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市場調查報告書
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2007827

2034年物流自動化人工智慧市場預測：按組件、部署模式、技術、企業規模、應用、最終用戶和地區分類的全球分析

AI in Logistics Automation Market Forecasts to 2034 - Global Analysis By Component (Hardware, Software and Services), Deployment Mode, Technology, Enterprise Size, 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，全球物流自動化人工智慧市場規模將達到 220 億美元，在預測期內將以 39.1% 的複合年成長率成長，到 2034 年將達到 4,257 億美元。

在物流自動化領域，人工智慧（AI）指的是利用人工智慧技術來簡化、最佳化和自動化物流及供應鏈營運。它透過運用機器學習、電腦視覺和預測分析等技術，提昇路線最佳化、需求預測、倉庫管理、庫存追蹤和自主運輸等任務的效率。透過即時分析大量營運數據，人工智慧能夠加快決策速度、降低營運成本並最大限度地減少人為錯誤，從而提高整個物流網路的交付效率、可視性和應對力，最終支援更敏捷和智慧化的供應鏈管理。

對營運效率和成本降低的需求日益成長

物流業面臨巨大的壓力，需要精簡營運流程並降低與人事費用、燃料和庫存管理相關的不斷上漲的成本。人工智慧驅動的自動化透過路線最佳化、倉庫重複性任務自動化以及提高需求預測的準確性，提供了極具吸引力的解決方案。企業正擴大採用自主機器人和人工智慧驅動的倉庫管理系統，以加快訂單處理速度並最大限度地減少錯誤。在追求更精簡的供應鏈和應對日益成長的電子商務交易量的驅動下，物流供應商被迫採用能夠以更低的營運成本提供更高處理能力的人工智慧解決方案。

初始投資高且整合複雜

實施人工智慧驅動的物流自動化需要前期在硬體、軟體和基礎設施升級方面進行大量投資。許多企業，尤其是中小企業，都面臨著高昂的總體擁有成本以及將新的人工智慧系統整合到現有IT基礎設施中的複雜性。無縫部署和資料遷移通常需要專業的技術知識，這可能成為一大障礙。此外，缺乏標準化平台以及不同供應商自動化系統之間互通性的擔憂，都可能導致計劃延期和投資報酬率（ROI）的不確定性。

生成式人工智慧與數位雙胞胎技術的發展

生成式人工智慧正成為一股變革性力量，能夠實現先進的供應鏈模擬、情境規劃和自主決策。數位雙胞胎技術的應用使物流公司能夠創建其網路的虛擬副本，從而在不中斷實際營運的情況下實現即時監控、預測性維護和營運最佳化。這些技術在風險管理和策略規劃方面提供了前所未有的能力。隨著企業對應對市場波動的敏捷性要求越來越高，生成式人工智慧與數位雙胞胎的融合為物流自動化領域的創新和差異化競爭帶來了巨大的機會。

網路安全與資料隱私風險

隨著自動化物流系統從物聯網感測器到雲端平台的互聯程度不斷提高，網路威脅的攻擊面也不斷擴大。安全漏洞可能導致嚴重的業務中斷、敏感供應鏈資料被盜以及經濟損失。此外，依賴海量資料集訓練人工智慧模型也引發了人們對資料隱私和合規性（例如GDPR）的擔憂。確保強大的網路安全通訊協定、資料加密和安全的網路架構至關重要，但挑戰依然存在。針對大型物流業者的大規模網路攻擊可能會破壞信任，並延緩互聯互通的人工智慧主導解決方案的普及。

新冠疫情的影響

新冠疫情大大推動了人工智慧在物流自動化領域的應用，同時也揭露了全球供應鏈的脆弱性。封鎖措施和人手不足迫使企業加快對自主機器人和非接觸式配送的投資，以維持營運。這場危機凸顯了預測分析在應對需求波動和供應中斷方面的必要性。疫情初期，硬體普及速度有所放緩，但疫情後的情況推動了硬體應用的激增，進而促使物流網路戰略向更具韌性、自動化和去中心化的方向轉變，以降低未來全球供應鏈中斷帶來的風險。

在預測期內，軟體產業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，因為人工智慧和機器學習平台在協調複雜的物流運營中發揮核心作用。在倉儲和運輸管理系統中，人工智慧的應用日益廣泛，以實現即時最佳化和決策。向雲端和混合部署模式的轉變提供了擴充性和柔軟性，從而方便用戶獲取先進的軟體解決方案。

