查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
2021757

人工智慧市場對智慧工廠的預測（至2034年）：按組件、技術、應用、最終用戶和地區分類的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球智慧工廠人工智慧市場規模將達到 180 億美元，到 2034 年將達到 1,650 億美元，預測期內複合年成長率將達到 31.5%。

在智慧工廠中，人工智慧利用先進的演算法、機器學習和數據分析技術，實現製造流程的自動化、監控和最佳化。透過分析海量生產數據，可以實現即時決策、預測性維護、品管和高效的資源管理。人工智慧與工業系統的整合有助於提高生產效率、減少停機時間、提升產品質量，並實現靈活適應性強的運營，最終推動整個現代製造環境的效率提升與創新。

對預測性維護和營運效率的需求日益成長

傳統維護方法常常導致設備意外故障和代價高昂的生產停機。人工智慧驅動的預測性維護持續分析感測器數據，偵測異常情況並預測機器故障，防患於未然。這種主動式策略最大限度地減少了非計劃性停機時間，延長了機器壽命，並降低了維護成本。此外，人工智慧還能即時最佳化生產計畫和資源分配，直接提高整體設備效率 (OEE)。隨著製造商面臨著在降低營運成本的同時最大化產量的巨大壓力，人工智慧解決方案透過提供一條通往更精益、更快速響應和更高效的生產環境的清晰路徑，正在加速全球市場成長。

資料整合實施成本高且複雜

在現有工廠中實施人工智慧 (AI) 需要對軟體平台進行大量投資，此外還需要購置邊緣設備、AI 晶片和工業感測器等先進硬體。對於中小型製造商而言，這些初始投資可能構成障礙。此外，許多老舊工廠缺乏標準化的資料基礎設施，導致難以收集和整合來自不同機器和控制系統的資料。將 AI 與老舊的可程式邏輯控制器 (PLC) 和製造執行系統 (MES) 整合通常需要大規模的客製化和專業知識。這些技術和資金障礙正在減緩 AI 的廣泛應用，尤其是在價格敏感型產業和發展中地區。

生成式人工智慧與數位雙胞胎技術的發展

生成式人工智慧使製造商能夠模擬無數生產場景，自動產生最佳化的工作流程，並設計出零缺陷零件。結合數位雙胞胎（實體工廠的虛擬副本），人工智慧可以即時測試和檢驗流程變更，而不會中斷實際生產。這種協同作用縮短了新產品推出時間，增強了品管，並加快了故障根本原因分析。此外，人工智慧驅動的數位雙胞胎透過身臨其境型模擬支援員工培訓。隨著雲端運算和邊緣基礎設施的成熟，​​即使是中型工廠也將能夠使用這些先進功能。率先採用生成式人工智慧的企業將在敏捷性、可自訂性和成本效益方面獲得顯著的競爭優勢。

網路安全漏洞和人才技能差距

人工智慧主導的智慧工廠依賴高度互聯，這擴大了惡意攻擊者的攻擊面。一旦人工智慧模型遭到破壞，可能導致生產資料被竄改、產品出現缺陷，甚至對設備造成物理損壞。保護從資料收集到模型部署的整個人工智慧流程需要強大的加密技術、持續的監控以及抵禦對抗性攻擊的防禦機制，這無疑增加了複雜性和成本。同時，人工智慧、資料科學和工業網路安全領域的人才嚴重短缺。彌合這一人才缺口需要對培訓和招募進行大量投資。如果無法同時解決安全和人才方面的挑戰，製造商可能會猶豫是否要全面採用人工智慧，從而限制其市場潛力。

新冠疫情的影響：

新冠疫情初期對智慧工廠人工智慧市場造成了衝擊，導致生產線停工、供應鏈崩壞，製造商的資本投資也隨之減少。然而，這場危機也成為自動化發展的強大催化劑。勞動力短缺和社交距離的要求迫使工廠加快採用人工智慧進行品質檢測、物料輸送和遠端監控。製造商意識到，人工智慧驅動的韌性對於抵禦未來的衝擊至關重要。因此，疫情後時代，對智慧工廠人工智慧的投資激增，企業優先考慮自動化、預測分析和非接觸式操作，以建構更敏捷、更具韌性的製造生態系統。

