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市场调查报告书
商品编码
2021757

人工智慧市场对智慧工厂的预测（至2034年）：按组件、技术、应用、最终用户和地区分類的全球分析

AI in Smart Factories Market Forecasts to 2034 - Global Analysis By Component (Hardware, Software, and Services), Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，预计到 2026 年，全球智慧工厂人工智慧市场规模将达到 180 亿美元，到 2034 年将达到 1,650 亿美元，预测期内复合年增长率将达到 31.5%。

在智慧工厂中，人工智慧利用先进的演算法、机器学习和数据分析技术，实现製造流程的自动化、监控和最佳化。透过分析海量生产数据，可以实现即时决策、预测性维护、品管和高效的资源管理。人工智慧与工业系统的整合有助于提高生产效率、减少停机时间、提升产品质量，并实现灵活适应性强的运营，最终推动整个现代製造环境的效率提升与创新。

对预测性维护和营运效率的需求日益增长

传统维护方法常常导致设备意外故障和代价高昂的生产停机。人工智慧驱动的预测性维护持续分析感测器数据，侦测异常情况并预测机器故障，防患于未然。这种主动式策略最大限度地减少了非计划性停机时间，延长了机器寿命，并降低了维护成本。此外，人工智慧还能即时优化生产计画和资源分配，直接提高整体设备效率 (OEE)。随着製造商面临着在降低营运成本的同时最大化产量的巨大压力，人工智慧解决方案透过提供一条通往更精益、更快速响应和更高效的生产环境的清晰路径，正在加速全球市场成长。

资料整合实施成本高且复杂

在现有工厂中实施人工智慧 (AI) 需要对软体平台进行大量投资，此外还需要购置边缘设备、AI 晶片和工业感测器等先进硬体。对于中小型製造商而言，这些初始投资可能构成障碍。此外，许多老旧工厂缺乏标准化的资料基础设施，导致难以收集和整合来自不同机器和控制系统的资料。将 AI 与老旧的可程式逻辑控制器 (PLC) 和製造执行系统 (MES) 整合通常需要大规模的客製化和专业知识。这些技术和资金障碍正在减缓 AI 的广泛应用，尤其是在价格敏感型产业和发展中地区。

生成式人工智慧与数位双胞胎技术的发展

生成式人工智慧使製造商能够模拟无数生产场景，自动产生最佳化的工作流程，并设计出零缺陷零件。结合数位双胞胎（实体工厂的虚拟副本），人工智慧可以即时测试和检验流程变更，而不会中断实际生产。这种协同作用缩短了新产品推出时间，增强了品管，并加快了故障根本原因分析。此外，人工智慧驱动的数位双胞胎透过身临其境型模拟支援员工培训。随着云端运算和边缘基础设施的成熟，​​即使是中型工厂也将能够使用这些先进功能。率先采用生成式人工智慧的企业将在敏捷性、可自订性和成本效益方面获得显着的竞争优势。

网路安全漏洞和人才技能差距

人工智慧主导的智慧工厂依赖高度互联，这扩大了恶意攻击者的攻击面。一旦人工智慧模型遭到破坏，可能导致生产资料被窜改、产品出现缺陷，甚至对设备造成物理损坏。保护从资料收集到模型部署的整个人工智慧流程需要强大的加密技术、持续的监控以及抵御对抗性攻击的防御机制，这无疑增加了复杂性和成本。同时，人工智慧、资料科学和工业网路安全领域的人才严重短缺。弥合这一人才缺口需要对培训和招募进行大量投资。如果无法同时解决安全和人才方面的挑战，製造商可能会犹豫是否要全面采用人工智慧，从而限制其市场潜力。

