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市場調查報告書
商品編碼
2021750

2034年製造業人工智慧市場預測：按交付方式、技術、部署方式、應用、最終用戶和地區分類的全球分析

AI in Manufacturing Market Forecasts to 2034 - Global Analysis By Offering (Hardware, Software, and Services), Technology, Deployment Mode, 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，全球製造業人工智慧市場規模將達到 98.5 億美元，到 2034 年將達到 1,288 億美元，預測期內複合年成長率將達到 37.9%。

人工智慧在製造業中應用先進的演算法、機器學習和數據分析技術，以最佳化生產流程、提高效率並增強決策能力。這使得即時監控、預測性維護、品管和複雜任務的自動化成為可能。透過分析來自機器和系統的大量數據，人工智慧幫助製造商減少停機時間、最大限度地減少錯誤並提高生產效率。總而言之，人工智慧推動創新和卓越運營，同時支援更智慧、更靈活、更經濟高效的製造營運。

製造業對營運效率和成本降低的需求日益成長。

製造商面臨著在保持高品質和高產量的同時降低生產成本的持續壓力。人工智慧能夠實現即時流程最佳化、預測性維護和智慧自動化，從而顯著減少機器停機時間、缺陷率和能源消耗。透過以數據驅動的主動決策取代被動維護，人工智慧最大限度地減少了代價高昂的營運停機時間，並延長了設備的使用壽命。人工智慧驅動的品質檢測系統還能減少返工和保固索賠。在全球競爭日益激烈和利潤率不斷下降的背景下，製造商正擴大採用人工智慧來簡化營運、提高資產利用率，並打造更精簡、更具成本效益的生產環境。

初始投資高且整合複雜

在製造業中實施人工智慧解決方案需要前期對感測器、邊緣設備、軟體平台和熟練人員進行大量投資。許多傳統生產設施缺乏必要的資料基礎設施和互通性標準，導致整合成本高且耗時。對老舊設備進行人工智慧感測器改造和連接通常會中斷生產。此外，缺乏具備製造業專業知識的資料科學家和人工智慧工程師也阻礙了人工智慧的普及應用。這些障礙對中小企業而言尤其嚴峻。由於缺乏明確的短期投資報酬率和內部技術專長，許多製造商對全面實施人工智慧持謹慎態度。

智慧工廠數位雙胞胎技術的擴展

工業4.0數位雙胞胎生態系統的興起，為人工智慧在製造業的應用創造了巨大的機會。數位雙胞胎是實體生產系統的虛擬副本，能夠持續產生資料流，供人工智慧模型分析，從而模擬、預測和最佳化實際生產營運。製造商正日益投資於完全互聯的智慧工廠，在這些工廠中，人工智慧統籌從原料交付到最終組裝的每一個環節。這種整合實現了封閉回路型控制系統，能夠即時進行自我修正。隨著雲端運算和5G連接的日益普及，人工智慧驅動的數位雙胞胎將帶來更高水準的敏捷性、可自訂性和韌性。

互聯工廠中的資料隱私和網路安全風險

人工智慧主導的製造業高度依賴互聯設備、雲端平台和即時數據共用，擴大了網路攻擊的範圍。人工智慧控制系統一旦遭到破壞，可能導致生產參數被竄改、品質檢查中斷或專有設計被竊取。惡意攻擊者可以將虛假資料注入機器學習模型，導致預測不準確和營運決策風險過高。IT安全資源有限的中小型製造商尤其容易受到攻擊。確保端對端加密、強大的存取控制和持續的威脅監控至關重要，但這會增加成本和複雜性。網路韌性仍然是一項重大挑戰。

新冠疫情的影響：

新冠疫情透過封鎖、勞動力短缺和供應鏈崩壞，對全球製造業造成了嚴重衝擊。然而，疫情也加速了數位轉型，製造商紛紛尋求非接觸式營運和更強的韌性。人工智慧驅動的預測性維護和自動化品質檢測減少了對現場人員的需求。社交距離的規定促進了人工智慧機器人和遠端監控解決方案的應用。這場危機暴露了僵化、勞力密集生產線的弊端，並促使企業對人工智慧進行長期投資，以提高供應鏈可視性和實現自適應製造。因此，疫情起到了催化劑的作用，顯示人工智慧對於保護製造業免受未來類似衝擊至關重要。

在預測期內，硬體領域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。這主要源於對工業機器人、物聯網感測器、處理器和邊緣設備等實體組件的根本性需求，這些組件用於收集和處理製造數據。這些硬體元素構成了任何人工智慧部署的基礎，能夠實現即時監控、自動化和控制。隨著工廠投資建造新的生產線並維修現有設備，對穩健、高性能硬體的需求持續成長。

