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市场调查报告书
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1865518

全球工业环境边缘人工智慧市场：预测至 2032 年—按组件、部署方式、应用、最终用户和地区分類的分析

Edge AI in Industrial Environments Market Forecasts to 2032 - Global Analysis By Component (Hardware, Software and Integration & Support Services), Deployment Mode, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的一项研究，预计 2025 年全球工业环境边缘人工智慧市场价值为 42 亿美元，到 2032 年将达到 103.3 亿美元，预测期内复合年增长率为 13.7%。

面向工业环境的边缘人工智慧透过在资料来源附近进行智慧资料处理，变革了营运模式。对从感测器和机器本地收集的数据进行分析，可最大限度地减少延迟、提高可靠性并降低对云端的依赖。这种即时处理能力支援预测性维护、流程优化和早期故障检测。製造业、公共产业和物流等行业正在利用边缘人工智慧加速自动化、提高资产利用率并增强安全性。本地决策使各行业即使在网路状况不佳的情况下也能保持持续生产力。边缘人工智慧的整合促进了互联且适应性强的工业生态系统的发展，从而提升效率、韧性和智慧营运控制。

根据《国际运算工程与管理日誌》（IJCEM）的报告，一项关于工业4.0中边缘人工智慧的研究发现，在汽车和航太领域的试点部署中，基于边缘的预测维修系统可将计画外停机时间减少高达30%。该研究强调了联邦学习和边缘推理在保护敏感运行资料方面的重要作用。

对预测性维护和营运效率的需求日益增长

对提升营运效率和预测性维护日益增长的需求正在推动边缘人工智慧在工业应用中的扩展。边缘人工智慧透过处理来自感测器和机器的即时数据，预测设备故障并防止计划外停机。这种预测能力可以提高运作、降低维护成本并支援更智慧的生产计画。此外，边缘人工智慧还能实现持续的流程最佳化和高效的能源管理。各行业都能从中受益，获得更快的反应速度和更可靠的效能。随着製造业寻求提高生产力和减少营运浪费，边缘驱动的智慧为数位化效率提供了一条永续的途径，帮助企业提高长期可靠性和资产利用率。

高昂的实施和整合成本

面向工业环境的边缘人工智慧市场面临着一项重大挑战：高昂的部署和整合成本。部署边缘智慧需要对设备、感测器、平台和专业人员进行大量投资。许多现有的工业系统与人工智慧技术不相容，需要进行昂贵的现代化改造。对于中小企业而言，这种财务负担构成了大规模采用边缘人工智慧的障碍。此外，持续的维护、软体更新和资料处理成本也会对预算造成压力。这些经济限制使得企业难以充分利用边缘人工智慧的功能。因此，成本仍然是一个重要的限制因素，减缓了边缘人工智慧的普及速度，并阻碍了其在资源受限的工业领域的广泛应用。

智慧製造与工业4.0的扩展

工业4.0和智慧製造的日益普及，为工业环境中的边缘人工智慧市场创造了巨大的成长机会。边缘人工智慧为工厂提供即时分析、自动化决策和智慧控制，从而提高效率和营运灵活性。其整合有助于预测性维护、品质保证数位双胞胎仿真，进而建构更智慧的生产生态系统。随着各行业向数据驱动和自主系统转型，基于边缘的智慧将增强製造业的竞争力和永续性。这一演进支援无缝连接、提高生产效率和减少停机时间。因此，边缘人工智慧是工业4.0革命的核心，加速全球製造业的数位转型。

科技快速过时

技术变革的快速步伐对工业环境的边缘人工智慧市场构成重大威胁。随着人工智慧演算法、处理器和边缘设备的快速发展，现有设备可能很快就会过时。企业难以在不增加高成本下保持相容性并升级系统。这种持续的现代化需求可能导致营运中断和盈利下降。此外，缺乏统一的技术标准限制了互通性，使得跨不同平台的整合变得困难。过早过时的风险阻碍了一些公司进行大规模投资，减缓了边缘人工智慧系统的整体普及速度，并使其长期价值和稳定性受到不确定性。

新冠疫情的影响：

新冠疫情为工业环境的边缘人工智慧市场带来了挑战和机会。疫情初期，计划延期、供应链中断和技术投资减少，但也加速了自动化和数位化创新。随着各行业适应远端营运和劳动力限制，边缘人工智慧成为实现自主决策和即时洞察的关键工具。企业纷纷采用边缘运算来维持生产力、优化流程并最大限度地减少中断。在疫情恢復阶段，对智慧自主系统的需求显着成长。总而言之，儘管疫情初期造成了一定的阻碍，但最终增强了边缘人工智慧长期应用的前景。