新冠疫情的影响：

新冠疫情初期对智慧工厂人工智慧市场造成了衝击，导致生产线停工、供应链崩坏，製造商的资本投资也随之减少。然而，这场危机也成为自动化发展的强大催化剂。劳动力短缺和社交距离的要求迫使工厂加快采用人工智慧进行品质检测、物料输送和远端监控。製造商意识到，人工智慧驱动的韧性对于抵御未来的衝击至关重要。因此，疫情后时代，对智慧工厂人工智慧的投资激增，企业优先考虑自动化、预测分析和非接触式操作，以建构更敏捷、更具韧性的製造生态系统。

在预测期内，硬体产业预计将占据最大的市场份额。

预计在预测期内，硬体领域将占据最大的市场份额，因为实现人工智慧功能所需的实体基础设施至关重要。该领域包括人工智慧晶片和处理器、感测器和执行器、边缘人工智慧设备以及机器人控制设备。工业IoT的日益普及以及对边缘即时数据处理需求的成长，推动了对可直接安装在工厂车间的高效能运算硬体的需求。随着製造商使用支援人工智慧的传感器和控制器升级传统设备，对稳健、低延迟硬体的需求持续增长，这构成了所有智慧工厂部署的基础。

在预测期内，边缘人工智慧领域预计将呈现最高的复合年增长率。

在预测期内，边缘人工智慧领域预计将呈现最高的成长率。边缘人工智慧透过在工厂内部设备上本地处理数据，而不是将其发送到集中式云端伺服器，从而显着降低延迟和频宽占用。这对于机器人控制、即时缺陷检测和工人安全监控等对时间要求极高的应用至关重要。低功耗人工智慧晶片和耐环境边缘设备的进步，使得即使在严苛的工业环境中也能可靠运作。随着製造商对更快决策和更高资料隐私的需求不断增长，边缘人工智慧的普及应用正在加速，尤其是在汽车和电子产品生产线等对即时回应要求极高的领域。

市占率最大的地区：

在整个预测期内，北美预计将保持最大的市场份额。这主要得益于北美地区对工业4.0技术的早期应用、对工业自动化的巨额投资，以及领先的人工智慧硬体和软体供应商的存在。该地区大力推动製造业回流（製造业回流）和老旧基础设施的现代化改造，进一步加速了人工智慧的普及应用。此外，政府大力支持智慧製造的措施以及高技能技术人才的聚集，也巩固了北美的市场主导地位。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于中国、日本、印度和韩国等国的快速工业化进程以及政府主导的「智慧工厂」倡议。该地区是全球电子、半导体和汽车零件的製造地，对人工智慧驱动的效率有着巨大的需求。不断上涨的人事费用以及对更高精度和品质的追求正在推动自动化技术的应用。

免费客製化服务：

所有购买此报告的客户均可享受以下免费自订选项之一：

  • 企业概况
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域划分
    • 应客户要求，我们提供主要国家和地区的市场估算和预测，以及复合年增长率（註：需进行可行性检查）。
  • 竞争性标竿分析
    • 根据产品系列、地理覆盖范围和策略联盟对主要企业进行基准分析。

目录

第一章执行摘要

  • 市场概览及主要亮点
  • 驱动因素、挑战与机会
  • 竞争格局概述
  • 战略洞察与建议

第二章：研究框架

  • 研究目标和范围
  • 相关人员分析
  • 研究假设和限制
  • 调查方法

第三章 市场动态与趋势分析

  • 市场定义与结构
  • 主要市场驱动因素
  • 市场限制与挑战
  • 投资成长机会和重点领域
  • 产业威胁与风险评估
  • 技术与创新展望
  • 新兴市场/高成长市场
  • 监管和政策环境
  • 新冠疫情的影响及復苏前景

第四章：竞争环境与策略评估

  • 波特五力分析
    • 供应商的议价能力
    • 买方的议价能力
    • 替代品的威胁
    • 新进入者的威胁
    • 竞争公司之间的竞争
  • 主要企业市占率分析
  • 产品基准评效和效能比较

