零售邊緣運算市場 - 全球產業規模、佔有率、趨勢、機會、預測：按組件、應用、組織規模、地區和競爭對手分類，2021-2031 年

全球零售邊緣運算市場預計將從 2025 年的 49.5 億美元大幅成長至 2031 年的 155.3 億美元，複合年成長率為 20.99%。

零售邊緣運算是指在資料來源（例如零售門市）附近部署資料處理能力，以實現即時分析和回應。推動這一市場成長的關鍵因素包括：即時庫存管理的必要性、門市對高度個人化客戶體驗日益成長的需求，以及降低向中央雲端平台發送大量數據所帶來的頻寬成本。根據美國零售聯合會 (NRF) 發布的《2025 年報告》，39% 的零售商預計將在三年內將超過 10% 的技術預算用於人工智慧 (AI)，這凸顯了零售商對需要強大邊緣架構的資料密集型應用的大量投資。

然而，儘管成長前景強勁，但分散式終端安全的挑戰卻嚴重阻礙了市場擴張。隨著零售商將其網路入口擴展到眾多實體門市，保護這種分散式基礎設施免受複雜網路威脅變得日益複雜且資源消耗巨大，這可能會阻礙該技術在整個行業的廣泛應用。

市場促進因素

推動零售業採用邊緣運算的主要動力之一是人工智慧驅動的自動化結帳系統日益普及。零售商正在部署電腦視覺和深度學習技術，用於無人收銀和即時庫存追蹤，這些任務需要即時數據處理以確保流暢的客戶體驗。集中式雲端伺服器會導致這些頻寬密集型操作出現無法接受的延遲，因此企業開始在本地處理影像和感測器資料。根據英偉達2024年2月發布的報告《零售和消費品行業人工智慧現狀：2024年趨勢》，69%的零售商將年度銷售成長歸功於人工智慧的應用，這鞏固了向本地化智慧基礎設施的轉變。

同時，利用物聯網技術擴展的智慧零售環境需要強大的本地處理能力來管理快速成長的連網設備數量。電子貨架標籤和智慧信標等技術持續產生資料流，需要即時同步才能實現精準定價和高效營運。在這種高密度終端環境中，邊緣節點需要充當本地閘道器，以減輕廣域網路的負載並實現門店內的快速更新。沃爾瑪在2024年6月發布的新聞稿顯示，計劃到2026年將數位貨架標籤擴展到2300家門市，這表明需要進行廣泛的硬體整合，而這依賴於邊緣運算的支援。此外，Nutanix 2024年的一份報告顯示，83%的零售商將混合多重雲端視為其首選的IT營運模式，這表明該行業正在策略性地向靈活分散式運算轉型。

市場挑戰

全球零售邊緣運算市場擴張的一大障礙是分散式終端的安全性問題。邊緣基礎設施的部署將單一零售門市轉變為資料處理節點，顯著擴大了攻擊面，使其超出傳統網路邊界。保護這種分散式架構需要在數千個實體位置實施一致且嚴格的標準，這比保護集中式雲端環境耗費更多資源。修補、監控和加強這些數量眾多且分佈廣泛的終端所帶來的固有運維複雜性，使得零售商對擴展邊緣部署猶豫不決，從而阻礙了市場成長。

零售業數位基礎設施面臨的威脅日益加劇，這進一步惡化了零售業對邊緣技術的抵觸情緒。美國零售聯合會 (NRF) 在 2025 年報告中指出，55% 的零售商觀察到數位和電子商務相關的詐欺行為有所增加，這表明網路攻擊明顯轉向技術剝削。這種風險的上升迫使企業將大量資金投入防禦和合規方面，而非基礎建設。因此，邊緣設備潛在漏洞帶來的財務和聲譽風險嚴重阻礙了零售邊緣技術的廣泛應用。

市場趨勢

一個值得關注的趨勢是，零售商正將電腦視覺技術應用於即時損失預防，這與以便捷性為導向的自助結帳系統截然不同。零售商正在整合基於邊緣的分析技術，以在本地處理高解析度影像，從而能夠立即檢測到複雜的竊盜行為，例如漏掃和貨架被清空。這避免了將資料傳送到雲端所帶來的延遲和頻寬成本。這種主動安全措施對於在高風險環境中減少庫存損失至關重要，因為在這些環境中需要即時介入。根據斑馬科技公司於2025年11月發布的第18屆年度全球購物者調查，87%的零售業領導者認為生成式人工智慧和自動化解決方案是損失預防的關鍵新興工具，凸顯了對智慧化、在地化監控基礎設施的需求。