在預測期內，硬體產業預計將佔據最大的市場佔有率。

預計在預測期內，硬體領域將佔據最大的市場佔有率，因為實現人工智慧功能所需的實體基礎設施至關重要。該領域包括人工智慧晶片和處理器、感測器和執行器、邊緣人工智慧設備以及機器人控制設備。工業IoT的日益普及以及對邊緣即時數據處理需求的成長，推動了對可直接安裝在工廠車間的高效能運算硬體的需求。隨著製造商使用支援人工智慧的感測器和控制器升級傳統設備，對穩健、低延遲硬體的需求持續成長，這構成了所有智慧工廠部署的基礎。

在預測期內，邊緣人工智慧領域預計將呈現最高的複合年成長率。

在預測期內，邊緣人工智慧領域預計將呈現最高的成長率。邊緣人工智慧透過在工廠內部設備上本地處理數據，而不是將其發送到集中式雲端伺服器，從而顯著降低延遲和頻寬佔用。這對於機器人控制、即時缺陷檢測和工人安全監控等對時間要求極高的應用至關重要。低功耗人工智慧晶片和耐環境邊緣設備的進步，使得即使在嚴苛的工業環境中也能可靠運作。隨著製造商對更快決策和更高資料隱私的需求不斷成長，邊緣人工智慧的普及應用正在加速，尤其是在汽車和電子產品生產線等對即時回應要求極高的領域。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率。這主要得益於北美地區對工業4.0技術的早期應用、對工業自動化的巨額投資，以及領先的人工智慧硬體和軟體供應商的存在。該地區大力推動製造業回流（製造業回流）和老舊基礎設施的現代化改造，進一步加速了人工智慧的普及應用。此外，政府大力支持智慧製造的措施以及高技能技術人才的聚集，也鞏固了北美的市場主導地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於中國、日本、印度和韓國等國的快速工業化進程以及政府主導的「智慧工廠」舉措。該地區是全球電子、半導體和汽車零件的製造地，對人工智慧驅動的效率有著巨大的需求。不斷上漲的人事費用以及對更高精度和品質的追求正在推動自動化技術的應用。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域分類
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球智慧工廠人工智慧市場：按組件分類

  • 硬體
    • 人工智慧晶片和處理器
    • 感測器和執行器
    • 邊緣人工智慧設備
    • 機器人控制器
  • 軟體
    • 人工智慧平台和框架
    • 自然語言處理（NLP）軟體
    • 機器學習（ML）模型
    • 電腦視覺軟體
  • 服務
    • 諮詢和策略服務
    • 整合和配置服務
    • 託管服務
    • 培訓和支援服務

第6章：全球智慧工廠人工智慧市場：按技術分類

  • 機器學習（ML）
  • 深度學習
  • 電腦視覺
  • 邊緣人工智慧
  • 自然語言處理（NLP）
  • 強化學習
  • 人工智慧世代
  • 其他技術

第7章：全球智慧工廠人工智慧市場：按應用領域分類

  • 預測性保護
  • 品質檢驗和缺陷檢測
  • 工人安全與監控
  • 生產計畫和調度
  • 庫存管理
  • 機器人與自動化
  • 能源管理
  • 供應鏈最佳化
  • 其他用途

第8章：全球智慧工廠人工智慧市場：按最終用戶分類

  • 車
  • 食品/飲料
  • 電子和半導體
  • 航太/國防
  • 重型設備/金屬加工
  • 消費品
  • 製藥和生命科學
  • 其他最終用戶

第9章：全球智慧工廠人工智慧市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第10章 戰略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第11章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第12章：公司簡介

  • 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.