第五章：全球智慧工厂人工智慧市场：按组件划分

  • 硬体
    • 人工智慧晶片和处理器
    • 感测器和执行器
    • 边缘人工智慧设备
    • 机器人控制器
  • 软体
    • 人工智慧平台和框架
    • 自然语言处理（NLP）软体
    • 机器学习（ML）模型
    • 电脑视觉软体
  • 服务
    • 咨询和策略服务
    • 整合和配置服务
    • 託管服务
    • 培训和支援服务

第六章：全球智慧工厂人工智慧市场：按技术划分

  • 机器学习（ML）
  • 深度学习
  • 电脑视觉
  • 边缘人工智慧
  • 自然语言处理（NLP）
  • 强化学习
  • 人工智慧世代
  • 其他技术

第七章：全球智慧工厂人工智慧市场：按应用领域划分

  • 预测性保护
  • 品质检验和缺陷检测
  • 工人安全与监控
  • 生产计画和调度
  • 库存管理
  • 机器人与自动化
  • 能源管理
  • 供应链优化
  • 其他用途

第八章：全球智慧工厂人工智慧市场：按最终用户划分

  • 车
  • 食品/饮料
  • 电子和半导体
  • 航太/国防
  • 重型设备/金属加工
  • 消费品
  • 製药和生命科学
  • 其他最终用户

第九章：全球智慧工厂人工智慧市场：按地区划分

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 荷兰
    • 比利时
    • 瑞典
    • 瑞士
    • 波兰
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 印尼
    • 泰国
    • 马来西亚
    • 新加坡
    • 越南
    • 其他亚太国家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥伦比亚
    • 智利
    • 秘鲁
    • 其他南美国家
  • 世界其他地区（RoW）
    • 中东
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 卡达
      • 以色列
      • 其他中东国家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲国家

第十章 战略市场资讯

  • 工业价值网络和供应链评估
  • 空白区域和机会地图
  • 产品演进与市场生命週期分析
  • 通路、经销商和打入市场策略的评估

第十一章 产业趋势与策略倡议

  • 併购
  • 伙伴关係、联盟和合资企业
  • 新产品发布和认证
  • 扩大生产能力和投资
  • 其他策略倡议

第十二章：公司简介

  • Siemens AG
  • Mitsubishi Electric
  • ABB Ltd.
  • Honeywell International
  • IBM Corporation
  • C3.ai
  • Microsoft Corporation
  • Google LLC
  • NVIDIA Corporation
  • Amazon Web Services（AWS）
  • Intel Corporation
  • Bosch Rexroth
  • Rockwell Automation
  • General Electric（GE）
  • Schneider Electric
Product Code: SMRC35017

According to Stratistics MRC, the Global AI in Smart Factories Market is accounted for $18.0 billion in 2026 and is expected to reach $165.0 billion by 2034, growing at a CAGR of 31.5% during the forecast period. AI in smart factories is the use of advanced algorithms, machine learning, and data analytics to automate, monitor, and optimize manufacturing processes. It enables real-time decision-making, predictive maintenance, quality control, and efficient resource management by analyzing large volumes of production data. Integration of AI with industrial systems enhances productivity, reduces downtime, improves product quality, and supports flexible, adaptive operations, ultimately driving higher efficiency and innovation across modern manufacturing environments.

Market Dynamics:

Driver:

Rising demand for predictive maintenance and operational efficiency

Traditional maintenance approaches often lead to unexpected equipment failures and costly production stoppages. AI-powered predictive maintenance continuously analyzes sensor data to detect anomalies and predict machine failures before they occur. This proactive strategy minimizes unplanned downtime, extends machinery lifespan, and reduces maintenance costs. Furthermore, AI optimizes production schedules and resource allocation in real time, directly improving overall equipment effectiveness (OEE). As manufacturers face intense pressure to lower operational expenses while maximizing output, AI solutions offer a clear pathway to leaner, more responsive, and highly efficient production environments, accelerating market growth globally.