在預測期內，醫療保健和製藥業預計將呈現最高的複合年成長率。

在預測期內，醫療保健和製藥業預計將呈現最高的成長率。人工智慧驅動的自動化技術可提供即時監控、溫度偏差預測分析和端到端可追溯性，確保符合嚴格的監管標準。個人化醫療和高價值基因療法的興起，使得安全無誤的交付至關重要。醫院和藥房正在部署自主機器人和人工智慧驅動的庫存管理系統，以有效管理易損庫存、減少廢棄物並確保病患安全。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於其對技術創新的高度重視以及先進自動化技術的快速普及。尤其值得一提的是，美國在自主配送機器人、人工智慧驅動的車輛管理以及用於供應鏈規劃的生成式人工智慧的開發和部署方面發揮著主導作用。強大的技術供應商生態系統以及大型零售商和第三方物流公司對相關技術的早期採用，正在推動這一成長。

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，這主要得益於快速的工業化、蓬勃發展的電子商務以及對智慧製造的大規模投資。中國、日本和韓國等國家在採用機器人和人工智慧技術以應對勞動力短缺和最佳化供應鏈方面處於領先地位。該地區作為全球製造地，對自動化倉儲解決方案和先進物流基礎設施的需求龐大。

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    • 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
  • 成長動力、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：全球物流自動化人工智慧市場：按組件分類

  • 硬體
    • 自主移動機器人（AMR）
    • 自動導引運輸車（AGV）
    • 無人機和飛機
    • 感測器和物聯網設備
    • 分類系統
    • 穿戴式裝置
  • 軟體
    • 人工智慧和機器學習平台
    • 倉庫管理系統（WMS）
    • 運輸管理系統（TMS）
    • 供應鏈規劃與最佳化
    • 電腦視覺軟體
    • 預測分析軟體
  • 服務
    • 專業服務
    • 託管服務
    • 整合與部署

第6章：全球物流自動化人工智慧市場：依部署模式分類

  • 基於雲端的
  • 現場
  • 混合

第7章：全球物流自動化人工智慧市場：按技術分類

  • 機器學習和深度學習
  • 電腦視覺
  • 自然語言處理（NLP）
  • 人工智慧世代
  • 自主系統與機器人
  • 預測分析
  • 數位雙胞胎

第8章：全球物流自動化人工智慧市場：依公司規模分類

  • 中小企業
  • 主要企業

第9章：全球物流自動化人工智慧市場：按應用領域分類

  • 倉庫自動化
    • 自主揀貨和包裝
    • 庫存管理與最佳化
    • 分類和運輸
    • 自動化倉庫系統
  • 車輛管理和自動駕駛車輛
    • 路線最佳化
    • 預測性保護
    • 自動駕駛卡車和送貨車輛
  • 最後一公里配送
    • 自主配送機器人
    • 無人機配送
    • 動態路線設定和調度
  • 供應鏈規劃與預測
    • 需求預測
    • 供應商協作
    • 風險管理
  • 客戶服務與客戶體驗
    • 人工智慧聊天機器人
    • 即時追蹤和可視化
  • 跨境物流與清關自動化

第10章：全球物流自動化人工智慧市場：以最終用戶分類

  • 零售與電子商務
  • 製造業
  • 醫療和藥品
  • 車
  • 食品/飲料
  • 第三方物流（3PL）貨運代理
  • 航太/國防
  • 消費品
  • 石油和天然氣

第11章：全球物流自動化人工智慧市場：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12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13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟、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14章：公司簡介

  • NVIDIA Corporation
  • Intel Corporation
  • IBM Corporation
  • Microsoft Corporation
  • Amazon Web Services, Inc.
  • Alphabet Inc.
  • SAP SE
  • Oracle Corporation
  • Siemens AG
  • ABB Ltd.
  • Honeywell International Inc.
  • Zebra Technologies Corporation
  • Rockwell Automation, Inc.
  • Daifuku Co., Ltd.
  • Dematic Corp.
Product Code: SMRC34702

According to Stratistics MRC, the Global AI in Logistics Automation Market is accounted for $22.0 billion in 2026 and is expected to reach $425.7 billion by 2034 growing at a CAGR of 39.1% during the forecast period. AI in Logistics Automation is the use of artificial intelligence technologies to streamline, optimize, and automate logistics and supply chain operations. It leverages machine learning, computer vision, and predictive analytics to enhance tasks such as route optimization, demand forecasting, warehouse management, inventory tracking, and autonomous transportation. By analyzing large volumes of operational data in real time, AI enables faster decision-making, reduces operational costs, minimizes human errors, and improves delivery efficiency, visibility, and responsiveness across logistics networks, supporting more agile and intelligent supply chain management.