在預測期內，電子和半導體產業預計將呈現最高的複合年成長率。

在預測期內，由於製造更小、更密集、更複雜且零缺陷晶片的壓力日益增大，電子和半導體產業預計將呈現最高的成長率。傳統的檢測方法難以在高速生產線上檢測到微小的缺陷。人工智慧驅動的電腦視覺和機器學習演算法能夠實現晶圓缺陷的即時檢測、微影術最佳化和良率預測。透過識別奈米級的異常情況，人工智慧在最先進的半導體製造工廠中正變得至關重要，因為它能夠減少漏檢、提高生產效率並減少代價高昂的返工。

市佔率最大的地區：

在預測期內，亞太地區預計將佔據最大的市場佔有率。這主要得益於快速的工業化進程、中國、印度、日本和韓國政府主導的數位化製造項目，以及電子和半導體生產的擴張。該地區集中了大量出口導向工廠，而人工智慧對於提升產品品質和效率至關重要。對5G基礎設施投資的增加以及價格親民的物聯網設備的普及降低了進入門檻。隨著人事費用的上升，製造商越來越依賴人工智慧驅動的自動化來保持全球競爭力，這進一步加速了市場成長。

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。這主要得益於快速的工業化進程、中國、印度、日本和韓國政府主導的智慧工廠計劃，以及該地區在電子和半導體生產領域的持續領先地位。人事費用的上升推動了自動化技術的應用，而5G基礎設施的擴展和價格親民的物聯網感測器則促進了人工智慧的普及。此外，主要製造地的存在以及對工業4.0技術不斷成長的投資，使亞太地區成為製造業人工智慧成長最快的市場。

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：全球製造業人工智慧市場：按產品/服務分類

  • 硬體
    • 感應器
    • 工業機器人
    • 處理器和邊緣設備
    • 物聯網設備
  • 軟體
    • 機器學習軟體
    • 數據分析平台
    • 品管軟體
    • 供應鏈管理軟體
  • 服務
    • 諮詢服務
    • 系統整合與部署
    • 培訓和支持
    • 託管服務

第6章：全球製造業人工智慧市場：按技術分類

  • 機器學習（ML）
  • 電腦視覺
  • 自然語言處理（NLP）
  • 情境感知計算

第7章：全球製造業人工智慧市場：依部署模式分類

  • 基於雲端的
  • 現場
  • 混合

第8章：全球製造業人工智慧市場：按應用領域分類

  • 預測性維護和機械檢查
  • 品管和檢驗
  • 生產計畫與最佳化
  • 供應鏈和庫存管理
  • 工業機器人與自動化
  • 物料運輸
  • 製造業網路安全
  • 現場服務

第9章：全球製造業人工智慧市場：按最終用戶分類

  • 車
  • 電子和半導體
  • 製藥
  • 重型設備/金屬製造
  • 食品/飲料
  • 能源與電力
  • 其他最終用戶

第10章：全球製造業人工智慧市場：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11章 策略市場資訊

  • 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12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13章：公司簡介

  • Siemens AG
  • General Electric Company
  • International Business Machines Corporation（IBM）
  • NVIDIA Corporation
  • Intel Corporation
  • Microsoft Corporation
  • Amazon Web Services, Inc.
  • Alphabet Inc.（Google LLC）
  • SAP SE
  • Oracle Corporation
  • Rockwell Automation, Inc.
  • Cisco Systems, Inc.
  • Mitsubishi Electric Corporation
  • SparkCognition, Inc.
  • Sight Machine, Inc.
Product Code: SMRC35015

According to Stratistics MRC, the Global AI in Manufacturing Market is accounted for $9.85 billion in 2026 and is expected to reach $128.8 billion by 2034, growing at a CAGR of 37.9% during the forecast period. AI in manufacturing is the application of advanced algorithms, machine learning, and data analytics to optimize production processes, enhance efficiency, and improve decision-making. It enables real-time monitoring, predictive maintenance, quality control, and automation of complex tasks. By analyzing large volumes of data from machines and systems, AI helps manufacturers reduce downtime, minimize errors, and increase productivity. Overall, it supports smarter, more flexible and cost-effective manufacturing operations while driving innovation and operational excellence.

