVIDIA Corporation.
Amazon.com, Inc.
Microsoft Corporation
Mitsubishi Electric Corporation.

第 16 章：策略建議

第 17 章：關於我們免責聲明

簡介目錄

Product Code: 22204

Global AI in Manufacturing Market was valued at USD 5.4 Billion in 2023 and is anticipated to project robust growth in the forecast period with a CAGR of 37.8% through 2029. The Global AI in Manufacturing Market is experiencing significant growth driven by a confluence of factors reshaping the industry landscape. With an increasing emphasis on operational efficiency, manufacturers are turning to artificial intelligence (AI) solutions to optimize production processes and enhance overall productivity. AI technologies, including machine learning and predictive analytics, are being deployed to streamline operations, reduce downtime, and improve resource utilization. The integration of AI enables manufacturers to gain actionable insights from vast datasets, facilitating informed decision-making and contributing to a more agile and adaptive production environment.

Moreover, the demand for predictive maintenance solutions is a key driver propelling the adoption of AI in manufacturing. Predictive maintenance powered by AI algorithms allows manufacturers to anticipate equipment failures and schedule maintenance activities proactively, minimizing unplanned downtime and optimizing asset utilization. Quality optimization is another critical aspect where AI is making significant inroads, helping manufacturers enhance product quality through real-time monitoring and analysis.

As the global manufacturing sector undergoes a digital transformation, AI is emerging as a pivotal technology, empowering manufacturers to stay competitive in a dynamic market. The ongoing advancements in AI, coupled with a growing awareness of its transformative potential, are fueling the upward trajectory of the AI in Manufacturing Market, offering companies a pathway to achieve operational excellence and sustainable growth.

Key Market Drivers

Operational Efficiency Enhancement

One of the primary drivers fueling the growth of the Global AI in Manufacturing Market is the relentless pursuit of operational efficiency by manufacturing enterprises. In an era characterized by intense competition and rapidly evolving consumer demands, manufacturers are increasingly turning to artificial intelligence (AI) to optimize their operational processes. AI technologies, including machine learning and advanced analytics, enable manufacturers to analyze vast datasets in real-time, extracting valuable insights that can inform and improve decision-making. By automating routine tasks, predictive maintenance, and quality control processes, AI empowers manufacturers to enhance efficiency, reduce production costs, and minimize errors. The implementation of AI-driven solutions not only accelerates production cycles but also ensures that resources are utilized more effectively, contributing to overall operational excellence.

Predictive Maintenance Revolution

The Global AI in Manufacturing Market is the paradigm shift towards predictive maintenance strategies. Traditional maintenance practices often lead to scheduled downtimes and can result in production losses. AI-driven predictive maintenance, however, leverages machine learning algorithms to analyze equipment performance data and predict potential failures before they occur. This proactive approach enables manufacturers to schedule maintenance activities precisely when needed, minimizing unplanned downtime and optimizing overall equipment effectiveness. The cost savings associated with reduced downtime, coupled with improved asset utilization, make predictive maintenance a compelling reason for manufacturers to integrate AI into their operations.

Quality Optimization through AI

Quality optimization stands out as a critical driver accelerating the adoption of AI in manufacturing. Maintaining and enhancing product quality is paramount for manufacturers seeking to meet stringent industry standards and customer expectations. AI technologies facilitate real-time monitoring and analysis of production processes, enabling manufacturers to identify and address quality issues promptly. Machine learning algorithms can learn from historical data, helping manufacturers predict and prevent defects, thereby reducing waste and ensuring consistent product quality. The ability to implement adaptive quality control measures positions AI as a transformative force in the manufacturing sector, where precision and consistency are non-negotiable.

Supply Chain Resilience and Flexibility

The evolving complexities of global supply chains have prompted manufacturers to embrace AI as a driver for enhancing resilience and flexibility. AI applications enable real-time monitoring and optimization of supply chain processes, from demand forecasting to inventory management. Machine learning algorithms can analyze vast datasets, identify patterns, and provide insights that facilitate better decision-making in supply chain operations. With the ability to adapt to changing market conditions and unforeseen disruptions, AI-equipped manufacturing systems contribute to the creation of more agile and responsive supply chains, ensuring that manufacturers can meet customer demands efficiently while minimizing supply chain risks.