预计在预测期内，硬体细分市场将占据最大的市场份额。

由于硬体在支援即时智慧和自动化方面发挥关键作用，预计在预测期内，硬体领域将占据最大的市场份额。感测器、处理器、网关和人工智慧加速器等核心组件对于本地数据采集和分析至关重要。这些设备能够提升工业环境中的运作速度、可靠性和效率。对智慧硬体的日益依赖使得预测性维护、流程优化和现场即时决策成为可能。先进晶片组和边缘运算设备的整合不断增强系统性能和扩充性。随着各产业向人工智慧驱动的营运模式转型，强大的硬体基础设施仍是实现高效边缘智慧的基础。

在预测期内，混合动力汽车细分市场将实现最高的复合年增长率。

由于其适应性强且均衡的架构，混合型解决方案预计将在预测期内呈现最高的成长率。将本地运算与云端功能结合，既能实现即时本地处理，又能利用云端基础设施进行进阶分析和储存。这种双管齐下的方法降低了延迟，提高了可扩展性，并增强了资料安全性。各行各业都能从即时边缘洞察和无所不在的集中式智慧中获益。混合系统的灵活性使其成为需要可靠性和效率的复杂工业运作的理想选择。随着企业对其数位生态系统进行现代化改造，混合边缘人工智慧解决方案正迅速获得市场认可，以实现效能和控制的最佳化。

占比最大的地区：

在整个预测期内，北美预计将保持最大的市场份额，这得益于其先进的基础设施和对数位转型的高度重视。该地区的关键产业，例如製造业、物流业和公共产业，正在积极采用边缘人工智慧来提高营运效率、预测性维护和自动化水准。领先的人工智慧和半导体公司的存在正在加速创新和大规模应用。对5G、物联网和智慧工厂技术的持续投资将进一步推动市场成长。有利于工业现代化的监管政策和政府措施也发挥关键作用。凭藉其强大的技术生态系统和早期应用文化，北美将继续在边缘人工智慧解决方案的采用和发展方面保持主导地位。

复合年增长率最高的地区：

由于工业扩张加速和对数位转型的坚定承诺，亚太地区预计将在预测期内实现最高的复合年增长率。中国、日本、印度和韩国等国家正在快速采用人工智慧、物联网和5G技术来实现工业营运的现代化。边缘人工智慧在该地区的应用支援自动化、预测性维护和更智慧的製造流程。政府主导的旨在促进工业4.0和发展智慧基础设施的倡议进一步推动了成长。在对先进技术投资不断增加和注重效率的推动下，亚太地区正成为边缘人工智慧应用领域最具活力且发展最快的市场。

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  • 公司简介
    • 对最多三家其他公司进行全面分析
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  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 介绍
  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球工业环境边缘人工智慧市场（按组件划分）

  • 介绍
  • 硬体
  • 软体
  • 整合支援服务

6. 按部署方式分類的全球工业环境边缘人工智慧市场

  • 介绍
  • 本地部署
  • 云
  • 杂交种

7. 全球工业环境边缘人工智慧市场（按应用划分）

  • 介绍
  • 预测性维护和故障预测
  • 视觉品质检查和缺陷检测
  • 即时流程优化
  • 自主机器人，机器控制
  • 工业资产追踪、状态监测
  • 职场安全与法规遵循
  • 智慧供应链、库存分析

8. 全球工业环境边缘人工智慧市场（依最终用户划分）

  • 介绍
  • 车
  • 电子和半导体
  • 食品/饮料
  • 製药
  • 航太/国防
  • 能源与公用事业
  • 化学
  • 金属和采矿
  • 物流/仓储

9. 全球工业环境边缘人工智慧市场（按地区划分）

  • 介绍
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十一章 公司简介

  • NVIDIA
  • Intel Corporation
  • GE Vernova
  • Siemens
  • Rockwell Automation
  • ABB
  • IBM
  • Advantech
  • Bosch
  • ClearBlade
  • CanaryBit
  • Emerson
  • MicroAI
  • ADLINK
  • Arm
Product Code: SMRC32137

According to Stratistics MRC, the Global Edge AI in Industrial Environments Market is accounted for $4.20 billion in 2025 and is expected to reach $10.33 billion by 2032 growing at a CAGR of 13.7% during the forecast period. Edge AI in industrial environments transforms operations by enabling intelligent data processing close to the data source. It minimizes latency, improves reliability, and reduces cloud dependency through on-site analytics of data collected from sensors and machines. This real-time capability supports predictive maintenance, process optimization, and early fault detection. Sectors like manufacturing, utilities, and logistics leverage Edge AI to achieve faster automation, better asset utilization, and enhanced safety. With local decision-making, industries maintain continuous productivity even under poor network conditions. The integration of Edge AI fosters a connected and adaptive industrial ecosystem, promoting efficiency, resilience, and intelligent operational control.