Market Dynamics:

Driver:

Rising demand for operational efficiency and cost reduction

The logistics sector faces immense pressure to streamline operations and reduce escalating costs associated with labor, fuel, and inventory management. AI-powered automation offers a compelling solution by optimizing routes, automating repetitive warehouse tasks, and improving demand forecasting. Companies are increasingly deploying autonomous mobile robots and AI-driven warehouse management systems to accelerate order fulfillment and minimize errors. The pursuit of leaner supply chains, coupled with the need to handle growing e-commerce volumes, is forcing logistics providers to adopt AI solutions that can deliver higher throughput with lower operational expenditure.

Restraint:

High initial investment and integration complexity

Implementing AI-driven logistics automation requires significant upfront capital expenditure for hardware, software, and infrastructure upgrades. Many organizations, particularly small and medium-sized enterprises, struggle with the high total cost of ownership and the complexity of integrating new AI systems with legacy IT infrastructure. The process often demands specialized technical expertise for seamless deployment and data migration, which can be a barrier. Additionally, the lack of standardized platforms and concerns about interoperability between different automated systems from various vendors can lead to project delays and uncertainty regarding return on investment.

Opportunity:

Growth of generative AI and digital twins

Generative AI is emerging as a transformative force, enabling advanced supply chain simulation, scenario planning, and autonomous decision-making. The adoption of digital twin technology allows logistics companies to create virtual replicas of their networks, facilitating real-time monitoring, predictive maintenance, and operational optimization without disrupting physical operations. These technologies offer unprecedented capabilities for risk management and strategic planning. As businesses seek greater agility to navigate market volatility, the integration of generative AI and digital twins presents a significant opportunity for innovation and competitive differentiation in logistics automation.

Threat:

Cybersecurity and data privacy risks

The increasing connectivity of automated logistics systems from IoT sensors to cloud-based platforms expands the attack surface for cyber threats. A security breach can lead to significant operational disruptions, theft of sensitive supply chain data, and financial losses. The reliance on vast datasets for training AI models also raises concerns about data privacy and compliance with regulations like GDPR. Ensuring robust cybersecurity protocols, data encryption, and secure network architecture is critical but challenging. A major cyberattack on a key logistics player could undermine trust and slow down the adoption of interconnected AI-driven solutions.

Covid-19 Impact

The COVID-19 pandemic acted as a powerful catalyst for AI in logistics automation, exposing vulnerabilities in global supply chains. Lockdowns and labor shortages forced companies to accelerate investments in autonomous robots and contactless delivery to maintain operations. The crisis highlighted the critical need for predictive analytics to manage demand volatility and supply disruptions. While initial disruptions slowed hardware deployments, the post-pandemic landscape has seen a surge in adoption, with a strategic shift toward resilient, automated, and decentralized logistics networks to mitigate risks from future global disruptions.

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, driven by the central role of AI and machine learning platforms in orchestrating complex logistics operations. Warehouse and transportation management systems are increasingly incorporating AI to enable real-time optimization and decision-making. The shift towards cloud-based and hybrid deployment models offers scalability and flexibility, making advanced software solutions accessible.

The healthcare and pharmaceuticals segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare and pharmaceuticals segment is predicted to witness the highest growth rate. AI-powered automation provides real-time monitoring, predictive analytics for temperature excursions, and end-to-end traceability to ensure compliance with stringent regulatory standards. The rise of personalized medicine and high-value gene therapies necessitates secure, error-free delivery. Hospitals and pharmacies are adopting autonomous robots and AI-driven inventory systems to manage sensitive inventories efficiently, reduce waste, and ensure patient safety.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by a strong focus on technological innovation and high adoption rates of advanced automation. The United States, in particular, is a leader in developing and deploying autonomous delivery robots, AI-driven fleet management, and generative AI for supply chain planning. A robust ecosystem of technology providers and early adoption by major retail and 3PL companies drive this growth.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by rapid industrialization, a booming e-commerce sector, and massive investments in smart manufacturing. Countries like China, Japan, and South Korea are at the forefront of adopting robotics and AI to address labor shortages and enhance supply chain efficiency. The region serves as a global manufacturing hub, creating immense demand for automated warehouse solutions and advanced logistics infrastructure.