Rising Investment in Industry 4.0 Initiatives

The growing recognition of Industry 4.0 as a transformative force in manufacturing has led to increased investments in AI technologies. Industry 4.0, characterized by the integration of digital technologies into the manufacturing process, relies heavily on AI for automation, data analytics, and connectivity. As manufacturers embark on digital transformation journeys, they are allocating significant resources to implement AI-driven solutions that align with Industry 4.0 principles. This strategic investment is driven by the understanding that AI is a cornerstone in unlocking the full potential of smart factories and interconnected manufacturing systems. The pursuit of competitiveness in the Industry 4.0 landscape is a compelling driver behind the rising adoption of AI in the global manufacturing sector.

Key Market Challenges

Data Integration and Standardization Challenges

A significant challenge facing the Global AI in Manufacturing Market is the intricacy of data integration and standardization across diverse manufacturing environments. Manufacturing operations generate vast amounts of data from various sources, including sensors, machines, and enterprise systems. However, this data often exists in silos, with different formats, structures, and levels of granularity. Integrating and standardizing this disparate data for effective AI utilization poses a formidable challenge. Manufacturers must invest in robust data integration solutions and establish standardized protocols to ensure seamless communication and interoperability between different data sources. Overcoming these challenges is essential for AI applications to derive meaningful insights, as the accuracy and reliability of AI algorithms depend on the quality and consistency of the data they analyze.

Skill Gaps and Workforce Training

The rapid evolution of AI technologies in manufacturing has exposed a significant challenge related to skill gaps and the need for workforce training. Integrating AI into manufacturing processes requires a skilled workforce capable of understanding, implementing, and maintaining AI-driven systems. Many manufacturers face challenges in finding and retaining professionals with the necessary expertise in AI, machine learning, and data analytics. Bridging this skill gap requires substantial investments in training programs and educational initiatives. Manufacturers need to cultivate a workforce that is not only proficient in using AI tools but also possesses the knowledge to interpret AI-generated insights and make informed decisions. Addressing this challenge is crucial for unlocking the full potential of AI in manufacturing and ensuring that organizations can effectively harness the benefits of these transformative technologies.

Security and Privacy Concerns

The integration of AI in manufacturing introduces a complex web of security and privacy concerns that pose a significant challenge to widespread adoption. As manufacturing systems become more interconnected and reliant on AI for critical functions, they become potential targets for cyber threats. Ensuring the security and privacy of sensitive data, intellectual property, and operational processes is paramount. Manufacturers must implement robust cybersecurity measures, including encryption, secure access controls, and continuous monitoring, to safeguard against cyberattacks. Additionally, the increasing connectivity of devices and systems within the Industrial Internet of Things (IIoT) raises concerns about data privacy. Striking a balance between leveraging the benefits of AI and safeguarding against cybersecurity threats is a persistent challenge that requires ongoing vigilance and investment.

High Implementation Costs and ROI Uncertainty

The upfront costs associated with implementing AI in manufacturing, including software development, hardware upgrades, and workforce training, present a considerable challenge for many organizations. While the long-term benefits of improved efficiency, predictive maintenance, and quality optimization are promising, manufacturers often face uncertainty regarding the return on investment (ROI) and the timeline for realizing these benefits. The complexity of AI implementation, coupled with the need for specialized expertise, can contribute to high initial costs. Manufacturers must carefully assess the potential ROI and develop clear implementation strategies to justify these investments. Overcoming this challenge involves establishing transparent benchmarks for success, monitoring key performance indicators, and continuously optimizing AI applications to ensure they deliver tangible value over time. Addressing these challenges is imperative for manufacturers to navigate the complexities of integrating AI into their operations successfully.

Key Market Trends

Evolution of Explainable AI in Manufacturing

A prominent trend shaping the Global AI in Manufacturing Market is the increasing focus on Explainable AI (XAI). As AI algorithms become more sophisticated and complex, there is a growing need for transparency and interpretability in decision-making processes, particularly in critical manufacturing operations. Explainable AI enables manufacturers to understand how AI systems arrive at specific conclusions or recommendations, providing insights into the factors influencing their decisions. This trend is crucial for gaining the trust of stakeholders, compliance with regulations, and fostering collaboration between AI systems and human operators. In the manufacturing context, where decisions can have significant operational and safety implications, the evolution of Explainable AI is poised to play a pivotal role in enhancing the adoption and acceptance of AI technologies across the industry.