According to International Journal of Computational Engineering & Management (IJCEM), a study on Edge AI in Industry 4.0 found that Edge-based predictive maintenance systems reduced unplanned downtime by up to 30% in pilot deployments across automotive and aerospace sectors. The study emphasized the role of federated learning and edge inference in protecting sensitive operational data.

Market Dynamics:

Driver:

Rising need for predictive maintenance and operational efficiency

The need for improved operational performance and predictive maintenance is fueling the expansion of Edge AI in industrial applications. By processing real-time data from sensors and machinery, Edge AI can anticipate equipment malfunctions and prevent unexpected breakdowns. This predictive capability enhances uptime, reduces maintenance costs, and supports smarter production planning. Additionally, Edge AI enables continuous process optimization and efficient energy management. Industries benefit from faster responses and more reliable performance. As manufacturers aim to increase productivity and reduce operational waste, edge-driven intelligence offers a sustainable path to digital efficiency, helping enterprises achieve long-term reliability and higher asset utilization.

Restraint:

High implementation and integration costs

The Edge AI in Industrial Environments Market faces a key challenge from high setup and integration expenses. Implementing edge intelligence involves costly investments in devices, sensors, platforms, and trained professionals. Many existing industrial systems lack compatibility with AI technologies, requiring expensive modernization. For smaller firms, these financial demands hinder large-scale adoption. Additionally, ongoing costs for maintenance, software upgrades, and data handling further strain budgets. These economic constraints make it difficult for organizations to fully leverage Edge AI capabilities. As a result, cost remains a critical limiting factor, slowing its expansion and preventing widespread application in resource-constrained industrial sectors.

Opportunity:

Expansion of smart manufacturing and industry 4.0

The growing adoption of Industry 4.0 and smart manufacturing is creating major growth prospects for the Edge AI in Industrial Environments Market. Edge AI empowers factories with real-time analytics, automated decision-making, and intelligent control, improving efficiency and operational flexibility. Its integration facilitates predictive maintenance, quality assurance, and digital twin simulations, enabling smarter production ecosystems. With the industrial shift toward data-driven and autonomous systems, edge-based intelligence strengthens manufacturing competitiveness and sustainability. This evolution supports seamless connectivity, greater productivity, and reduced downtime. Consequently, Edge AI stands at the core of the Industry 4.0 revolution, accelerating the digital transformation of global manufacturing operations.

Threat:

Rapid technological obsolescence

The fast pace of technological change poses a significant threat to the Edge AI in Industrial Environments Market. As AI algorithms, processors, and edge devices evolve rapidly, existing installations may become obsolete within short periods. Organizations struggle to maintain compatibility and upgrade systems without incurring high costs. This constant need for modernization can lead to operational disruptions and reduced profitability. Additionally, the absence of unified technology standards limits interoperability, making integration difficult across diverse platforms. The risk of early obsolescence discourages some companies from large-scale investments, slowing overall adoption and creating uncertainty around the long-term value and stability of Edge AI systems.

Covid-19 Impact:

The COVID-19 pandemic created both challenges and opportunities for the Edge AI in Industrial Environments Market. While early phases saw project delays, supply chain interruptions, and reduced technology spending, the situation also accelerated automation and digital innovation. As industries adapted to remote operations and workforce limitations, Edge AI emerged as a vital tool for enabling autonomous decision-making and real-time insights. Organizations adopted edge computing to maintain productivity, optimize processes, and minimize disruptions. In the recovery stage, demand for intelligent, self-sufficient systems increased significantly. Overall, the pandemic served as an initial barrier but ultimately strengthened long-term growth prospects for Edge AI adoption.

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 its critical role in supporting real-time intelligence and automation. Core components such as sensors, processors, gateways, and AI accelerators are essential for collecting and analyzing data locally. These devices enhance operational speed, reliability, and efficiency in industrial settings. Growing reliance on intelligent hardware enables predictive maintenance, process optimization, and instant decision-making at the source. The integration of advanced chipsets and edge computing devices continues to expand system performance and scalability. As industries increasingly shift toward AI-enabled operations, robust hardware infrastructure remains the foundation for effective and efficient edge intelligence.

