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市场调查报告书
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2007824

人工智慧在製造业品管领域的市场：2034 年预测——按组件、技术、部署模式、品管应用、最终用户和地区分類的全球分析

AI in Manufacturing Quality Control Market Forecasts to 2034 - Global Analysis By Component (Hardware, Software and Services), Technology, Deployment Mode, Quality Control 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，全球用于製造业品管的人工智慧市场规模将达到 171 亿美元，并在预测期内以 22.2% 的复合年增长率增长，到 2034 年将达到 1243 亿美元。

在製造业品管，人工智慧（AI）指的是利用机器学习、电脑视觉和进阶数据分析等人工智慧技术，在整个製造过程中监控、检查和改进产品品质。人工智慧系统能够分析即时生产数据，识别缺陷，预测潜在的品质问题，并高精度地自动执行检测任务。人工智慧驱动的品管能够加快决策速度、最大限度地减少人为错误、维持产品标准的一致性、减少材料浪费，并帮助製造商维持可靠、扩充性且高效能的生产环境，从而提高营运效率。

对零缺陷製造的需求日益增长

消费者和监管机构对零缺陷产品的压力日益增大，迫使製造商采用人工智慧驱动的品管系统。汽车、电子和医疗设备等产业正面临着因产品缺陷导致的召回和品牌形象受损而造成的巨大成本。人工智慧驱动的视觉侦测和预测分析能够即时侦测出人眼无法察觉的微小缺陷。这项技术能够确保大规模生产线上品质的一致性，从而降低缺陷率和返工率。对卓越营运的追求以及在精度要求极高的领域保持竞争优势的需求，正在显着加速人工智慧品管解决方案的普及应用。

初始投资高且整合复杂

在製造业中应用人工智慧，除了高解析度摄影机和边缘运算设备等硬体外，还需要对先进的软体平台进行大量前期投资。将这些系统整合到现有生产线中通常需要停产和进行大规模定制，这带来了巨大的技术挑战。缺乏既了解製造流程又了解人工智慧演算法的熟练专家，进一步加剧了实施的复杂性。由于高昂的资本支出和漫长的引进週期，中小企业难以证明投资报酬率 (ROI) 的合理性。这些财务和技术壁垒会减缓市场渗透，尤其是在成本敏感产业和发展中地区。

边缘人工智慧和即时分析的成长

边缘人工智慧的出现正在变革品管，它能够在工厂现场进行资料处理，并显着降低延迟和频宽成本。这使得即时决策成为可能，例如在毫秒内识别缺陷零件并将其从生产线上移除。工业IoT(IIoT) 设备和 5G 连接的普及正在增强边缘人工智慧系统的能力，使其能够在工厂现场进行更复杂的分析。製造商正在利用这些进步来建构封闭回路型品管系统，该系统能够自动调整机器参数，从而主动预防缺陷。这种向即时、本地智慧的转变，为提供强大的边缘人工智慧硬体和软体解决方案的供应商带来了巨大的商机。

资料安全和隐私问题

由于人工智慧品管系统依赖包含专有製造设计和生产参数的庞大资料集，因此它们极易成为网路攻击的目标。安全漏洞可能导致智慧财产权被盗、生产流程中断或品质资料被窜改，最终可能导致不安全产品流入市场。云端分析平台的整合扩大了攻击面，因此强大的网路安全通讯协定和资料加密至关重要。航空航太和国防等高度监管产业的製造商面临严格的合规要求，而这些要求难以透过互联的人工智慧系统来满足。这些安全漏洞会阻碍系统部署，并需要持续投资于安全防护措施。

新冠疫情的影响

疫情对全球製造业供应链和劳动力管理造成了严重衝击，使得自动化成为维持生产连续性的关键。社交距离的措施加速了人工智慧视觉检测系统的应用，以减少对人工品质检查的依赖。封锁措施凸显了人性化的品质流程的脆弱性，促使製造商投资更具弹性的自动化系统。儘管初期资本投资受到限制，但长期策略重点已果断转向工业4.0计画。在后疫情时代，製造商正优先考虑人工智慧驱动的品管，以增强供应链韧性，缓解未来人手不足，并实现更大的营运柔软性。