Edge AI for Real-Time Decision-Making

The adoption of Edge AI is emerging as a key market trend in manufacturing, driven by the need for real-time decision-making capabilities. Traditional cloud-based AI systems often encounter latency issues, which can be a hindrance in time-sensitive manufacturing processes. Edge AI involves deploying AI algorithms directly on edge devices, such as sensors and machinery, enabling real-time data analysis and decision-making at the source. This trend enhances the responsiveness of manufacturing systems, improves overall operational efficiency, and reduces dependence on centralized cloud computing. As the industry embraces the era of Industry 4.0, where interconnected devices play a pivotal role, the integration of Edge AI is becoming increasingly prevalent as a strategic approach to address the demand for instant insights and actions in manufacturing environments.

AI-Driven Customization in Smart Manufacturing

Smart manufacturing is witnessing a trend towards AI-driven customization, catering to the rising demand for personalized and tailored products. AI algorithms analyze consumer preferences, historical data, and market trends to optimize production processes for greater flexibility and customization. Manufacturers are leveraging AI to dynamically adjust production parameters, such as product configurations and assembly processes, to meet individualized customer requirements. This trend not only enhances customer satisfaction but also allows manufacturers to respond quickly to changes in market demands. The integration of AI-driven customization aligns with the broader Industry 4.0 vision, where smart manufacturing systems are characterized by adaptability, responsiveness, and the ability to deliver products that precisely match diverse customer needs.Sustainable.

Manufacturing with AI

Sustainability has become a central theme in global manufacturing, and AI is playing a pivotal role in driving sustainable practices. AI applications are being employed to optimize energy consumption, reduce waste, and enhance resource efficiency in manufacturing processes. Predictive analytics powered by AI helps manufacturers anticipate equipment failures, preventing unnecessary resource usage and minimizing environmental impact. Additionally, AI-driven simulations and modeling enable manufacturers to assess the environmental footprint of different production scenarios, aiding in the design of more sustainable processes. As environmental consciousness continues to shape consumer and regulatory expectations, the trend of integrating AI for sustainable manufacturing practices is expected to gain further momentum across industries.

Collaborative Robotics and AI Integration

The convergence of AI and robotics, particularly collaborative robotics, is a notable trend in the Global AI in Manufacturing Market. Manufacturers are increasingly deploying AI-enhanced robots that can work alongside human operators in a collaborative and flexible manner. These robots leverage AI for tasks such as vision recognition, decision-making, and adaptive learning, allowing them to perform complex tasks with precision and efficiency. This trend enhances the overall productivity and agility of manufacturing operations while addressing safety concerns through the implementation of AI-driven sensors and real-time monitoring. The collaborative integration of AI and robotics is reshaping the manufacturing landscape, creating more adaptive and responsive production environments that leverage the strengths of both human workers and intelligent machines.

Segmental Insights

Offering Insights

The Global AI in Manufacturing Market witnessed the dominance of the software segment, which is anticipated to maintain its leading position throughout the forecast period. Software offerings in AI for manufacturing play a pivotal role in enabling advanced analytics, machine learning algorithms, and cognitive computing applications that drive operational efficiency and decision-making processes. The significance of AI software lies in its ability to analyze vast datasets generated by manufacturing operations, extract actionable insights, and facilitate predictive maintenance, quality optimization, and process automation. As manufacturers increasingly recognize the transformative potential of AI applications, the demand for sophisticated software solutions continues to surge. These software offerings empower manufacturers to implement AI-driven strategies without the need for substantial investments in new hardware infrastructure, providing scalability and flexibility in adapting to evolving manufacturing needs. Furthermore, continuous advancements in AI algorithms, coupled with the growing emphasis on Industry 4.0 initiatives, contribute to the sustained dominance of the software segment in the AI in Manufacturing Market. The software segment's dominance is indicative of the industry's focus on leveraging intelligent data analytics and machine learning capabilities to enhance production processes, minimize downtime, and optimize resource utilization, thereby ensuring a competitive edge in the dynamic landscape of global manufacturing. As manufacturers prioritize software-based AI solutions to unlock operational efficiencies and gain a strategic advantage, it is anticipated that the software segment will continue to be the driving force behind the growth and innovation in the AI in Manufacturing Market in the coming years.