Restraint:

High implementation costs and data integration complexities

Deploying AI in existing factories requires substantial investment in advanced hardware such as edge devices, AI chips, and industrial sensors, along with software platforms. For small and medium-sized manufacturers, these upfront capital expenditures can be prohibitive. Additionally, many legacy factories lack standardized data infrastructure, making it difficult to collect and unify data from disparate machines and control systems. Integrating AI with older programmable logic controllers (PLCs) and manufacturing execution systems (MES) often demands extensive customization and specialized expertise. These technical and financial barriers slow down widespread adoption, particularly in price-sensitive industries and developing regions.

Opportunity:

Growth of generative AI and digital twin technologies

Generative AI enables manufacturers to simulate countless production scenarios, automatically generate optimized workflows, and design defect-free parts. When combined with digital twins virtual replicas of physical factories AI allows real-time testing and validation of process changes without disrupting actual production. This synergy reduces ramp-up time for new products, enhances quality control, and accelerates root cause analysis of failures. Additionally, AI-powered digital twins support worker training through immersive simulations. As cloud computing and edge infrastructure mature, even mid-sized factories can access these advanced capabilities. Early adopters leveraging generative AI will gain significant competitive advantages in agility, customization, and cost efficiency.

Threat:

Cybersecurity vulnerabilities and workforce skill gaps

AI-driven smart factories rely on hyper-connectivity, creating an expanded attack surface for malicious actors. Compromised AI models could lead to manipulated production data, defective outputs, or even physical damage to equipment. Protecting AI pipelines-from data collection to model deployment-requires robust encryption, continuous monitoring, and adversarial defense mechanisms, which add complexity and cost. Simultaneously, there is a critical shortage of workers skilled in AI, data science, and industrial cybersecurity. Bridging this gap demands significant investment in training and recruitment. Without addressing both security and talent challenges, manufacturers may hesitate to fully embrace AI, limiting market potential.

Covid-19 Impact:

The COVID-19 pandemic initially disrupted the AI in Smart Factories market due to halted production lines, supply chain breakdowns, and reduced capital spending by manufacturers. However, the crisis also acted as a powerful catalyst for automation. Widespread labor shortages and social distancing requirements forced factories to accelerate AI adoption for quality inspection, material handling, and remote monitoring. Manufacturers realized that AI-enabled resilience is essential to withstand future disruptions. As a result, post-pandemic investment in AI for smart factories has surged, with companies prioritizing automation, predictive analytics, and contactless operations to build more agile and robust manufacturing ecosystems.

The hardware segment is expected to be the largest during the forecast period

The hardware segment is expected to account for the largest market share during the forecast period, driven by the essential need for physical infrastructure to enable AI functionalities. This segment includes AI chips and processors, sensors and actuators, edge AI devices, and robotics controllers. The growing deployment of industrial IoT and real-time data processing at the edge requires high-performance computing hardware directly on the factory floor. As manufacturers upgrade legacy equipment with AI-capable sensors and controllers, demand for robust, low-latency hardware continues to rise, making it the foundation of any smart factory implementation.

The Edge AI segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the Edge AI segment is predicted to witness the highest growth rate. Edge AI processes data locally on factory devices rather than sending it to centralized cloud servers, significantly reducing latency and bandwidth usage. This is critical for time-sensitive applications such as robotic control, real-time defect detection, and worker safety monitoring. Advances in low-power AI chips and ruggedized edge devices enable reliable operation in harsh industrial environments. As manufacturers seek faster decision-making and enhanced data privacy, Edge AI adoption is accelerating, particularly in automotive and electronics production lines where split-second responses are essential.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by early adoption of Industry 4.0 technologies, significant investments in industrial automation, and the presence of leading AI hardware and software vendors. The region's strong focus on reshoring manufacturing and modernizing aging infrastructure further accelerates AI deployment. Additionally, robust government initiatives supporting smart manufacturing and a highly skilled technology workforce contribute to market dominance.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid industrialization, government-backed "smart factory" initiatives in China, Japan, India, and South Korea. The region is a global manufacturing hub for electronics, semiconductors, and automotive components, creating immense demand for AI-driven efficiency gains. Increasing labor costs and a push for higher precision and quality are driving automation adoption.