在预测期内，软体领域预计将占据最大的市场份额。

预计在预测期内，软体领域将占据最大的市场份额。其主导地位源自于电子、汽车和製药等关键应用领域，在这些领域，精确度至关重要。透过实现即时检测和分类，该软体能够降低缺陷率并提高营运效率。演算法的持续改进以及与现有摄影机基础设施的无缝集成，巩固了其作为市场中最大软体类别的地位。

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，受对元件超小型化和零缺陷製造的需求驱动，电子和半导体产业预计将呈现最高的成长率。人工智慧驱动的光学检测系统对于识别电路基板、焊点和硅片中人工检测无法发现的微小缺陷至关重要。随着半导体日益复杂，家用电子电器的需求激增，製造商正依赖机器学习来优化产量比率。这种对技术的依赖正在推动持续投资，并将电子产业定位为关键的终端用户领域。

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，这得益于其强大的技术领先地位和先进自动化技术的快速普及。美国在开发用于工业应用的尖端人工智慧演算法和边缘运算硬体方面处于领先地位。美国大力推动製造业回流，尤其是在电子和医疗设备领域，这推动了对自动化品管的需求，以在低成本劳动力市场中保持竞争力。主要人工智慧软体供应商的存在以及强大的创新生态系统正在加速市场成长。

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，这主要得益于其作为全球製造地的地位，尤其是在电子、汽车和半导体行业。中国、日本、韩国和印度等国家正积极采用工业4.0技术，以提高生产效率和产品品质。政府主导的大规模智慧工厂建设和在地化生产措施正在推动大量投资。

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）
  • 区域细分
    • 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：全球人工智慧在製造业品管领域的市场：按组件划分

  • 硬体
    • 人工智慧摄影机和视觉感测器
    • 边缘人工智慧设备
    • 工业机器人与协作机器人
    • 智慧感测器和物联网设备
  • 软体
    • 电脑视觉测试软体
    • 机器学习品质分析平台
    • 缺陷检测和分类软体
    • 预测品质分析
    • 数据视觉化和报告工具
  • 服务
    • 咨询服务
    • 系统整合与部署
    • 维护和支援
    • AI模型训练和客製化

第六章：全球人工智慧在製造业品管领域的市场：依技术划分

  • 机器学习
    • 监督式学习
    • 无监督学习
    • 强化学习
  • 电脑视觉
    • 影像识别
    • 视觉缺陷检测
    • 模式识别
  • 深度学习
    • 卷积类神经网路（CNN）
    • 用于缺陷模拟的生成式人工智慧
  • 自然语言处理（NLP）
  • 边缘人工智慧和即时分析

第七章：全球人工智慧在製造业品管领域的市场：依部署模式划分

  • 基于云端的
  • 现场
  • 混合实现

第八章：全球人工智慧在製造业品管领域的市场：按品管应用划分

  • 目视检查和缺陷检测
  • 表面缺陷检测
  • 组装检验
  • 尺寸检验
  • 过程品质监控
  • 预测性品管和根本原因分析
  • 自动化品质分类

第九章：全球製造业品管领域的人工智慧市场：按最终用户划分

  • 汽车製造
  • 电子和半导体
  • 航太/国防
  • 食品/饮料
  • 药品和医疗设备
  • 重型机械和工业设备
  • 消费品製造
  • 其他最终用户

第十章：全球製造业品管领域的人工智慧市场：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

  • Cognex Corporation
  • KEYENCE Corporation
  • Omron Corporation
  • Basler AG
  • Teledyne Technologies Incorporated
  • SICK AG
  • ISRA Vision AG
  • MVTec Software GmbH
  • National Instruments Corporation
  • Landing AI
  • Robovision
  • Elementary
  • Pleora Technologies
  • JAI A/S
  • Baumer Group
Product Code: SMRC34699

According to Stratistics MRC, the Global AI in Manufacturing Quality Control Market is accounted for $17.1 billion in 2026 and is expected to reach $124.3 billion by 2034 growing at a CAGR of 22.2% during the forecast period. AI in Manufacturing Quality Control involves the use of artificial intelligence technologies such as machine learning, computer vision, and advanced data analytics to monitor, inspect, and enhance product quality throughout manufacturing processes. AI systems analyze real-time production data, identify defects, predict possible quality issues, and automate inspection activities with high precision. By enabling faster decision-making and minimizing human errors, AI-driven quality control improves operational efficiency, maintains consistent product standards, reduces material waste, and helps manufacturers sustain reliable, scalable, and high-performance production environments.