TechnologyInsights

The Global AI in Manufacturing Market witnessed the dominance of the machine learning segment, which is poised to maintain its leading position throughout the forecast period. Machine learning technology has emerged as a cornerstone in transforming manufacturing processes by enabling systems to learn from data, identify patterns, and make informed decisions without explicit programming. This technology's dominance is attributed to its versatility and applicability across various manufacturing applications, such as predictive maintenance, quality control, and production optimization. Machine learning algorithms empower manufacturers to analyze vast datasets generated by sensors and devices on the factory floor, providing valuable insights that enhance efficiency and productivity. The ability of machine learning to adapt and improve performance over time aligns with the dynamic and evolving nature of manufacturing operations. While computer vision and natural language processing also contribute significantly to the AI in Manufacturing Market, the broad spectrum of applications and the continuous evolution of machine learning algorithms contribute to its sustained dominance. As manufacturing enterprises seek to unlock the full potential of AI technologies, the machine learning segment is expected to play a central role in shaping the industry's landscape, driving innovation, and delivering tangible business outcomes. With ongoing advancements in machine learning techniques and their integration into smart manufacturing processes, this technology segment is likely to remain at the forefront, steering the transformative impact of AI across the global manufacturing sector.

Application Insights

The Global AI in Manufacturing Market was dominated by the predictive maintenance and machinery inspection segment, and this dominance is anticipated to persist throughout the forecast period. Predictive maintenance, enabled by artificial intelligence (AI) applications, has emerged as a key driver for manufacturers seeking to enhance operational efficiency and minimize unplanned downtime. By leveraging advanced analytics and machine learning algorithms, manufacturers can predict potential equipment failures and schedule maintenance activities proactively, thereby optimizing asset performance and reducing production disruptions. The machinery inspection aspect of this segment involves AI-powered systems that analyze sensor data and visual information to monitor the condition of machinery in real-time, ensuring early detection of anomalies and potential issues. The emphasis on cost-effective and efficient maintenance strategies has fueled the demand for predictive maintenance and machinery inspection applications, positioning this segment as a cornerstone in the adoption of AI across the manufacturing sector. As manufacturers increasingly prioritize strategies for improving equipment reliability, reducing maintenance costs, and maximizing production uptime, the predictive maintenance and machinery inspection segment is expected to maintain its dominance, driving innovation and transformative changes in the global AI in Manufacturing Market. The proactive and data-driven approach offered by predictive maintenance aligns with the industry's goals of achieving operational excellence and underscores the critical role of AI applications in ensuring the reliability and performance of manufacturing machinery.

Regional Insights

Asia-Pacific emerged as the dominant region in the Global AI in Manufacturing Market, and this dominance is projected to persist throughout the forecast period. The Asia-Pacific region has witnessed rapid industrialization, coupled with substantial investments in emerging technologies, making it a key hub for the adoption of artificial intelligence (AI) in manufacturing. Countries such as China, Japan, and South Korea have been at the forefront of incorporating AI technologies into their manufacturing processes to enhance efficiency, productivity, and innovation. The extensive manufacturing activities in sectors such as automotive, electronics, and machinery, combined with government initiatives promoting Industry 4.0 and smart manufacturing, have fueled the demand for AI solutions. Additionally, the region benefits from a robust ecosystem of technology providers, research institutions, and a skilled workforce, contributing to the widespread integration of AI in manufacturing operations. As businesses in Asia-Pacific continue to prioritize digital transformation and automation, the region is expected to maintain its dominance in the global AI in Manufacturing Market. The ongoing commitment to technological advancements, large-scale industrial production, and a conducive business environment position Asia-Pacific as a driving force in shaping the future of AI adoption across the manufacturing landscape. The region's leadership in AI implementation underscores its strategic position as a key player in the global manufacturing industry, and its continued dominance is anticipated as organizations across various sectors leverage AI to stay competitive and address the challenges of modern manufacturing.

Key Market Players

Siemens AG

General Electric Company (GE)

International Business Machines Corporation (IBM)

SAP SE

Rockwell Automation, Inc.

Google LLC

NVIDIA Corporation.

Amazon.com, Inc.

Microsoft Corporation

Mitsubishi Electric Corporation.