Key players in the market

Some of the key players in AI in Smart Factories Market include Siemens AG, Mitsubishi Electric, ABB Ltd., Honeywell International, IBM Corporation, C3.ai, Microsoft Corporation, Google LLC, NVIDIA Corporation, Amazon Web Services (AWS), Intel Corporation, Bosch Rexroth, Rockwell Automation, General Electric (GE), and Schneider Electric.

Key Developments:

In March 2026, Siemens and Rittal have entered a strategic partnership to jointly develop future-proof, sustainable solutions for more efficient data center power distribution in the IEC market. The standardized infrastructure is intended to accelerate the construction of high-performance data centers, minimize time-to-compute, and address the rapidly increasing power densities of AI applications.

In March 2026, Honeywell announced it has signed a groundbreaking supplier framework agreement with the U.S. Department of War (DoW) to rapidly increase the production of critical defense technologies. This agreement includes a $500 million multi-year investment to upgrade the company's production capacity.

Components Covered:

  • Hardware
  • Software
  • Services

Technologies Covered:

  • Machine Learning (ML)
  • Deep Learning
  • Computer Vision
  • Edge AI
  • Natural Language Processing (NLP)
  • Reinforcement Learning
  • Generative AI
  • Other Technologies

Applications Covered:

  • Predictive Maintenance
  • Quality Inspection & Defect Detection
  • Worker Safety & Monitoring
  • Production Planning & Scheduling
  • Inventory Management
  • Robotics & Automation
  • Energy Management
  • Supply Chain Optimization
  • Other Applications

End Users Covered:

  • Automotive
  • Food & Beverage
  • Electronics & Semiconductors
  • Aerospace & Defense
  • Heavy Machinery & Metal Fabrication
  • Consumer Goods
  • Pharmaceuticals & Life Sciences
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI in Smart Factories Market, By Component

  • 5.1 Hardware
    • 5.1.1 AI Chips & Processors
    • 5.1.2 Sensors & Actuators
    • 5.1.3 Edge AI Devices
    • 5.1.4 Robotics Controllers
  • 5.2 Software
    • 5.2.1 AI Platforms & Frameworks
    • 5.2.2 Natural Language Processing (NLP) Software
    • 5.2.3 Machine Learning (ML) Models
    • 5.2.4 Computer Vision Software
  • 5.3 Services
    • 5.3.1 Consulting & Strategy Services
    • 5.3.2 Integration & Deployment Services
    • 5.3.3 Managed Services
    • 5.3.4 Training & Support Services

6 Global AI in Smart Factories Market, By Technology

  • 6.1 Machine Learning (ML)
  • 6.2 Deep Learning
  • 6.3 Computer Vision
  • 6.4 Edge AI
  • 6.5 Natural Language Processing (NLP)
  • 6.6 Reinforcement Learning
  • 6.7 Generative AI
  • 6.8 Other Technologies

7 Global AI in Smart Factories Market, By Application

  • 7.1 Predictive Maintenance
  • 7.2 Quality Inspection & Defect Detection
  • 7.3 Worker Safety & Monitoring
  • 7.4 Production Planning & Scheduling
  • 7.5 Inventory Management
  • 7.6 Robotics & Automation
  • 7.7 Energy Management
  • 7.8 Supply Chain Optimization
  • 7.9 Other Applications

8 Global AI in Smart Factories Market, By End User

  • 8.1 Automotive
  • 8.2 Food & Beverage
  • 8.3 Electronics & Semiconductors
  • 8.4 Aerospace & Defense
  • 8.5 Heavy Machinery & Metal Fabrication
  • 8.6 Consumer Goods
  • 8.7 Pharmaceuticals & Life Sciences
  • 8.8 Other End Users