Market Dynamics:

Driver:

Increasing demand for zero-defect manufacturing

The escalating pressure from consumers and regulatory bodies for flawless products is compelling manufacturers to adopt AI-driven quality control systems. Industries such as automotive, electronics, and medical devices face high costs associated with recalls and brand damage from defective products. AI-powered visual inspection and predictive analytics enable real-time detection of micro-defects that are invisible to the human eye. This technology facilitates consistent quality assurance across high-volume production lines, reducing scrap rates and rework. The pursuit of operational excellence and the need to maintain competitive advantage in precision-dependent sectors are significantly accelerating the deployment of AI-based quality control solutions.

Restraint:

High initial investment and integration complexity

Implementing AI in manufacturing requires substantial upfront investment in hardware, including high-resolution cameras and edge computing devices, alongside sophisticated software platforms. The integration of these systems into legacy manufacturing lines poses significant technical challenges, often requiring production halts and extensive customization. A shortage of skilled professionals who understand both manufacturing processes and AI algorithms further complicates deployment. Small and medium-sized enterprises (SMEs) struggle to justify the return on investment due to high capital expenditure and long implementation cycles. This financial and technical barrier can slow down market penetration, particularly in cost-sensitive industries and developing regions.

Opportunity:

Growth of edge AI and real-time analytics

The emergence of edge AI is transforming quality control by enabling data processing at the source of production, drastically reducing latency and bandwidth costs. This allows for instantaneous decision-making, where defective components can be identified and ejected from the production line in milliseconds. The proliferation of industrial IoT (IIoT) devices and 5G connectivity is enhancing the capabilities of edge AI systems, allowing for more complex analytics on the factory floor. Manufacturers are leveraging these advancements to create closed-loop quality systems that automatically adjust machine parameters to prevent defects. This shift towards real-time, localized intelligence presents a significant opportunity for vendors offering robust edge AI hardware and software solutions.

Threat:

Data security and privacy concerns

The reliance on extensive datasets, including proprietary manufacturing designs and production parameters, makes AI quality control systems a prime target for cyberattacks. A security breach could lead to intellectual property theft, sabotage of production integrity, or the manipulation of quality data, resulting in unsafe products reaching the market. The integration of cloud-based analytics platforms expands the attack surface, requiring robust cybersecurity protocols and data encryption. Manufacturers in highly regulated sectors like aerospace and defense face stringent compliance requirements that can be challenging to meet with interconnected AI systems. These security vulnerabilities can deter adoption and necessitate continuous investment in protective measures.

Covid-19 Impact

The pandemic severely disrupted global manufacturing supply chains and labor availability, creating a critical need for automation to maintain production continuity. Social distancing measures accelerated the adoption of AI-powered visual inspection systems to reduce reliance on manual quality checkers. Lockdowns highlighted the fragility of human-centric quality processes, pushing manufacturers to invest in resilient, automated systems. Although initial capital expenditure was constrained, the long-term strategic focus shifted decisively toward Industry 4.0 initiatives. Post-pandemic, manufacturers are prioritizing AI-driven quality control to build supply chain resilience, mitigate future labor shortages, and achieve greater operational flexibility.

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

The software segment is expected to account for the largest market share during the forecast period, due to its dominance stems from critical applications across electronics, automotive, and pharmaceuticals, where precision is non-negotiable. By enabling real-time detection and classification, it reduces scrap rates and enhances operational efficiency. Continuous algorithm improvements and seamless integration with existing camera infrastructure solidify its position as the market's largest software category.

