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市場調查報告書
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2007824

人工智慧在製造業品管領域的市場：2034 年預測——按組件、技術、部署模式、品管應用、最終用戶和地區分類的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球用於製造業品管的人工智慧市場規模將達到 171 億美元，並在預測期內以 22.2% 的複合年成長率成長，到 2034 年將達到 1243 億美元。

在製造業品管，人工智慧（AI）指的是利用機器學習、電腦視覺和進階數據分析等人工智慧技術，在整個製造過程中監控、檢查和改進產品品質。人工智慧系統能夠分析即時生產數據，識別缺陷，預測潛在的品質問題，並高精度地自動執行檢測任務。人工智慧驅動的品管能夠加快決策速度、最大限度地減少人為錯誤、維持產品標準的一致性、減少材料浪費，並幫助製造商維持可靠、擴充性且高效能的生產環境，從而提高營運效率。

對零缺陷製造的需求日益成長

消費者和監管機構對零缺陷產品的壓力日益增大，迫使製造商採用人工智慧驅動的品管系統。汽車、電子和醫療設備等產業正面臨著因產品缺陷導致的召回和品牌形象受損而造成的巨大成本。人工智慧驅動的視覺偵測和預測分析能夠即時偵測出人眼無法察覺的微小缺陷。這項技術能夠確保大規模生產線上品質的一致性，從而降低缺陷率和返工率。對卓越營運的追求以及在精度要求極高的領域保持競爭優勢的需求，正在顯著加速人工智慧品管解決方案的普及應用。

初始投資高且整合複雜

在製造業中應用人工智慧，除了高解析度攝影機和邊緣運算設備等硬體外，還需要對先進的軟體平台進行大量前期投資。將這些系統整合到現有生產線中通常需要停產和進行大規模客製化，這帶來了巨大的技術挑戰。缺乏既了解製造流程又了解人工智慧演算法的熟練專家，進一步加劇了實施的複雜性。由於高昂的資本支出和漫長的引進週期，中小企業難以證明投資報酬率 (ROI) 的合理性。這些財務和技術壁壘會減緩市場滲透，尤其是在成本敏感產業和發展中地區。

邊緣人工智慧和即時分析的成長

邊緣人工智慧的出現正在變革品管，它能夠在工廠現場進行資料處理，並顯著降低延遲和頻寬成本。這使得即時決策成為可能，例如在毫秒內識別缺陷零件並將其從生產線上移除。工業IoT(IIoT) 設備和 5G 連接的普及正在增強邊緣人工智慧系統的能力，使其能夠在工廠現場進行更複雜的分析。製造商正在利用這些進步來建構封閉回路型品管系統，該系統能夠自動調整機器參數，從而主動預防缺陷。這種向即時、本地智慧的轉變，為提供強大的邊緣人工智慧硬體和軟體解決方案的供應商帶來了巨大的商機。

資料安全和隱私問題

由於人工智慧品管系統依賴包含專有製造設計和生產參數的龐大資料集，因此它們極易成為網路攻擊的目標。安全漏洞可能導致智慧財產權被盜、生產流程中斷或品質資料被竄改，最終可能導致不安全產品流入市場。雲端分析平台的整合擴大了攻擊面，因此強大的網路安全通訊協定和資料加密至關重要。航空航太和國防等高度監管產業的製造商面臨嚴格的合規要求，而這些要求難以透過互聯的人工智慧系統來滿足。這些安全漏洞會阻礙系統部署，並需要持續投資於安全防護措施。

新冠疫情的影響

疫情對全球製造業供應鏈和勞動力管理造成了嚴重衝擊，使得自動化成為維持生產連續性的關鍵。社交距離的措施加速了人工智慧視覺檢測系統的應用，以減少對人工品質檢查的依賴。封鎖措施凸顯了人性化的品質流程的脆弱性，促使製造商投資更具彈性的自動化系統。儘管初期資本投資受到限制，但長期策略重點已果斷轉向工業4.0計畫。在後疫情時代，製造商正優先考慮人工智慧驅動的品管，以增強供應鏈韌性，緩解未來人手不足，並實現更大的營運柔軟性。

在預測期內，軟體領域預計將佔據最大的市場佔有率。

預計在預測期內，軟體領域將佔據最大的市場佔有率。其主導地位源自於電子、汽車和製藥等關鍵應用領域，在這些領域，精確度至關重要。透過實現即時檢測和分類，該軟體能夠降低缺陷率並提高營運效率。演算法的持續改進以及與現有攝影機基礎設施的無縫整合，鞏固了其作為市場中最大軟體類別的地位。

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，受對元件超小型化和零缺陷製造的需求驅動，電子和半導體產業預計將呈現最高的成長率。人工智慧驅動的光學檢測系統對於識別電路基板、焊點和矽晶圓中人工檢測無法發現的微小缺陷至關重要。隨著半導體日益複雜，家用電子電器的需求激增，製造商正依賴機器學習來最佳化產量比率。這種對技術的依賴正在推動持續投資，並將電子產業定位為關鍵的終端用戶領域。

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，這得益於其強大的技術領先地位和先進自動化技術的快速普及。美國在開發用於工業應用的尖端人工智慧演算法和邊緣運算硬體方面處於領先地位。美國大力推動製造業回流，尤其是在電子和醫療設備領域，這推動了對自動化品管的需求，以在低成本勞動力市場中保持競爭力。主要人工智慧軟體供應商的存在以及強大的創新生態系統正在加速市場成長。

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，這主要得益於其作為全球製造地的地位，尤其是在電子、汽車和半導體行業。中國、日本、韓國和印度等國家正積極採用工業4.0技術，以提高生產效率和產品品質。政府主導的大規模智慧工廠建設和在地化生產措施正在推動大量投資。

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）
  • 區域細分
    • 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：全球人工智慧在製造業品管領域的市場：按組件分類

  • 硬體
    • 人工智慧攝影機和視覺感測器
    • 邊緣人工智慧設備
    • 工業機器人與協作機器人
    • 智慧感測器和物聯網設備
  • 軟體
    • 電腦視覺測試軟體
    • 機器學習品質分析平台
    • 缺陷檢測和分類軟體
    • 預測品質分析
    • 數據視覺化和報告工具
  • 服務
    • 諮詢服務
    • 系統整合與部署
    • 維護和支援
    • AI模型訓練和客製化

第6章：全球人工智慧在製造業品管領域的市場：依技術分類

  • 機器學習
    • 監督式學習
    • 無監督學習
    • 強化學習
  • 電腦視覺
    • 影像識別
    • 視覺缺陷檢測
    • 模式識別
  • 深度學習
    • 卷積類神經網路（CNN）
    • 用於缺陷模擬的生成式人工智慧
  • 自然語言處理（NLP）
  • 邊緣人工智慧和即時分析

第7章：全球人工智慧在製造業品管領域的市場：依部署模式分類

  • 基於雲端的
  • 現場
  • 混合實現

第8章：全球人工智慧在製造業品管領域的市場：按品管應用分類

  • 目視檢查和缺陷檢測
  • 表面缺陷檢測
  • 組裝檢驗
  • 尺寸檢驗
  • 過程品質監控
  • 預測性品管和根本原因分析
  • 自動化品質分類

第9章：全球製造業品管領域的人工智慧市場：按最終用戶分類

  • 汽車製造
  • 電子和半導體
  • 航太/國防
  • 食品/飲料
  • 藥品和醫療設備
  • 重型機械和工業設備
  • 消費品製造
  • 其他最終用戶

第10章：全球製造業品管領域的人工智慧市場：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

  • 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.