9 Global AI in Smart Factories Market, By Geography

  • 9.1 North America
    • 9.1.1 United States
    • 9.1.2 Canada
    • 9.1.3 Mexico
  • 9.2 Europe
    • 9.2.1 United Kingdom
    • 9.2.2 Germany
    • 9.2.3 France
    • 9.2.4 Italy
    • 9.2.5 Spain
    • 9.2.6 Netherlands
    • 9.2.7 Belgium
    • 9.2.8 Sweden
    • 9.2.9 Switzerland
    • 9.2.10 Poland
    • 9.2.11 Rest of Europe
  • 9.3 Asia Pacific
    • 9.3.1 China
    • 9.3.2 Japan
    • 9.3.3 India
    • 9.3.4 South Korea
    • 9.3.5 Australia
    • 9.3.6 Indonesia
    • 9.3.7 Thailand
    • 9.3.8 Malaysia
    • 9.3.9 Singapore
    • 9.3.10 Vietnam
    • 9.3.11 Rest of Asia Pacific
  • 9.4 South America
    • 9.4.1 Brazil
    • 9.4.2 Argentina
    • 9.4.3 Colombia
    • 9.4.4 Chile
    • 9.4.5 Peru
    • 9.4.6 Rest of South America
  • 9.5 Rest of the World (RoW)
    • 9.5.1 Middle East
      • 9.5.1.1 Saudi Arabia
      • 9.5.1.2 United Arab Emirates
      • 9.5.1.3 Qatar
      • 9.5.1.4 Israel
      • 9.5.1.5 Rest of Middle East
    • 9.5.2 Africa
      • 9.5.2.1 South Africa
      • 9.5.2.2 Egypt
      • 9.5.2.3 Morocco
      • 9.5.2.4 Rest of Africa

10 Strategic Market Intelligence

  • 10.1 Industry Value Network and Supply Chain Assessment
  • 10.2 White-Space and Opportunity Mapping
  • 10.3 Product Evolution and Market Life Cycle Analysis
  • 10.4 Channel, Distributor, and Go-to-Market Assessment

11 Industry Developments and Strategic Initiatives

  • 11.1 Mergers and Acquisitions
  • 11.2 Partnerships, Alliances, and Joint Ventures
  • 11.3 New Product Launches and Certifications
  • 11.4 Capacity Expansion and Investments
  • 11.5 Other Strategic Initiatives

12 Company Profiles

  • 12.1 Siemens AG
  • 12.2 Mitsubishi Electric
  • 12.3 ABB Ltd.
  • 12.4 Honeywell International
  • 12.5 IBM Corporation
  • 12.6 C3.ai
  • 12.7 Microsoft Corporation
  • 12.8 Google LLC
  • 12.9 NVIDIA Corporation
  • 12.10 Amazon Web Services (AWS)
  • 12.11 Intel Corporation
  • 12.12 Bosch Rexroth
  • 12.13 Rockwell Automation
  • 12.14 General Electric (GE)
  • 12.15 Schneider Electric