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 the extreme miniaturization of components and the demand for zero-defect manufacturing. AI-powered optical inspection systems are essential for identifying microscopic flaws in circuit boards, soldering, and silicon wafers that human inspectors cannot detect. As semiconductor complexity increases and consumer electronics demand surges, manufacturers rely on machine learning to ensure yield optimization. This technological dependency drives consistent investment, positioning electronics as a critical end-user segment.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by strong technological leadership and the rapid adoption of advanced automation. The United States is at the forefront of developing cutting-edge AI algorithms and edge computing hardware for industrial applications. A strong focus on reshoring manufacturing capabilities, particularly in electronics and medical devices, is driving demand for automated quality control to compete with low-cost labor markets. The presence of major AI software vendors and a robust ecosystem for technology innovation accelerates market growth.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by its status as the global manufacturing hub, particularly in electronics, automotive, and semiconductors. Countries like China, Japan, South Korea, and India are aggressively adopting Industry 4.0 technologies to enhance production efficiency and product quality. Massive government initiatives promoting smart factory development and local manufacturing are driving substantial investments.

Key players in the market

Some of the key players in AI in Manufacturing Quality Control Market include Cognex Corporation, KEYENCE Corporation, Omron Corporation, Basler AG, Teledyne Technologies Incorporated, SICK AG, ISRA Vision AG, MVTec Software GmbH, National Instruments Corporation, Landing AI, Robovision, Elementary, Pleora Technologies, JAI A/S, and Baumer Group.

Key Developments:

In March 2025, Cognex Corporation announced IMA E-COMMERCE, part of the IMA Group, is enhancing order fulfillment efficiency and sustainability with Cognex's advanced In-Sight(R) vision systems and DataMan(R) barcode readers. IMA E-COMMERCE and Cognex share a commitment to innovation and plan to continue to develop new solutions for logistics automation.

Components Covered:

  • Hardware
  • Software
  • Services

Technologies Covered:

  • Machine Learning
  • Computer Vision
  • Deep Learning
  • Natural Language Processing (NLP)
  • Edge AI and Real-Time Analytics

Deployment Modes Covered:

  • Cloud-Based
  • On-Premise
  • Hybrid Deployment

Quality Control Applications Covered:

  • Visual Inspection & Defect Detection
  • Surface Defect Detection
  • Assembly Verification
  • Dimensional Inspection
  • Process Quality Monitoring
  • Predictive Quality & Root Cause Analysis
  • Automated Quality Sorting

End Users Covered:

  • Automotive Manufacturing
  • Electronics & Semiconductor
  • Aerospace & Defense
  • Food & Beverage
  • Pharmaceuticals & Medical Devices
  • Heavy Machinery & Industrial Equipment
  • Consumer Goods Manufacturing
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of 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, 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 Quality Control Market, By Component

  • 5.1 Hardware
    • 5.1.1 AI Cameras & Vision Sensors
    • 5.1.2 Edge AI Devices
    • 5.1.3 Industrial Robots & Cobots
    • 5.1.4 Smart Sensors & IoT Devices
  • 5.2 Software
    • 5.2.1 Computer Vision Inspection Software
    • 5.2.2 Machine Learning Quality Analytics Platforms
    • 5.2.3 Defect Detection & Classification Software
    • 5.2.4 Predictive Quality Analytics
    • 5.2.5 Data Visualization & Reporting Tools
  • 5.3 Services
    • 5.3.1 Consulting Services
    • 5.3.2 System Integration & Deployment
    • 5.3.3 Maintenance & Support
    • 5.3.4 AI Model Training & Customization

6 Global AI in Manufacturing Quality Control Market, By Technology

  • 6.1 Machine Learning
    • 6.1.1 Supervised Learning
    • 6.1.2 Unsupervised Learning
    • 6.1.3 Reinforcement Learning
  • 6.2 Computer Vision
    • 6.2.1 Image Recognition
    • 6.2.2 Visual Defect Detection
    • 6.2.3 Pattern Recognition
  • 6.3 Deep Learning
    • 6.3.1 Convolutional Neural Networks (CNN)
    • 6.3.2 Generative AI for Defect Simulation
  • 6.4 Natural Language Processing (NLP)
  • 6.5 Edge AI and Real-Time Analytics