同時，隨著自主移動機器人（AMR）在店內補貨領域的應用日益廣泛，強大的邊緣運算能力對於動態導航至關重要。與固定的物聯網感測器不同，這些移動單元依靠近邊緣處理來執行同步定位與地圖建置（SLAM）演算法，從而確保在消費者之間安全移動並管理庫存。本地運算能力使這些機器人即使在網路不穩定的情況下也能保持運作連續性並快速做出決策。根據Honeywell於2025年1月進行的「零售業人工智慧」調查，超過80%的零售商表示計劃在其營運中擴大自動化和人工智慧的應用，凸顯了零售業整體正策略性地依賴機器人輔助來提升門市業績。

目錄

第1章概述

第2章：調查方法

第3章執行摘要

第4章：客戶心聲

第5章：全球零售邊緣運算市場展望

• 市場規模及預測
  • 按金額
• 市佔率及預測
  • 按組件（硬體、軟體、服務）
  • 按應用領域（智慧城市、工業IoT、遠端監控、內容傳送、擴增實境、虛擬實境、其他）
  • 依組織規模（中小企業、大型企業）
  • 按地區
  • 按公司（2025 年）
• 市場地圖

第6章：北美零售邊緣運算市場展望

• 市場規模及預測
• 市佔率及預測
• 北美洲：國別分析
  • 美國
  • 加拿大
  • 墨西哥

第7章：歐洲零售邊緣運算市場展望

• 市場規模及預測
• 市佔率及預測
• 歐洲：國別分析
  • 德國
  • 法國
  • 英國
  • 義大利
  • 西班牙

第8章：亞太零售邊緣運算市場展望

• 市場規模及預測
• 市佔率及預測
• 亞太地區：國別分析
  • 中國
  • 印度
  • 日本
  • 韓國
  • 澳洲

第9章：中東和非洲零售邊緣運算市場展望

• 市場規模及預測
• 市佔率及預測
• 中東與非洲：國別分析
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國
  • 南非

第10章：南美洲零售邊緣運算市場展望

• 市場規模及預測
• 市佔率及預測
• 南美洲：國別分析
  • 巴西
  • 哥倫比亞
  • 阿根廷

第11章 市場動態

• 促進因素
• 任務

第12章 市場趨勢與發展

• 併購
• 產品發布
• 近期趨勢

第13章：全球零售邊緣運算市場：SWOT分析

第14章：波特五力分析

• 產業競爭
• 新進入者的潛力
• 供應商的議價能力
• 顧客權力
• 替代品的威脅

第15章 競爭格局

• Amazon.com, Inc.
• Microsoft Corporation
• IBM Corporation
• Intel Corporation
• Cisco Systems, Inc.
• Hewlett Packard Enterprise Company
• NVIDIA Corporation
• Google LLC
• Oracle Corporation
• Qualcomm Incorporated

第16章 策略建議

第17章：關於研究公司及免責聲明

The Global Retail Edge Computing Market is projected to expand significantly, rising from USD 4.95 Billion in 2025 to USD 15.53 Billion by 2031, demonstrating a compound annual growth rate (CAGR) of 20.99%. Retail Edge Computing involves placing data processing capabilities at or near the source of data generation, such as retail stores, to facilitate instant analysis and response. Key factors driving this market's growth include the essential requirement for real-time inventory oversight, the increasing desire for highly personalized customer experiences within stores, and the need to lower bandwidth expenses incurred by sending large data volumes to central cloud platforms. A 2025 report from the National Retail Federation indicated that 39% of retailers expected artificial intelligence to consume over 10% of their technology budgets within three years, highlighting the substantial investments in data-intensive applications that necessitate robust edge architectures.

However, despite these strong growth prospects, the market's expansion is notably hampered by challenges related to securing distributed endpoints. As retailers establish more network entry points across numerous physical locations, safeguarding this decentralized infrastructure from advanced cyber threats becomes increasingly intricate and demands significant resources, which could impede broad adoption throughout the industry.

Market Driver

A primary driver for edge computing adoption in retail is the increasing use of AI-powered automated checkout systems. Retailers are deploying computer vision and deep learning for cashier-less transactions and real-time inventory tracking, tasks that demand immediate data processing to ensure seamless customer experiences. Centralized cloud servers introduce unacceptable latency for these bandwidth-intensive operations, prompting businesses to process video and sensor data locally. NVIDIA's 'State of AI in Retail and CPG: 2024 Trends' report from February 2024 noted that 69% of retailers attributed increased annual revenue to AI implementation, affirming the shift towards localized, intelligent infrastructure.