Report Scope:

In this report, the Global AI in Manufacturing Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

AI in Manufacturing Market,By Offering:

Hardware
Services
Software

AI in Manufacturing Market,By Technology:

Computer Vision
Machine Learning
Natural Language Processing

AI in Manufacturing Market,By Application:

Process Control
Production Planning
Predictive Maintenance Machinery Inspection

AI in Manufacturing Market,By Industry:

Automotive
Medical Devices
Semiconductor Electronics

AI in Manufacturing Market, By Region:

North America
- United States
- Canada
- Mexico
Europe
- France
- United Kingdom
- Italy
- Germany
- Spain
- Belgium
Asia-Pacific
- China
- India
- Japan
- Australia
- South Korea
- Indonesia
- Vietnam
South America
- Brazil
- Argentina
- Colombia
- Chile
- Peru
Middle East Africa
- South Africa
- Saudi Arabia
- UAE
- Turkey
- Israel

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global AI in Manufacturing Market.

Available Customizations:

Global AI in Manufacturing market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

1.Product Overview

1.1.Market Definition
1.2.Scope of the Market
- 1.2.1.Markets Covered
- 1.2.2.Years Considered for Study
- 1.2.3.Key Market Segmentations

2.Research Methodology

2.1.Objective of the Study
2.2.Baseline Methodology
2.3.Formulation of the Scope
2.4.Assumptions and Limitations
2.5.Sources of Research
- 2.5.1.Secondary Research
- 2.5.2.Primary Research
2.6.Approach for the Market Study
- 2.6.1.The Bottom-Up Approach
- 2.6.2.The Top-Down Approach
2.7.Methodology Followed for Calculation of Market Size Market Shares
2.8.Forecasting Methodology
- 2.8.1.Data Triangulation Validation

3.Executive Summary

4.Impact of COVID-19 on Global AI in Manufacturing Market

5.Voice of Customer

6.Global AI in Manufacturing

7.Global AI in Manufacturing Market Outlook

7.1.Market Size Forecast
- 7.1.1.By Value
7.2.Market Share Forecast
- 7.2.1.By Offering (Hardware, Software, and Services)
- 7.2.2.By Technology (Computer Vision, Machine Learning, Natural Language Processing)
- 7.2.3.By Application (Process Control, Production Planning, Predictive Maintenance Machinery Inspection)
- 7.2.4.By Industry (Automotive, Medical Devices, Semiconductor Electronics)
- 7.2.5.By Region (North America, Europe, South America, Middle East Africa, Asia Pacific)
7.3.By Company (2023)
7.4.Market Map

8.North America AI in Manufacturing MarketOutlook

8.1.Market Size Forecast
- 8.1.1.By Value
8.2.Market Share Forecast
- 8.2.1.By Offering
- 8.2.2.By Technology
- 8.2.3.By Application
- 8.2.4.By Industry
- 8.2.5.By Country
8.3.North America: Country Analysis
- 8.3.1.United States AI in Manufacturing Market Outlook
  - 8.3.1.1.Market Size Forecast
    - 8.3.1.1.1.By Value
  - 8.3.1.2.Market Share Forecast
    - 8.3.1.2.1.By Offering
    - 8.3.1.2.2.By Technology
    - 8.3.1.2.3.By Application
    - 8.3.1.2.4.By Industry
- 8.3.2.Canada AI in Manufacturing Market Outlook
  - 8.3.2.1.Market Size Forecast
    - 8.3.2.1.1.By Value
  - 8.3.2.2.Market Share Forecast
    - 8.3.2.2.1.By Offering
    - 8.3.2.2.2.By Technology
    - 8.3.2.2.3.By Application
    - 8.3.2.2.4.By Industry
- 8.3.3.Mexico AI in Manufacturing Market Outlook
  - 8.3.3.1.Market Size Forecast
    - 8.3.3.1.1.By Value
  - 8.3.3.2.Market Share Forecast
    - 8.3.3.2.1.By Offering
    - 8.3.3.2.2.By Technology
    - 8.3.3.2.3.By Application
    - 8.3.3.2.4.By Industry

