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市场调查报告书
商品编码
2021750

2034年製造业人工智慧市场预测：按交付方式、技术、部署方式、应用、最终用户和地区分類的全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，预计到 2026 年，全球製造业人工智慧市场规模将达到 98.5 亿美元，到 2034 年将达到 1,288 亿美元，预测期内复合年增长率将达到 37.9%。

人工智慧在製造业中应用先进的演算法、机器学习和数据分析技术，以优化生产流程、提高效率并增强决策能力。这使得即时监控、预测性维护、品管和复杂任务的自动化成为可能。透过分析来自机器和系统的大量数据，人工智慧帮助製造商减少停机时间、最大限度地减少错误并提高生产效率。总而言之，人工智慧推动创新和卓越运营，同时支援更智慧、更灵活、更经济高效的製造营运。

製造业对营运效率和成本降低的需求日益增长。

製造商面临着在保持高品质和高产量的同时降低生产成本的持续压力。人工智慧能够实现即时流程优化、预测性维护和智慧自动化，从而显着减少机器停机时间、缺陷率和能源消耗。透过以数据驱动的主动决策取代被动维护，人工智慧最大限度地减少了代价高昂的营运停机时间，并延长了设备的使用寿命。人工智慧驱动的品质检测系统还能减少返工和保固索赔。在全球竞争日益激烈和利润率不断下降的背景下，製造商正越来越多地采用人工智慧来简化营运、提高资产利用率，并打造更精简、更具成本效益的生产环境。

初始投资高且整合复杂

在製造业中实施人工智慧解决方案需要前期对感测器、边缘设备、软体平台和熟练人员进行大量投资。许多传统生产设施缺乏必要的资料基础设施和互通性标准，导致整合成本高且耗时。对老旧设备进行人工智慧感测器改造和连接通常会中断生产。此外，缺乏具备製造业专业知识的资料科学家和人工智慧工程师也阻碍了人工智慧的普及应用。这些障碍对中小企业而言尤其严峻。由于缺乏明确的短期投资报酬率和内部技术专长，许多製造商对全面实施人工智慧持谨慎态度。

智慧工厂数位双胞胎技术的扩展

工业4.0数位双胞胎生态系统的兴起，为人工智慧在製造业的应用创造了巨大的机会。数位双胞胎是实体生产系统的虚拟副本，能够持续产生资料流，供人工智慧模型分析，从而模拟、预测和优化实际生产营运。製造商正日益投资于完全互联的智慧工厂，在这些工厂中，人工智慧统筹从原材料交付到最终组装的每一个环节。这种整合实现了封闭回路型控制系统，能够即时进行自我修正。随着云端运算和5G连接的日益普及，人工智慧驱动的数位双胞胎将带来更高水准的敏捷性、可自订性和韧性。

互联工厂中的资料隐私和网路安全风险

人工智慧主导的製造业高度依赖互联设备、云端平台和即时数据共用，扩大了网路攻击的范围。人工智慧控制系统一旦遭到破坏，可能导致生产参数被窜改、品质检查中断或专有设计被窃取。恶意攻击者可以将虚假资料注入机器学习模型，导致预测不准确和营运决策风险过高。IT安全资源有限的中小型製造商尤其容易受到攻击。确保端对端加密、强大的存取控制和持续的威胁监控至关重要，但这会增加成本和复杂性。网路韧性仍然是一项重大挑战。

新冠疫情的影响：

新冠疫情透过封锁、劳动力短缺和供应链崩坏，对全球製造业造成了严重衝击。然而，疫情也加速了数位转型，製造商纷纷寻求非接触式营运和更强的韧性。人工智慧驱动的预测性维护和自动化品质检测减少了对现场人员的需求。社交距离的规定促进了人工智慧机器人和远端监控解决方案的应用。这场危机暴露了僵化、劳力密集生产线的弊端，并促使企业对人工智慧进行长期投资，以提高供应链可视性和实现自适应製造。因此，疫情起到了催化剂的作用，显示人工智慧对于保护製造业免受未来类似衝击至关重要。

在预测期内，硬体领域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。这主要源于对工业机器人、物联网感测器、处理器和边缘设备等实体组件的根本性需求，这些组件用于收集和处理製造数据。这些硬体元素构成了任何人工智慧部署的基础，能够实现即时监控、自动化和控制。随着工厂投资建造新的生产线并维修现有设备，对稳健、高性能硬体的需求持续成长。

在预测期内，电子和半导体产业预计将呈现最高的复合年增长率。

在预测期内，由于製造更小、更密集、更复杂且零缺陷晶片的压力日益增大，电子和半导体产业预计将呈现最高的成长率。传统的检测方法难以在高速生产线上检测到微小的缺陷。人工智慧驱动的电脑视觉和机器学习演算法能够实现晶圆缺陷的即时检测、微影术优化和良率预测。透过识别奈米级的异常情况，人工智慧在最先进的半导体製造工厂中正变得至关重要，因为它能够减少漏检、提高生产效率并减少代价高昂的返工。

市占率最大的地区：

在预测期内，亚太地区预计将占据最大的市场份额。这主要得益于快速的工业化进程、中国、印度、日本和韩国政府主导的数位化製造项目，以及电子和半导体生产的扩张。该地区集中了大量出口导向工厂，而人工智慧对于提升产品品质和效率至关重要。对5G基础设施投资的增加以及价格亲民的物联网设备的普及降低了进入门槛。随着人事费用的上升，製造商越来越依赖人工智慧驱动的自动化来保持全球竞争力，这进一步加速了市场成长。

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。这主要得益于快速的工业化进程、中国、印度、日本和韩国政府主导的智慧工厂计划，以及该地区在电子和半导体生产领域的持续领先地位。人事费用的上升推动了自动化技术的应用，而5G基础设施的扩展和价格亲民的物联网感测器则促进了人工智慧的普及。此外，主要製造地的存在以及对工业4.0技术不断增长的投资，使亚太地区成为製造业人工智慧成长最快的市场。

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：全球製造业人工智慧市场：按产品/服务划分

  • 硬体
    • 感应器
    • 工业机器人
    • 处理器和边缘设备
    • 物联网设备
  • 软体
    • 机器学习软体
    • 数据分析平台
    • 品管软体
    • 供应链管理软体
  • 服务
    • 咨询服务
    • 系统整合与部署
    • 培训和支持
    • 託管服务

第六章：全球製造业人工智慧市场：按技术划分

  • 机器学习（ML）
  • 电脑视觉
  • 自然语言处理（NLP）
  • 情境感知计算

第七章：全球製造业人工智慧市场：依部署模式划分

  • 基于云端的
  • 现场
  • 杂交种

第八章：全球製造业人工智慧市场：按应用领域划分

  • 预测性维护和机械检查
  • 品管和检验
  • 生产计画与优化
  • 供应链和库存管理
  • 工业机器人与自动化
  • 物料运输
  • 製造业网路安全
  • 现场服务

第九章：全球製造业人工智慧市场：按最终用户划分

  • 车
  • 电子和半导体
  • 製药
  • 重型设备/金属製造
  • 食品/饮料
  • 能源与电力
  • 其他最终用户

第十章：全球製造业人工智慧市场：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

第十一章 策略市场资讯

  • 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

第十二章 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

第十三章：公司简介

  • 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.