Key players in the market

Some of the key players in AI in Logistics Automation Market include NVIDIA Corporation, Intel Corporation, IBM Corporation, Microsoft Corporation, Amazon Web Services, Inc., Alphabet Inc., SAP SE, Oracle Corporation, Siemens AG, ABB Ltd., Honeywell International Inc., Zebra Technologies Corporation, Rockwell Automation, Inc., Daifuku Co., Ltd., and Dematic Corp.

Key Developments:

In March 2026, NVIDIA and Emerald AI announced that they are working with AES, Constellation, Invenergy, NextEra Energy, Nscale Energy & Power and Vistra to power and advance a new class of AI factories that connect to the grid faster, generate valuable AI tokens and intelligence, and operate as flexible energy assets that can support the grid.

In March 2026, Intel announced the launch of its new Intel(R) Core(TM) Ultra 200HX Plus series mobile processors, giving gamers and professionals new high-performance options in the Core Ultra 200 series family. Optimized for advanced gaming, streaming, content creation, and workstation use, the Intel Core Ultra 200HX Plus series introduces two new processors - Intel Core Ultra 9 290HX Plus and Intel Core Ultra 7 270HX Plus.

Components Covered:

  • Hardware
  • Software
  • Services

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises
  • Hybrid

Technologies Covered:

  • Machine Learning and Deep Learning
  • Computer Vision
  • Natural Language Processing (NLP)
  • Generative AI
  • Autonomous Systems and Robotics
  • Predictive Analytics
  • Digital Twins

Enterprise Sizes Covered:

  • Small and Medium Enterprises (SMEs)
  • Large Enterprises

Applications Covered:

  • Warehouse Automation
  • Fleet Management and Autonomous Vehicles
  • Last-Mile Delivery
  • Supply Chain Planning and Forecasting
  • Customer Service and Experience
  • Cross-Border Logistics and Customs Automation

End Users Covered:

  • Retail and E-Commerce
  • Manufacturing
  • Healthcare and Pharmaceuticals
  • Automotive
  • Food and Beverage
  • Third-Party Logistics (3PL) and Freight Forwarders
  • Aerospace and Defense
  • Consumer Goods
  • Oil and Gas

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 Logistics Automation Market, By Component

  • 5.1 Hardware
    • 5.1.1 Autonomous Mobile Robots (AMRs)
    • 5.1.2 Automated Guided Vehicles (AGVs)
    • 5.1.3 Drones and Aerial Vehicles
    • 5.1.4 Sensors and IoT Devices
    • 5.1.5 Sorting and Picking Systems
    • 5.1.6 Wearable Devices
  • 5.2 Software
    • 5.2.1 AI and Machine Learning Platforms
    • 5.2.2 Warehouse Management Systems (WMS)
    • 5.2.3 Transportation Management Systems (TMS)
    • 5.2.4 Supply Chain Planning and Optimization
    • 5.2.5 Computer Vision Software
    • 5.2.6 Predictive Analytics Software
  • 5.3 Services
    • 5.3.1 Professional Services
    • 5.3.2 Managed Services
    • 5.3.3 Integration and Deployment

6 Global AI in Logistics Automation Market, By Deployment Mode

  • 6.1 Cloud-Based
  • 6.2 On-Premises
  • 6.3 Hybrid

7 Global AI in Logistics Automation Market, By Technology

  • 7.1 Machine Learning and Deep Learning
  • 7.2 Computer Vision
  • 7.3 Natural Language Processing (NLP)
  • 7.4 Generative AI
  • 7.5 Autonomous Systems and Robotics
  • 7.6 Predictive Analytics
  • 7.7 Digital Twins