9.Europe AI in Manufacturing MarketOutlook

9.1.Market Size Forecast
- 9.1.1.By Value
9.2.Market Share Forecast
- 9.2.1.By Offering
- 9.2.2.By Technology
- 9.2.3.By Application
- 9.2.4.By Industry
- 9.2.5.By Country
9.3.Europe: Country Analysis
- 9.3.1.Germany AI in Manufacturing Market Outlook
  - 9.3.1.1.Market Size Forecast
    - 9.3.1.1.1.By Value
  - 9.3.1.2.Market Share Forecast
    - 9.3.1.2.1.By Offering
    - 9.3.1.2.2.By Technology
    - 9.3.1.2.3.By Application
    - 9.3.1.2.4.By Industry
- 9.3.2.France AI in Manufacturing Market Outlook
  - 9.3.2.1.Market Size Forecast
    - 9.3.2.1.1.By Value
  - 9.3.2.2.Market Share Forecast
    - 9.3.2.2.1.By Offering
    - 9.3.2.2.2.By Technology
    - 9.3.2.2.3.By Application
    - 9.3.2.2.4.By Industry
- 9.3.3.United Kingdom AI in Manufacturing Market Outlook
  - 9.3.3.1.Market Size Forecast
    - 9.3.3.1.1.By Value
  - 9.3.3.2.Market Share Forecast
    - 9.3.3.2.1.By Offering
    - 9.3.3.2.2.By Technology
    - 9.3.3.2.3.By Application
    - 9.3.3.2.4.By Industry
- 9.3.4.Italy AI in Manufacturing Market Outlook
  - 9.3.4.1.Market Size Forecast
    - 9.3.4.1.1.By Value
  - 9.3.4.2.Market Share Forecast
    - 9.3.4.2.1.By Offering
    - 9.3.4.2.2.By Technology
    - 9.3.4.2.3.By Application
    - 9.3.4.2.4.By Industry
- 9.3.5.Spain AI in Manufacturing Market Outlook
  - 9.3.5.1.Market Size Forecast
    - 9.3.5.1.1.By Value
  - 9.3.5.2.Market Share Forecast
    - 9.3.5.2.1.By Offering
    - 9.3.5.2.2.By Technology
    - 9.3.5.2.3.By Application
    - 9.3.5.2.4.By Industry
- 9.3.6.Belgium AI in Manufacturing Market Outlook
  - 9.3.6.1.Market Size Forecast
    - 9.3.6.1.1.By Value
  - 9.3.6.2.Market Share Forecast
    - 9.3.6.2.1.By Offering
    - 9.3.6.2.2.By Technology
    - 9.3.6.2.3.By Application
    - 9.3.6.2.4.By Industry

10.South America AI in Manufacturing Market Outlook

10.1.Market Size Forecast
- 10.1.1.By Value
10.2.Market Share Forecast
- 10.2.1.By Offering
- 10.2.2.By Technology
- 10.2.3.By Application
- 10.2.4.By Industry
- 10.2.5.By Country
10.3.South America: Country Analysis
- 10.3.1.Brazil AI in Manufacturing Market Outlook
  - 10.3.1.1.Market Size Forecast
    - 10.3.1.1.1.By Value
  - 10.3.1.2.Market Share Forecast
    - 10.3.1.2.1.By Offering
    - 10.3.1.2.2.By Technology
    - 10.3.1.2.3.By Application
    - 10.3.1.2.4.By Industry
- 10.3.2.Colombia AI in Manufacturing Market Outlook
  - 10.3.2.1.Market Size Forecast
    - 10.3.2.1.1.By Value
  - 10.3.2.2.Market Share Forecast
    - 10.3.2.2.1.By Offering
    - 10.3.2.2.2.By Technology
    - 10.3.2.2.3.By Application
    - 10.3.2.2.4.By Industry
- 10.3.3.Argentina AI in Manufacturing Market Outlook
  - 10.3.3.1.Market Size Forecast
    - 10.3.3.1.1.By Value
  - 10.3.3.2.Market Share Forecast
    - 10.3.3.2.1.By Offering
    - 10.3.3.2.2.By Technology
    - 10.3.3.2.3.By Application
    - 10.3.3.2.4.By Industry
- 10.3.4.Chile AI in Manufacturing Market Outlook
  - 10.3.4.1.Market Size Forecast
    - 10.3.4.1.1.By Value
  - 10.3.4.2.Market Share Forecast
    - 10.3.4.2.1.By Offering
    - 10.3.4.2.2.By Technology
    - 10.3.4.2.3.By Application
    - 10.3.4.2.4.By Industry
- 10.3.5.Peru AI in Manufacturing Market Outlook
  - 10.3.5.1.Market Size Forecast
    - 10.3.5.1.1.By Value
  - 10.3.5.2.Market Share Forecast
    - 10.3.5.2.1.By Offering
    - 10.3.5.2.2.By Technology
    - 10.3.5.2.3.By Application
    - 10.3.5.2.4.By Industry