Concurrently, the expansion of IoT-enabled smart retail environments demands strong local processing power to manage the surging number of connected devices. Technologies like electronic shelf labels and smart beacons continuously generate data streams that require instant synchronization for accurate pricing and efficient operations. This high density of endpoints necessitates edge nodes to function as local gateways, alleviating wide area network strain and facilitating swift updates across store locations. Walmart's June 2024 press release revealed plans to extend digital shelf labels to 2,300 stores by 2026, showcasing the extensive hardware integration that relies on edge support. Moreover, a 2024 Nutanix report indicated that 83% of retail organizations view hybrid multicloud as their preferred IT operating model, signaling the industry's strategic move toward flexible, distributed computing.

Market Challenge

A significant obstacle to the expansion of the Global Retail Edge Computing Market is the security of its distributed endpoints. Deploying edge infrastructure transforms individual retail stores into data processing nodes, substantially broadening the attack surface beyond conventional network boundaries. Securing this decentralized architecture demands consistent, stringent standards across thousands of physical sites, a task far more resource-intensive than safeguarding a centralized cloud. The inherent operational complexity of patching, monitoring, and hardening these numerous, fragmented endpoints makes retailers reluctant to scale their edge deployments, thus impeding market growth.

This reluctance is further exacerbated by the increasing threat landscape targeting the retail sector's digital infrastructure. The National Retail Federation reported in 2025 that 55% of retailers observed a rise in digital and e-commerce frauds, indicating a clear move towards technical exploitation. This heightened risk compels organizations to allocate considerable funds to defensive measures and compliance, rather than to infrastructure development. As a result, the financial and reputational dangers linked to potential vulnerabilities in edge devices significantly hinder the wider adoption of retail edge technologies.

Market Trends

A prominent trend involves deploying computer vision for real-time loss prevention, distinct from automated checkout systems focused on convenience. Retailers are integrating edge-based analytics to locally process high-definition video, allowing for instant detection of intricate theft behaviors like non-scans or shelf sweeping. This avoids the latency and bandwidth costs associated with cloud transmission. This proactive security measure is crucial for reducing shrinkage in high-risk settings where immediate intervention is necessary. Zebra Technologies' '18th Annual Global Shopper Study' from November 2025 reported that 87% of retail leaders identified Generative AI and automation solutions as vital emerging tools for loss prevention, emphasizing the need for intelligent, localized monitoring infrastructures.

Concurrently, the growing adoption of Autonomous Mobile Robots (AMR) for in-store fulfillment necessitates robust edge computing to facilitate dynamic navigation. Unlike stationary IoT sensors, these mobile units depend on near-edge processing to execute Simultaneous Localization and Mapping (SLAM) algorithms, ensuring safe movement among shoppers while managing inventory. Localized computing power enables these robotic fleets to sustain operational continuity and make rapid decisions, even amidst network inconsistencies. Honeywell's 'AI in Retail Survey' from January 2025 indicated that over 80% of retailers plan to increase their use of automation and artificial intelligence across operations, reflecting the sector's strategic dependence on robotic assistance to enhance store performance.

Key Market Players

• Amazon.com, Inc.
• Microsoft Corporation
• IBM Corporation
• Intel Corporation
• Cisco Systems, Inc.
• Hewlett Packard Enterprise Company
• NVIDIA Corporation
• Google LLC
• Oracle Corporation
• Qualcomm Incorporated

Report Scope

In this report, the Global Retail Edge Computing Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Retail Edge Computing Market, By Component

• Hardware
• Software
• Services

Retail Edge Computing Market, By Application

• Smart Cities
• Industrial Internet of Things
• Remote Monitoring
• Content Delivery
• Augmented Reality
• Virtual Reality
• Others

Retail Edge Computing Market, By Organization Size

• Small & Medium Enterprises
• Large Enterprises

Retail Edge Computing Market, By Region

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
• South America
  • Brazil
  • Argentina
  • Colombia
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Retail Edge Computing Market.