8 Global AI in Logistics Automation Market, By Enterprise Size

  • 8.1 Small and Medium Enterprises (SMEs)
  • 8.2 Large Enterprises

9 Global AI in Logistics Automation Market, By Application

  • 9.1 Warehouse Automation
    • 9.1.1 Autonomous Picking and Packing
    • 9.1.2 Inventory Management and Optimization
    • 9.1.3 Sorting and Conveying
    • 9.1.4 Automated Storage and Retrieval
  • 9.2 Fleet Management and Autonomous Vehicles
    • 9.2.1 Route Optimization
    • 9.2.2 Predictive Maintenance
    • 9.2.3 Autonomous Trucks and Delivery Vehicles
  • 9.3 Last-Mile Delivery
    • 9.3.1 Autonomous Delivery Robots
    • 9.3.2 Drone Delivery
    • 9.3.3 Dynamic Routing and Scheduling
  • 9.4 Supply Chain Planning and Forecasting
    • 9.4.1 Demand Forecasting
    • 9.4.2 Supplier Collaboration
    • 9.4.3 Risk Management
  • 9.5 Customer Service and Experience
    • 9.5.1 AI-Powered Chatbots
    • 9.5.2 Real-Time Tracking and Visibility
  • 9.6 Cross-Border Logistics and Customs Automation

10 Global AI in Logistics Automation Market, By End User

  • 10.1 Retail and E-Commerce
  • 10.2 Manufacturing
  • 10.3 Healthcare and Pharmaceuticals
  • 10.4 Automotive
  • 10.5 Food and Beverage
  • 10.6 Third-Party Logistics (3PL) and Freight Forwarders
  • 10.7 Aerospace and Defense
  • 10.8 Consumer Goods
  • 10.9 Oil and Gas

11 Global AI in Logistics Automation Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 NVIDIA Corporation
  • 14.2 Intel Corporation
  • 14.3 IBM Corporation
  • 14.4 Microsoft Corporation
  • 14.5 Amazon Web Services, Inc.
  • 14.6 Alphabet Inc.
  • 14.7 SAP SE
  • 14.8 Oracle Corporation
  • 14.9 Siemens AG
  • 14.10 ABB Ltd.
  • 14.11 Honeywell International Inc.
  • 14.12 Zebra Technologies Corporation
  • 14.13 Rockwell Automation, Inc.
  • 14.14 Daifuku Co., Ltd.
  • 14.15 Dematic Corp.