11.Middle East Africa AI in Manufacturing MarketOutlook

11.1.Market Size Forecast
- 11.1.1.By Value
11.2.Market Share Forecast
- 11.2.1.By Offering
- 11.2.2.By Technology
- 11.2.3.By Application
- 11.2.4.By Industry
- 11.2.5.By Country
11.3.Middle East Africa: Country Analysis
- 11.3.1.Saudi Arabia AI in Manufacturing Market Outlook
  - 11.3.1.1.Market Size Forecast
    - 11.3.1.1.1.By Value
  - 11.3.1.2.Market Share Forecast
    - 11.3.1.2.1.By Offering
    - 11.3.1.2.2.By Technology
    - 11.3.1.2.3.By Application
    - 11.3.1.2.4.By Industry
- 11.3.2.UAE AI in Manufacturing Market Outlook
  - 11.3.2.1.Market Size Forecast
    - 11.3.2.1.1.By Value
  - 11.3.2.2.Market Share Forecast
    - 11.3.2.2.1.By Offering
    - 11.3.2.2.2.By Technology
    - 11.3.2.2.3.By Application
    - 11.3.2.2.4.By Industry
- 11.3.3.South Africa AI in Manufacturing Market Outlook
  - 11.3.3.1.Market Size Forecast
    - 11.3.3.1.1.By Value
  - 11.3.3.2.Market Share Forecast
    - 11.3.3.2.1.By Offering
    - 11.3.3.2.2.By Technology
    - 11.3.3.2.3.By Application
    - 11.3.3.2.4.By Industry
- 11.3.4.Turkey AI in Manufacturing Market Outlook
  - 11.3.4.1.Market Size Forecast
    - 11.3.4.1.1.By Value
  - 11.3.4.2.Market Share Forecast
    - 11.3.4.2.1.By Offering
    - 11.3.4.2.2.By Technology
    - 11.3.4.2.3.By Application
    - 11.3.4.2.4.By Industry
- 11.3.5.Israel AI in Manufacturing Market Outlook
  - 11.3.5.1.Market Size Forecast
    - 11.3.5.1.1.By Value
  - 11.3.5.2.Market Share Forecast
    - 11.3.5.2.1.By Offering
    - 11.3.5.2.2.By Technology
    - 11.3.5.2.3.By Application
    - 11.3.5.2.4.By Industry