The hybrid segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the hybrid segment is predicted to witness the highest growth rate, due to its adaptive and balanced architecture. By merging on-premises computing with cloud capabilities, it enables real-time local processing while utilizing cloud infrastructure for advanced analytics and storage. This dual approach ensures reduced latency, improved scalability, and stronger data security. Industries benefit from both immediate edge insights and broader centralized intelligence. The flexibility of hybrid systems makes them ideal for complex industrial operations demanding reliability and efficiency. As companies modernize digital ecosystems, hybrid Edge AI solutions are rapidly gaining traction for optimized performance and control.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by advanced infrastructure and a strong focus on digital transformation. The region's leading industries-such as manufacturing, logistics, and utilities-actively deploy Edge AI to improve operational efficiency, predictive maintenance, and automation. The presence of major AI and semiconductor companies accelerates innovation and large-scale implementation. Continuous investments in 5G, IoT, and smart factory technologies further strengthen market growth. Favorable regulatory policies and government initiatives promoting industrial modernization also play a key role. With its robust technological ecosystem and early adoption culture, North America continues to lead in deploying and evolving Edge AI solutions.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR due to its accelerating industrial expansion and strong commitment to digital transformation. Nations like China, Japan, India, and South Korea are rapidly implementing AI, IoT, and 5G technologies to modernize industrial operations. Edge AI adoption in this region supports automation, predictive maintenance, and smarter manufacturing processes. Government-backed initiatives promoting Industry 4.0 and intelligent infrastructure development are further boosting growth. With increasing investments in advanced technologies and a focus on efficiency, Asia-Pacific is emerging as the most dynamic and fastest-evolving market for Edge AI applications.

Key players in the market

Some of the key players in Edge AI in Industrial Environments Market include NVIDIA, Intel Corporation, GE Vernova, Siemens, Rockwell Automation, ABB, IBM, Advantech, Bosch, ClearBlade, CanaryBit, Emerson, MicroAI, ADLINK and Arm.

Key Developments:

In October 2025, GE Vernova has signed a supply agreement with Greenvolt Power to provide 42 wind turbines for the Ialomita wind project in Romania. Under the deal, GE Vernova will supply, install, and commission its 6.1MW turbine with a 158-metre rotor for the 252MW project. The contract was finalised in the third quarter of this year, with turbine deliveries scheduled to begin in 2026.

In August 2025, Intel Corporation announced an agreement with the Trump Administration under which the US government will make an 8.9 billion US dollar investment in Intel common stock. The government agrees to purchase 433.3 million primary shares of Intel common stock at a price of 20.47 dollars per share, equivalent to a 9.9 percent stake in the company.

In August 2025, Nvidia and AMD have agreed to pay the US government 15% of Chinese revenues as part of an unprecedented deal to secure export licences to China. The US had previously banned the sale of powerful chips used in areas like artificial intelligence (AI) to China under export controls usually related to national security concerns. Under the agreement, Nvidia will pay 15% of its revenues from H20 chip sales in China to the US government.

Components Covered:

  • Hardware
  • Software
  • Integration & Support Services

Deployment Modes Covered:

  • On-Premises
  • Cloud
  • Hybrid

Applications Covered:

  • Predictive Maintenance & Failure Forecasting
  • Visual Quality Inspection & Defect Detection
  • Real-Time Process Optimization
  • Autonomous Robotics & Machine Control
  • Industrial Asset Tracking & Condition Monitoring
  • Workplace Safety & Regulatory Compliance
  • Intelligent Supply Chain & Inventory Analytics

End Users Covered:

  • Automotive
  • Electronics & Semiconductors
  • Food & Beverage
  • Pharmaceuticals
  • Aerospace & Defense
  • Energy & Utilities
  • Chemicals
  • Metals & Mining
  • Logistics & Warehousing

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 2024, 2025, 2026, 2028, and 2032
  • 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

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Edge AI in Industrial Environments Market, By Component

  • 5.1 Introduction
  • 5.2 Hardware
  • 5.3 Software
  • 5.4 Integration & Support Services

6 Global Edge AI in Industrial Environments Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 On-Premises
  • 6.3 Cloud
  • 6.4 Hybrid