Available Customizations:

Global Retail Edge Computing Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Retail Edge Computing Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Component (Hardware, Software, Services)
  • 5.2.2. By Application (Smart Cities, Industrial Internet of Things, Remote Monitoring, Content Delivery, Augmented Reality, Virtual Reality, Others)
  • 5.2.3. By Organization Size (Small & Medium Enterprises, Large Enterprises)
  • 5.2.4. By Region
  • 5.2.5. By Company (2025)
• 5.3. Market Map

6. North America Retail Edge Computing Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Component
  • 6.2.2. By Application
  • 6.2.3. By Organization Size
  • 6.2.4. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States Retail Edge Computing Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Component
      • 6.3.1.2.2. By Application
      • 6.3.1.2.3. By Organization Size
  • 6.3.2. Canada Retail Edge Computing Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Component
      • 6.3.2.2.2. By Application
      • 6.3.2.2.3. By Organization Size
  • 6.3.3. Mexico Retail Edge Computing Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Component
      • 6.3.3.2.2. By Application
      • 6.3.3.2.3. By Organization Size

7. Europe Retail Edge Computing Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Component
  • 7.2.2. By Application
  • 7.2.3. By Organization Size
  • 7.2.4. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany Retail Edge Computing Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Component
      • 7.3.1.2.2. By Application
      • 7.3.1.2.3. By Organization Size
  • 7.3.2. France Retail Edge Computing Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Component
      • 7.3.2.2.2. By Application
      • 7.3.2.2.3. By Organization Size
  • 7.3.3. United Kingdom Retail Edge Computing Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Component
      • 7.3.3.2.2. By Application
      • 7.3.3.2.3. By Organization Size
  • 7.3.4. Italy Retail Edge Computing Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Component
      • 7.3.4.2.2. By Application
      • 7.3.4.2.3. By Organization Size
  • 7.3.5. Spain Retail Edge Computing Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Component
      • 7.3.5.2.2. By Application
      • 7.3.5.2.3. By Organization Size

8. Asia Pacific Retail Edge Computing Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Component
  • 8.2.2. By Application
  • 8.2.3. By Organization Size
  • 8.2.4. By Country
• 8.3. Asia Pacific: Country Analysis
  • 8.3.1. China Retail Edge Computing Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Component
      • 8.3.1.2.2. By Application
      • 8.3.1.2.3. By Organization Size
  • 8.3.2. India Retail Edge Computing Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Component
      • 8.3.2.2.2. By Application
      • 8.3.2.2.3. By Organization Size
  • 8.3.3. Japan Retail Edge Computing Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Component
      • 8.3.3.2.2. By Application
      • 8.3.3.2.3. By Organization Size
  • 8.3.4. South Korea Retail Edge Computing Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Component
      • 8.3.4.2.2. By Application
      • 8.3.4.2.3. By Organization Size
  • 8.3.5. Australia Retail Edge Computing Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Component
      • 8.3.5.2.2. By Application
      • 8.3.5.2.3. By Organization Size

9. Middle East & Africa Retail Edge Computing Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Component
  • 9.2.2. By Application
  • 9.2.3. By Organization Size
  • 9.2.4. By Country
• 9.3. Middle East & Africa: Country Analysis
  • 9.3.1. Saudi Arabia Retail Edge Computing Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Component
      • 9.3.1.2.2. By Application
      • 9.3.1.2.3. By Organization Size
  • 9.3.2. UAE Retail Edge Computing Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Component
      • 9.3.2.2.2. By Application
      • 9.3.2.2.3. By Organization Size
  • 9.3.3. South Africa Retail Edge Computing Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Component
      • 9.3.3.2.2. By Application
      • 9.3.3.2.3. By Organization Size

10. South America Retail Edge Computing Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Component
  • 10.2.2. By Application
  • 10.2.3. By Organization Size
  • 10.2.4. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil Retail Edge Computing Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Component
      • 10.3.1.2.2. By Application
      • 10.3.1.2.3. By Organization Size
  • 10.3.2. Colombia Retail Edge Computing Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Component
      • 10.3.2.2.2. By Application
      • 10.3.2.2.3. By Organization Size
  • 10.3.3. Argentina Retail Edge Computing Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Component
      • 10.3.3.2.2. By Application
      • 10.3.3.2.3. By Organization Size

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Global Retail Edge Computing Market: SWOT Analysis

14. Porter's Five Forces Analysis

• 14.1. Competition in the Industry
• 14.2. Potential of New Entrants
• 14.3. Power of Suppliers
• 14.4. Power of Customers
• 14.5. Threat of Substitute Products

15. Competitive Landscape

• 15.1. Amazon.com, Inc.
  • 15.1.1. Business Overview
  • 15.1.2. Products & Services
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel
  • 15.1.5. SWOT Analysis
• 15.2. Microsoft Corporation
• 15.3. IBM Corporation
• 15.4. Intel Corporation
• 15.5. Cisco Systems, Inc.
• 15.6. Hewlett Packard Enterprise Company
• 15.7. NVIDIA Corporation
• 15.8. Google LLC
• 15.9. Oracle Corporation
• 15.10. Qualcomm Incorporated

16. Strategic Recommendations

17. About Us & Disclaimer