12.Asia Pacific AI in Manufacturing Market Outlook

12.1.Market Size Forecast
- 12.1.1.By Value
12.2.Market Share Forecast
- 12.2.1.By Offering
- 12.2.2.By Technology
- 12.2.3.By Application
- 12.2.4.By Industry
- 12.2.5.By Country
12.3.Asia-Pacific: Country Analysis
- 12.3.1.China AI in Manufacturing Market Outlook
  - 12.3.1.1.Market Size Forecast
    - 12.3.1.1.1.By Value
  - 12.3.1.2.Market Share Forecast
    - 12.3.1.2.1.By Offering
    - 12.3.1.2.2.By Technology
    - 12.3.1.2.3.By Application
    - 12.3.1.2.4.By Industry
- 12.3.2.India AI in Manufacturing Market Outlook
  - 12.3.2.1.Market Size Forecast
    - 12.3.2.1.1.By Value
  - 12.3.2.2.Market Share Forecast
    - 12.3.2.2.1.By Offering
    - 12.3.2.2.2.By Technology
    - 12.3.2.2.3.By Application
    - 12.3.2.2.4.By Industry
- 12.3.3.Japan AI in Manufacturing Market Outlook
  - 12.3.3.1.Market Size Forecast
    - 12.3.3.1.1.By Value
  - 12.3.3.2.Market Share Forecast
    - 12.3.3.2.1.By Offering
    - 12.3.3.2.2.By Technology
    - 12.3.3.2.3.By Application
    - 12.3.3.2.4.By Industry
- 12.3.4.South Korea AI in Manufacturing Market Outlook
  - 12.3.4.1.Market Size Forecast
    - 12.3.4.1.1.By Value
  - 12.3.4.2.Market Share Forecast
    - 12.3.4.2.1.By Offering
    - 12.3.4.2.2.By Technology
    - 12.3.4.2.3.By Application
    - 12.3.4.2.4.By Industry
- 12.3.5.Australia AI in Manufacturing Market Outlook
  - 12.3.5.1.Market Size Forecast
    - 12.3.5.1.1.By Value
  - 12.3.5.2.Market Share Forecast
    - 12.3.5.2.1.By Offering
    - 12.3.5.2.2.By Technology
    - 12.3.5.2.3.By Application
    - 12.3.5.2.4.By Industry
- 12.3.6.Indonesia AI in Manufacturing Market Outlook
  - 12.3.6.1.Market Size Forecast
    - 12.3.6.1.1.By Value
  - 12.3.6.2.Market Share Forecast
    - 12.3.6.2.1.By Offering
    - 12.3.6.2.2.By Technology
    - 12.3.6.2.3.By Application
    - 12.3.6.2.4.By Industry
- 12.3.7.Vietnam AI in Manufacturing Market Outlook
  - 12.3.7.1.Market Size Forecast
    - 12.3.7.1.1.By Value
  - 12.3.7.2.Market Share Forecast
    - 12.3.7.2.1.By Offering
    - 12.3.7.2.2.By Technology
    - 12.3.7.2.3.By Application
    - 12.3.7.2.4.By Industry

13.Market Dynamics

13.1.Drivers
13.2.Challenges

14.Market Trends and Developments

15.Company Profiles

15.1.Siemens AG
- 15.1.1.Business Overview
- 15.1.2.Key Revenue and Financials
- 15.1.3.Recent Developments
- 15.1.4.Key Personnel/Key Contact Person
- 15.1.5.Key Product/Services Offered
15.2.General Electric Company (GE)
- 15.2.1.Business Overview
- 15.2.2.Key Revenue and Financials
- 15.2.3.Recent Developments
- 15.2.4.Key Personnel/Key Contact Person
- 15.2.5.Key Product/Services Offered
15.3.International Business Machines Corporation (IBM)
- 15.3.1.Business Overview
- 15.3.2.Key Revenue and Financials
- 15.3.3.Recent Developments
- 15.3.4.Key Personnel/Key Contact Person
- 15.3.5.Key Product/Services Offered
15.4.SAP SE
- 15.4.1.Business Overview
- 15.4.2.Key Revenue and Financials
- 15.4.3.Recent Developments
- 15.4.4.Key Personnel/Key Contact Person
- 15.4.5.Key Product/Services Offered
15.5.Rockwell Automation, Inc.
- 15.5.1.Business Overview
- 15.5.2.Key Revenue and Financials
- 15.5.3.Recent Developments
- 15.5.4.Key Personnel/Key Contact Person
- 15.5.5.Key Product/Services Offered
15.6.Google LLC
- 15.6.1.Business Overview
- 15.6.2.Key Revenue and Financials
- 15.6.3.Recent Developments
- 15.6.4.Key Personnel/Key Contact Person
- 15.6.5.Key Product/Services Offered
15.7.NVIDIA Corporation.
- 15.7.1.Business Overview
- 15.7.2.Key Revenue and Financials
- 15.7.3.Recent Developments
- 15.7.4.Key Personnel/Key Contact Person
- 15.7.5.Key Product/Services Offered
15.8.Amazon.com, Inc.
- 15.8.1.Business Overview
- 15.8.2.Key Revenue and Financials
- 15.8.3.Recent Developments
- 15.8.4.Key Personnel/Key Contact Person
- 15.8.5.Key Product/Services Offered
15.9.Microsoft Corporation
- 15.9.1.Business Overview
- 15.9.2.Key Revenue and Financials
- 15.9.3.Recent Developments
- 15.9.4.Key Personnel/Key Contact Person
- 15.9.5.Key Product/Services Offered
15.10.Mitsubishi Electric Corporation.
- 15.10.1.Business Overview
- 15.10.2.Key Revenue and Financials
- 15.10.3.Recent Developments
- 15.10.4.Key Personnel/Key Contact Person
- 15.10.5.Key Product/Services Offered