Market Dynamics:

Driver:

Rising need for operational efficiency and cost reduction in manufacturing

Manufacturers face persistent pressure to lower production costs while maintaining high quality and output levels. AI enables real-time process optimization, predictive maintenance, and intelligent automation, which significantly reduce machine downtime, scrap rates, and energy consumption. By replacing reactive maintenance with proactive, data-driven decisions, AI minimizes costly disruptions and extends equipment life. AI-driven quality inspection systems also reduce rework and warranty claims. As global competition intensifies and profit margins shrink, manufacturers are increasingly adopting AI to streamline operations, improve asset utilization, and achieve leaner, more cost-effective production environments.

Restraint:

High initial investment and integration complexity

Deploying AI solutions in manufacturing requires substantial upfront capital for sensors, edge devices, software platforms, and skilled personnel. Many legacy production facilities lack the necessary data infrastructure and interoperability standards, making integration costly and time-consuming. Retrofitting older machinery with AI-capable sensors and connectivity often involves significant production disruptions. Additionally, the shortage of data scientists and AI engineers with manufacturing domain knowledge limits adoption. Small and medium-sized enterprises, in particular, find these barriers challenging. Without clear short-term ROI or internal technical expertise, many manufacturers hesitate to commit to full-scale AI implementation.

Opportunity:

Expansion of smart factories and digital twin technology

The rise of Industry 4.0 and digital twin ecosystems creates a powerful opportunity for AI in manufacturing. Digital twins virtual replicas of physical production systems-generate continuous data streams that AI models can analyze to simulate, predict, and optimize real-world operations. Manufacturers are increasingly investing in fully connected smart factories where AI orchestrates everything from raw material intake to final assembly. This convergence allows for closed-loop control systems that self-correct in real time. As cloud computing and 5G connectivity become more accessible, AI-driven digital twins will enable new levels of agility, customization, and resilience.

Threat:

Data privacy and cybersecurity risks in connected factories

AI-driven manufacturing relies heavily on interconnected devices, cloud platforms, and real-time data sharing, which expands the cyberattack surface. A breach in an AI control system could lead to manipulated production parameters, sabotage of quality checks, or theft of proprietary designs. Malicious actors might inject false data into machine learning models, causing incorrect predictions or dangerous operational decisions. Small and medium manufacturers with limited IT security resources are especially vulnerable. Ensuring end-to-end encryption, robust access controls, and continuous threat monitoring is essential but adds cost and complexity. Cyber resilience remains a critical challenge.

Covid-19 Impact:

The COVID-19 pandemic severely disrupted global manufacturing through lockdowns, labor shortages, and supply chain breakdowns. However, it also accelerated digital transformation as manufacturers sought contactless operations and greater resilience. AI-powered predictive maintenance and automated quality inspection reduced the need for on-site personnel. Social distancing rules drove adoption of AI-driven robotics and remote monitoring solutions. The crisis exposed weaknesses in rigid, labor-intensive production lines, prompting long-term investments in AI for supply chain visibility and adaptive manufacturing. As a result, the pandemic acted as a catalyst, positioning AI as essential for future-proofing manufacturing against similar disruptions.

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 fundamental need for physical components such as industrial robots, IoT sensors, processors, and edge devices that collect and act upon manufacturing data. These hardware elements form the backbone of any AI deployment, enabling real-time monitoring, automation, and control. As factories invest in new production lines and retrofit legacy equipment, demand for robust, high-performance hardware continues to grow.

The electronics & semiconductor segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the electronics & semiconductor segment is predicted to witness the highest growth rate, due to increasing pressure to manufacture smaller, denser, and more complex chips with zero defects. Traditional inspection methods struggle to detect microscopic flaws in high-speed production lines. AI-powered computer vision and machine learning algorithms enable real-time wafer defect detection, lithography optimization, and yield prediction. By identifying anomalies at nanoscale levels, AI reduces false rejects, improves production throughput, and lowers costly rework, making it indispensable for advanced semiconductor fabrication facilities.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, fueled by rapid industrialization, government-backed digital manufacturing programs in China, India, Japan, and South Korea, and the expansion of electronics and semiconductor production. The region's large concentration of export-oriented factories seeks AI to improve quality and efficiency. Growing investments in 5G infrastructure and affordable IoT devices lower entry barriers. As labor costs rise, manufacturers increasingly turn to AI-driven automation to maintain global competitiveness, accelerating market growth.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, rapid industrialization, government-backed smart factory initiatives in China, India, Japan, and South Korea, and the region's dominance in electronics and semiconductor production. Increasing labor costs are driving automation adoption, while expanding 5G infrastructure and affordable IoT sensors enable AI deployment. Additionally, the presence of major manufacturing hubs and rising investments in Industry 4.0 technologies position Asia Pacific as the fastest-growing market for AI in manufacturing.