7 Global AI in Manufacturing Quality Control Market, By Deployment Mode

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

8 Global AI in Manufacturing Quality Control Market, By Quality Control Application

  • 8.1 Visual Inspection & Defect Detection
  • 8.2 Surface Defect Detection
  • 8.3 Assembly Verification
  • 8.4 Dimensional Inspection
  • 8.5 Process Quality Monitoring
  • 8.6 Predictive Quality & Root Cause Analysis
  • 8.7 Automated Quality Sorting

9 Global AI in Manufacturing Quality Control Market, By End User

  • 9.1 Automotive Manufacturing
  • 9.2 Electronics & Semiconductor
  • 9.3 Aerospace & Defense
  • 9.4 Food & Beverage
  • 9.5 Pharmaceuticals & Medical Devices
  • 9.6 Heavy Machinery & Industrial Equipment
  • 9.7 Consumer Goods Manufacturing
  • 9.8 Other End Users

10 Global AI in Manufacturing Quality Control 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 Cognex Corporation
  • 13.2 KEYENCE Corporation
  • 13.3 Omron Corporation
  • 13.4 Basler AG
  • 13.5 Teledyne Technologies Incorporated
  • 13.6 SICK AG
  • 13.7 ISRA Vision AG
  • 13.8 MVTec Software GmbH
  • 13.9 National Instruments Corporation
  • 13.10 Landing AI
  • 13.11 Robovision
  • 13.12 Elementary
  • 13.13 Pleora Technologies
  • 13.14 JAI A/S
  • 13.15 Baumer Group