7 Global Edge AI in Industrial Environments Market, By Application

  • 7.1 Introduction
  • 7.2 Predictive Maintenance & Failure Forecasting
  • 7.3 Visual Quality Inspection & Defect Detection
  • 7.4 Real-Time Process Optimization
  • 7.5 Autonomous Robotics & Machine Control
  • 7.6 Industrial Asset Tracking & Condition Monitoring
  • 7.7 Workplace Safety & Regulatory Compliance
  • 7.8 Intelligent Supply Chain & Inventory Analytics

8 Global Edge AI in Industrial Environments Market, By End User

  • 8.1 Introduction
  • 8.2 Automotive
  • 8.3 Electronics & Semiconductors
  • 8.4 Food & Beverage
  • 8.5 Pharmaceuticals
  • 8.6 Aerospace & Defense
  • 8.7 Energy & Utilities
  • 8.8 Chemicals
  • 8.9 Metals & Mining
  • 8.10 Logistics & Warehousing

9 Global Edge AI in Industrial Environments Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 NVIDIA
  • 11.2 Intel Corporation
  • 11.3 GE Vernova
  • 11.4 Siemens
  • 11.5 Rockwell Automation
  • 11.6 ABB
  • 11.7 IBM
  • 11.8 Advantech
  • 11.9 Bosch
  • 11.10 ClearBlade
  • 11.11 CanaryBit
  • 11.12 Emerson
  • 11.13 MicroAI
  • 11.14 ADLINK
  • 11.15 Arm

List of Tables

  • Table 1 Global Edge AI in Industrial Environments Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Edge AI in Industrial Environments Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Edge AI in Industrial Environments Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 4 Global Edge AI in Industrial Environments Market Outlook, By Software (2024-2032) ($MN)
  • Table 5 Global Edge AI in Industrial Environments Market Outlook, By Integration & Support Services (2024-2032) ($MN)
  • Table 6 Global Edge AI in Industrial Environments Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 7 Global Edge AI in Industrial Environments Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 8 Global Edge AI in Industrial Environments Market Outlook, By Cloud (2024-2032) ($MN)
  • Table 9 Global Edge AI in Industrial Environments Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 10 Global Edge AI in Industrial Environments Market Outlook, By Application (2024-2032) ($MN)
  • Table 11 Global Edge AI in Industrial Environments Market Outlook, By Predictive Maintenance & Failure Forecasting (2024-2032) ($MN)
  • Table 12 Global Edge AI in Industrial Environments Market Outlook, By Visual Quality Inspection & Defect Detection (2024-2032) ($MN)
  • Table 13 Global Edge AI in Industrial Environments Market Outlook, By Real-Time Process Optimization (2024-2032) ($MN)
  • Table 14 Global Edge AI in Industrial Environments Market Outlook, By Autonomous Robotics & Machine Control (2024-2032) ($MN)
  • Table 15 Global Edge AI in Industrial Environments Market Outlook, By Industrial Asset Tracking & Condition Monitoring (2024-2032) ($MN)
  • Table 16 Global Edge AI in Industrial Environments Market Outlook, By Workplace Safety & Regulatory Compliance (2024-2032) ($MN)
  • Table 17 Global Edge AI in Industrial Environments Market Outlook, By Intelligent Supply Chain & Inventory Analytics (2024-2032) ($MN)
  • Table 18 Global Edge AI in Industrial Environments Market Outlook, By End User (2024-2032) ($MN)
  • Table 19 Global Edge AI in Industrial Environments Market Outlook, By Automotive (2024-2032) ($MN)
  • Table 20 Global Edge AI in Industrial Environments Market Outlook, By Electronics & Semiconductors (2024-2032) ($MN)
  • Table 21 Global Edge AI in Industrial Environments Market Outlook, By Food & Beverage (2024-2032) ($MN)
  • Table 22 Global Edge AI in Industrial Environments Market Outlook, By Pharmaceuticals (2024-2032) ($MN)
  • Table 23 Global Edge AI in Industrial Environments Market Outlook, By Aerospace & Defense (2024-2032) ($MN)
  • Table 24 Global Edge AI in Industrial Environments Market Outlook, By Energy & Utilities (2024-2032) ($MN)
  • Table 25 Global Edge AI in Industrial Environments Market Outlook, By Chemicals (2024-2032) ($MN)
  • Table 26 Global Edge AI in Industrial Environments Market Outlook, By Metals & Mining (2024-2032) ($MN)
  • Table 27 Global Edge AI in Industrial Environments Market Outlook, By Logistics & Warehousing (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.