Key players in the market

Some of the key players in AI in Manufacturing Market include Siemens AG, General Electric Company, International Business Machines Corporation (IBM), NVIDIA Corporation, Intel Corporation, Microsoft Corporation, Amazon Web Services, Inc., Alphabet Inc. (Google LLC), SAP SE, Oracle Corporation, Rockwell Automation, Inc., Cisco Systems, Inc., Mitsubishi Electric Corporation, SparkCognition, Inc., and Sight Machine, Inc.

Key Developments:

In March 2026, NVIDIA and Marvell Technology, Inc. announced a strategic partnership to connect Marvell to the NVIDIA AI factory and AI-RAN ecosystem through NVIDIA NVLink Fusion(TM), offering customers building on NVIDIA architectures greater choice and flexibility in developing next-generation infrastructure. The companies will also collaborate on silicon photonics technology.

In March 2026, Oracle announced the latest updates to Oracle AI Agent Studio for Fusion Applications, a complete development platform for building, connecting, and running AI automation and agentic applications. The latest updates to Oracle AI Agent Studio include a new agentic applications builder as well as new capabilities that support workflow orchestration, content intelligence, contextual memory, and ROI measurement.

Offerings Covered:

  • Hardware
  • Software
  • Services

Technologies Covered:

  • Machine Learning (ML)
  • Computer Vision
  • Natural Language Processing (NLP)
  • Context-Aware Computing

Deployment Modes Covered:

  • Cloud-Based
  • On-Premise
  • Hybrid

Applications Covered:

  • Predictive Maintenance & Machinery Inspection
  • Quality Control & Inspection
  • Production Planning & Optimization
  • Supply Chain & Inventory Management
  • Industrial Robotics & Automation
  • Material Movement
  • Cybersecurity in Manufacturing
  • Field Services

End Users Covered:

  • Automotive
  • Electronics & Semiconductor
  • Pharmaceuticals
  • Heavy Machinery & Metal Manufacturing
  • Food & Beverage
  • Energy & Power
  • 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 Manufacturing Market, By Offering

  • 5.1 Hardware
    • 5.1.1 Sensors
    • 5.1.2 Industrial Robots
    • 5.1.3 Processors & Edge Devices
    • 5.1.4 IoT Devices
  • 5.2 Software
    • 5.2.1 Machine Learning Software
    • 5.2.2 Data Analytics Platforms
    • 5.2.3 Quality Control Software
    • 5.2.4 Supply Chain Management Software
  • 5.3 Services
    • 5.3.1 Consulting Services
    • 5.3.2 System Integration & Deployment
    • 5.3.3 Training & Support
    • 5.3.4 Managed Services

6 Global AI in Manufacturing Market, By Technology

  • 6.1 Machine Learning (ML)
  • 6.2 Computer Vision
  • 6.3 Natural Language Processing (NLP)
  • 6.4 Context-Aware Computing

7 Global AI in Manufacturing Market, By Deployment Mode

  • 7.1 Cloud-Based
  • 7.2 On-Premise
  • 7.3 Hybrid

8 Global AI in Manufacturing Market, By Application

  • 8.1 Predictive Maintenance & Machinery Inspection
  • 8.2 Quality Control & Inspection
  • 8.3 Production Planning & Optimization
  • 8.4 Supply Chain & Inventory Management
  • 8.5 Industrial Robotics & Automation
  • 8.6 Material Movement
  • 8.7 Cybersecurity in Manufacturing
  • 8.8 Field Services

9 Global AI in Manufacturing Market, By End User

  • 9.1 Automotive
  • 9.2 Electronics & Semiconductor
  • 9.3 Pharmaceuticals
  • 9.4 Heavy Machinery & Metal Manufacturing
  • 9.5 Food & Beverage
  • 9.6 Energy & Power
  • 9.7 Other End Users

10 Global AI in Manufacturing Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 Siemens AG
  • 13.2 General Electric Company
  • 13.3 International Business Machines Corporation (IBM)
  • 13.4 NVIDIA Corporation
  • 13.5 Intel Corporation
  • 13.6 Microsoft Corporation
  • 13.7 Amazon Web Services, Inc.
  • 13.8 Alphabet Inc. (Google LLC)
  • 13.9 SAP SE
  • 13.10 Oracle Corporation
  • 13.11 Rockwell Automation, Inc.
  • 13.12 Cisco Systems, Inc.
  • 13.13 Mitsubishi Electric Corporation
  • 13.14 SparkCognition, Inc.
  • 13.15 Sight Machine, Inc.