List of Tables

  • Table 1 Global AI in Manufacturing Quality Control Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Manufacturing Quality Control Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Manufacturing Quality Control Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 4 Global AI in Manufacturing Quality Control Market Outlook, By AI Cameras & Vision Sensors (2023-2034) ($MN)
  • Table 5 Global AI in Manufacturing Quality Control Market Outlook, By Edge AI Devices (2023-2034) ($MN)
  • Table 6 Global AI in Manufacturing Quality Control Market Outlook, By Industrial Robots & Cobots (2023-2034) ($MN)
  • Table 7 Global AI in Manufacturing Quality Control Market Outlook, By Smart Sensors & IoT Devices (2023-2034) ($MN)
  • Table 8 Global AI in Manufacturing Quality Control Market Outlook, By Software (2023-2034) ($MN)
  • Table 9 Global AI in Manufacturing Quality Control Market Outlook, By Computer Vision Inspection Software (2023-2034) ($MN)
  • Table 10 Global AI in Manufacturing Quality Control Market Outlook, By Machine Learning Quality Analytics Platforms (2023-2034) ($MN)
  • Table 11 Global AI in Manufacturing Quality Control Market Outlook, By Defect Detection & Classification Software (2023-2034) ($MN)
  • Table 12 Global AI in Manufacturing Quality Control Market Outlook, By Predictive Quality Analytics (2023-2034) ($MN)
  • Table 13 Global AI in Manufacturing Quality Control Market Outlook, By Data Visualization & Reporting Tools (2023-2034) ($MN)
  • Table 14 Global AI in Manufacturing Quality Control Market Outlook, By Services (2023-2034) ($MN)
  • Table 15 Global AI in Manufacturing Quality Control Market Outlook, By Consulting Services (2023-2034) ($MN)
  • Table 16 Global AI in Manufacturing Quality Control Market Outlook, By System Integration & Deployment (2023-2034) ($MN)
  • Table 17 Global AI in Manufacturing Quality Control Market Outlook, By Maintenance & Support (2023-2034) ($MN)
  • Table 18 Global AI in Manufacturing Quality Control Market Outlook, By AI Model Training & Customization (2023-2034) ($MN)
  • Table 19 Global AI in Manufacturing Quality Control Market Outlook, By Technology (2023-2034) ($MN)
  • Table 20 Global AI in Manufacturing Quality Control Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 21 Global AI in Manufacturing Quality Control Market Outlook, By Supervised Learning (2023-2034) ($MN)
  • Table 22 Global AI in Manufacturing Quality Control Market Outlook, By Unsupervised Learning (2023-2034) ($MN)
  • Table 23 Global AI in Manufacturing Quality Control Market Outlook, By Reinforcement Learning (2023-2034) ($MN)
  • Table 24 Global AI in Manufacturing Quality Control Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 25 Global AI in Manufacturing Quality Control Market Outlook, By Image Recognition (2023-2034) ($MN)
  • Table 26 Global AI in Manufacturing Quality Control Market Outlook, By Visual Defect Detection (2023-2034) ($MN)
  • Table 27 Global AI in Manufacturing Quality Control Market Outlook, By Pattern Recognition (2023-2034) ($MN)
  • Table 28 Global AI in Manufacturing Quality Control Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 29 Global AI in Manufacturing Quality Control Market Outlook, By Convolutional Neural Networks (CNN) (2023-2034) ($MN)
  • Table 30 Global AI in Manufacturing Quality Control Market Outlook, By Generative AI for Defect Simulation (2023-2034) ($MN)
  • Table 31 Global AI in Manufacturing Quality Control Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 32 Global AI in Manufacturing Quality Control Market Outlook, By Edge AI and Real-Time Analytics (2023-2034) ($MN)
  • Table 33 Global AI in Manufacturing Quality Control Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 34 Global AI in Manufacturing Quality Control Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 35 Global AI in Manufacturing Quality Control Market Outlook, By On-Premise (2023-2034) ($MN)
  • Table 36 Global AI in Manufacturing Quality Control Market Outlook, By Hybrid Deployment (2023-2034) ($MN)
  • Table 37 Global AI in Manufacturing Quality Control Market Outlook, By Quality Control Application (2023-2034) ($MN)
  • Table 38 Global AI in Manufacturing Quality Control Market Outlook, By Visual Inspection & Defect Detection (2023-2034) ($MN)
  • Table 39 Global AI in Manufacturing Quality Control Market Outlook, By Surface Defect Detection (2023-2034) ($MN)
  • Table 40 Global AI in Manufacturing Quality Control Market Outlook, By Assembly Verification (2023-2034) ($MN)
  • Table 41 Global AI in Manufacturing Quality Control Market Outlook, By Dimensional Inspection (2023-2034) ($MN)
  • Table 42 Global AI in Manufacturing Quality Control Market Outlook, By Process Quality Monitoring (2023-2034) ($MN)
  • Table 43 Global AI in Manufacturing Quality Control Market Outlook, By Predictive Quality & Root Cause Analysis (2023-2034) ($MN)
  • Table 44 Global AI in Manufacturing Quality Control Market Outlook, By Automated Quality Sorting (2023-2034) ($MN)
  • Table 45 Global AI in Manufacturing Quality Control Market Outlook, By End User (2023-2034) ($MN)
  • Table 46 Global AI in Manufacturing Quality Control Market Outlook, By Automotive Manufacturing (2023-2034) ($MN)
  • Table 47 Global AI in Manufacturing Quality Control Market Outlook, By Electronics & Semiconductor (2023-2034) ($MN)
  • Table 48 Global AI in Manufacturing Quality Control Market Outlook, By Aerospace & Defense (2023-2034) ($MN)
  • Table 49 Global AI in Manufacturing Quality Control Market Outlook, By Food & Beverage (2023-2034) ($MN)
  • Table 50 Global AI in Manufacturing Quality Control Market Outlook, By Pharmaceuticals & Medical Devices (2023-2034) ($MN)
  • Table 51 Global AI in Manufacturing Quality Control Market Outlook, By Heavy Machinery & Industrial Equipment (2023-2034) ($MN)
  • Table 52 Global AI in Manufacturing Quality Control Market Outlook, By Consumer Goods Manufacturing (2023-2034) ($MN)
  • Table 53 Global AI in Manufacturing Quality Control 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.