List of Tables

  • Table 1 Global AI in Smart Factories Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Smart Factories Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Smart Factories Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 4 Global AI in Smart Factories Market Outlook, By AI Chips & Processors (2023-2034) ($MN)
  • Table 5 Global AI in Smart Factories Market Outlook, By Sensors & Actuators (2023-2034) ($MN)
  • Table 6 Global AI in Smart Factories Market Outlook, By Edge AI Devices (2023-2034) ($MN)
  • Table 7 Global AI in Smart Factories Market Outlook, By Robotics Controllers (2023-2034) ($MN)
  • Table 8 Global AI in Smart Factories Market Outlook, By Software (2023-2034) ($MN)
  • Table 9 Global AI in Smart Factories Market Outlook, By AI Platforms & Frameworks (2023-2034) ($MN)
  • Table 10 Global AI in Smart Factories Market Outlook, By Natural Language Processing (NLP) Software (2023-2034) ($MN)
  • Table 11 Global AI in Smart Factories Market Outlook, By Machine Learning (ML) Models (2023-2034) ($MN)
  • Table 12 Global AI in Smart Factories Market Outlook, By Computer Vision Software (2023-2034) ($MN)
  • Table 13 Global AI in Smart Factories Market Outlook, By Services (2023-2034) ($MN)
  • Table 14 Global AI in Smart Factories Market Outlook, By Consulting & Strategy Services (2023-2034) ($MN)
  • Table 15 Global AI in Smart Factories Market Outlook, By Integration & Deployment Services (2023-2034) ($MN)
  • Table 16 Global AI in Smart Factories Market Outlook, By Managed Services (2023-2034) ($MN)
  • Table 17 Global AI in Smart Factories Market Outlook, By Training & Support Services (2023-2034) ($MN)
  • Table 18 Global AI in Smart Factories Market Outlook, By Technology (2023-2034) ($MN)
  • Table 19 Global AI in Smart Factories Market Outlook, By Machine Learning (ML) (2023-2034) ($MN)
  • Table 20 Global AI in Smart Factories Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 21 Global AI in Smart Factories Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 22 Global AI in Smart Factories Market Outlook, By Edge AI (2023-2034) ($MN)
  • Table 23 Global AI in Smart Factories Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 24 Global AI in Smart Factories Market Outlook, By Reinforcement Learning (2023-2034) ($MN)
  • Table 25 Global AI in Smart Factories Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 26 Global AI in Smart Factories Market Outlook, By Other Technologies (2023-2034) ($MN)
  • Table 27 Global AI in Smart Factories Market Outlook, By Application (2023-2034) ($MN)
  • Table 28 Global AI in Smart Factories Market Outlook, By Predictive Maintenance (2023-2034) ($MN)
  • Table 29 Global AI in Smart Factories Market Outlook, By Quality Inspection & Defect Detection (2023-2034) ($MN)
  • Table 30 Global AI in Smart Factories Market Outlook, By Worker Safety & Monitoring (2023-2034) ($MN)
  • Table 31 Global AI in Smart Factories Market Outlook, By Production Planning & Scheduling (2023-2034) ($MN)
  • Table 32 Global AI in Smart Factories Market Outlook, By Inventory Management (2023-2034) ($MN)
  • Table 33 Global AI in Smart Factories Market Outlook, By Robotics & Automation (2023-2034) ($MN)
  • Table 34 Global AI in Smart Factories Market Outlook, By Energy Management (2023-2034) ($MN)
  • Table 35 Global AI in Smart Factories Market Outlook, By Supply Chain Optimization (2023-2034) ($MN)
  • Table 36 Global AI in Smart Factories Market Outlook, By Other Applications (2023-2034) ($MN)
  • Table 37 Global AI in Smart Factories Market Outlook, By End User (2023-2034) ($MN)
  • Table 38 Global AI in Smart Factories Market Outlook, By Automotive (2023-2034) ($MN)
  • Table 39 Global AI in Smart Factories Market Outlook, By Food & Beverage (2023-2034) ($MN)
  • Table 40 Global AI in Smart Factories Market Outlook, By Electronics & Semiconductors (2023-2034) ($MN)
  • Table 41 Global AI in Smart Factories Market Outlook, By Aerospace & Defense (2023-2034) ($MN)
  • Table 42 Global AI in Smart Factories Market Outlook, By Heavy Machinery & Metal Fabrication (2023-2034) ($MN)
  • Table 43 Global AI in Smart Factories Market Outlook, By Consumer Goods (2023-2034) ($MN)
  • Table 44 Global AI in Smart Factories Market Outlook, By Pharmaceuticals & Life Sciences (2023-2034) ($MN)
  • Table 45 Global AI in Smart Factories Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.