List of Tables

  • Table 1 Global AI in Manufacturing Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Manufacturing Market Outlook, By Offering (2023-2034) ($MN)
  • Table 3 Global AI in Manufacturing Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 4 Global AI in Manufacturing Market Outlook, By Sensors (2023-2034) ($MN)
  • Table 5 Global AI in Manufacturing Market Outlook, By Industrial Robots (2023-2034) ($MN)
  • Table 6 Global AI in Manufacturing Market Outlook, By Processors & Edge Devices (2023-2034) ($MN)
  • Table 7 Global AI in Manufacturing Market Outlook, By IoT Devices (2023-2034) ($MN)
  • Table 8 Global AI in Manufacturing Market Outlook, By Software (2023-2034) ($MN)
  • Table 9 Global AI in Manufacturing Market Outlook, By Machine Learning Software (2023-2034) ($MN)
  • Table 10 Global AI in Manufacturing Market Outlook, By Data Analytics Platforms (2023-2034) ($MN)
  • Table 11 Global AI in Manufacturing Market Outlook, By Quality Control Software (2023-2034) ($MN)
  • Table 12 Global AI in Manufacturing Market Outlook, By Supply Chain Management Software (2023-2034) ($MN)
  • Table 13 Global AI in Manufacturing Market Outlook, By Services (2023-2034) ($MN)
  • Table 14 Global AI in Manufacturing Market Outlook, By Consulting Services (2023-2034) ($MN)
  • Table 15 Global AI in Manufacturing Market Outlook, By System Integration & Deployment (2023-2034) ($MN)
  • Table 16 Global AI in Manufacturing Market Outlook, By Training & Support (2023-2034) ($MN)
  • Table 17 Global AI in Manufacturing Market Outlook, By Managed Services (2023-2034) ($MN)
  • Table 18 Global AI in Manufacturing Market Outlook, By Technology (2023-2034) ($MN)
  • Table 19 Global AI in Manufacturing Market Outlook, By Machine Learning (ML) (2023-2034) ($MN)
  • Table 20 Global AI in Manufacturing Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 21 Global AI in Manufacturing Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 22 Global AI in Manufacturing Market Outlook, By Context-Aware Computing (2023-2034) ($MN)
  • Table 23 Global AI in Manufacturing Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 24 Global AI in Manufacturing Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 25 Global AI in Manufacturing Market Outlook, By On-Premise (2023-2034) ($MN)
  • Table 26 Global AI in Manufacturing Market Outlook, By Hybrid (2023-2034) ($MN)
  • Table 27 Global AI in Manufacturing Market Outlook, By Application (2023-2034) ($MN)
  • Table 28 Global AI in Manufacturing Market Outlook, By Predictive Maintenance & Machinery Inspection (2023-2034) ($MN)
  • Table 29 Global AI in Manufacturing Market Outlook, By Quality Control & Inspection (2023-2034) ($MN)
  • Table 30 Global AI in Manufacturing Market Outlook, By Production Planning & Optimization (2023-2034) ($MN)
  • Table 31 Global AI in Manufacturing Market Outlook, By Supply Chain & Inventory Management (2023-2034) ($MN)
  • Table 32 Global AI in Manufacturing Market Outlook, By Industrial Robotics & Automation (2023-2034) ($MN)
  • Table 33 Global AI in Manufacturing Market Outlook, By Material Movement (2023-2034) ($MN)
  • Table 34 Global AI in Manufacturing Market Outlook, By Cybersecurity in Manufacturing (2023-2034) ($MN)
  • Table 35 Global AI in Manufacturing Market Outlook, By Field Services (2023-2034) ($MN)
  • Table 36 Global AI in Manufacturing Market Outlook, By End User (2023-2034) ($MN)
  • Table 37 Global AI in Manufacturing Market Outlook, By Automotive (2023-2034) ($MN)
  • Table 38 Global AI in Manufacturing Market Outlook, By Electronics & Semiconductor (2023-2034) ($MN)
  • Table 39 Global AI in Manufacturing Market Outlook, By Pharmaceuticals (2023-2034) ($MN)
  • Table 40 Global AI in Manufacturing Market Outlook, By Heavy Machinery & Metal Manufacturing (2023-2034) ($MN)
  • Table 41 Global AI in Manufacturing Market Outlook, By Food & Beverage (2023-2034) ($MN)
  • Table 42 Global AI in Manufacturing Market Outlook, By Energy & Power (2023-2034) ($MN)
  • Table 43 Global AI in Manufacturing 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.