List of Tables

  • Table 1 Global AI in Logistics Automation Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Logistics Automation Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Logistics Automation Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 4 Global AI in Logistics Automation Market Outlook, By Autonomous Mobile Robots (AMRs) (2023-2034) ($MN)
  • Table 5 Global AI in Logistics Automation Market Outlook, By Automated Guided Vehicles (AGVs) (2023-2034) ($MN)
  • Table 6 Global AI in Logistics Automation Market Outlook, By Drones and Aerial Vehicles (2023-2034) ($MN)
  • Table 7 Global AI in Logistics Automation Market Outlook, By Sensors and IoT Devices (2023-2034) ($MN)
  • Table 8 Global AI in Logistics Automation Market Outlook, By Sorting and Picking Systems (2023-2034) ($MN)
  • Table 9 Global AI in Logistics Automation Market Outlook, By Wearable Devices (2023-2034) ($MN)
  • Table 10 Global AI in Logistics Automation Market Outlook, By Software (2023-2034) ($MN)
  • Table 11 Global AI in Logistics Automation Market Outlook, By AI and Machine Learning Platforms (2023-2034) ($MN)
  • Table 12 Global AI in Logistics Automation Market Outlook, By Warehouse Management Systems (WMS) (2023-2034) ($MN)
  • Table 13 Global AI in Logistics Automation Market Outlook, By Transportation Management Systems (TMS) (2023-2034) ($MN)
  • Table 14 Global AI in Logistics Automation Market Outlook, By Supply Chain Planning and Optimization (2023-2034) ($MN)
  • Table 15 Global AI in Logistics Automation Market Outlook, By Computer Vision Software (2023-2034) ($MN)
  • Table 16 Global AI in Logistics Automation Market Outlook, By Predictive Analytics Software (2023-2034) ($MN)
  • Table 17 Global AI in Logistics Automation Market Outlook, By Services (2023-2034) ($MN)
  • Table 18 Global AI in Logistics Automation Market Outlook, By Professional Services (2023-2034) ($MN)
  • Table 19 Global AI in Logistics Automation Market Outlook, By Managed Services (2023-2034) ($MN)
  • Table 20 Global AI in Logistics Automation Market Outlook, By Integration and Deployment (2023-2034) ($MN)
  • Table 21 Global AI in Logistics Automation Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 22 Global AI in Logistics Automation Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 23 Global AI in Logistics Automation Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 24 Global AI in Logistics Automation Market Outlook, By Hybrid (2023-2034) ($MN)
  • Table 25 Global AI in Logistics Automation Market Outlook, By Technology (2023-2034) ($MN)
  • Table 26 Global AI in Logistics Automation Market Outlook, By Machine Learning and Deep Learning (2023-2034) ($MN)
  • Table 27 Global AI in Logistics Automation Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 28 Global AI in Logistics Automation Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 29 Global AI in Logistics Automation Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 30 Global AI in Logistics Automation Market Outlook, By Autonomous Systems and Robotics (2023-2034) ($MN)
  • Table 31 Global AI in Logistics Automation Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 32 Global AI in Logistics Automation Market Outlook, By Digital Twins (2023-2034) ($MN)
  • Table 33 Global AI in Logistics Automation Market Outlook, By Enterprise Size (2023-2034) ($MN)
  • Table 34 Global AI in Logistics Automation Market Outlook, By Small and Medium Enterprises (SMEs) (2023-2034) ($MN)
  • Table 35 Global AI in Logistics Automation Market Outlook, By Large Enterprises (2023-2034) ($MN)
  • Table 36 Global AI in Logistics Automation Market Outlook, By Application (2023-2034) ($MN)
  • Table 37 Global AI in Logistics Automation Market Outlook, By Warehouse Automation (2023-2034) ($MN)
  • Table 38 Global AI in Logistics Automation Market Outlook, By Autonomous Picking and Packing (2023-2034) ($MN)
  • Table 39 Global AI in Logistics Automation Market Outlook, By Inventory Management and Optimization (2023-2034) ($MN)
  • Table 40 Global AI in Logistics Automation Market Outlook, By Sorting and Conveying (2023-2034) ($MN)
  • Table 41 Global AI in Logistics Automation Market Outlook, By Automated Storage and Retrieval (2023-2034) ($MN)
  • Table 42 Global AI in Logistics Automation Market Outlook, By Fleet Management and Autonomous Vehicles (2023-2034) ($MN)
  • Table 43 Global AI in Logistics Automation Market Outlook, By Route Optimization (2023-2034) ($MN)
  • Table 44 Global AI in Logistics Automation Market Outlook, By Predictive Maintenance (2023-2034) ($MN)
  • Table 45 Global AI in Logistics Automation Market Outlook, By Autonomous Trucks and Delivery Vehicles (2023-2034) ($MN)
  • Table 46 Global AI in Logistics Automation Market Outlook, By Last-Mile Delivery (2023-2034) ($MN)
  • Table 47 Global AI in Logistics Automation Market Outlook, By Autonomous Delivery Robots (2023-2034) ($MN)
  • Table 48 Global AI in Logistics Automation Market Outlook, By Drone Delivery (2023-2034) ($MN)
  • Table 49 Global AI in Logistics Automation Market Outlook, By Dynamic Routing and Scheduling (2023-2034) ($MN)
  • Table 50 Global AI in Logistics Automation Market Outlook, By Supply Chain Planning and Forecasting (2023-2034) ($MN)
  • Table 51 Global AI in Logistics Automation Market Outlook, By Demand Forecasting (2023-2034) ($MN)
  • Table 52 Global AI in Logistics Automation Market Outlook, By Supplier Collaboration (2023-2034) ($MN)
  • Table 53 Global AI in Logistics Automation Market Outlook, By Risk Management (2023-2034) ($MN)
  • Table 54 Global AI in Logistics Automation Market Outlook, By Customer Service and Experience (2023-2034) ($MN)
  • Table 55 Global AI in Logistics Automation Market Outlook, By AI-Powered Chatbots (2023-2034) ($MN)
  • Table 56 Global AI in Logistics Automation Market Outlook, By Real-Time Tracking and Visibility (2023-2034) ($MN)
  • Table 57 Global AI in Logistics Automation Market Outlook, By Cross-Border Logistics and Customs Automation (2023-2034) ($MN)
  • Table 58 Global AI in Logistics Automation Market Outlook, By End User (2023-2034) ($MN)
  • Table 59 Global AI in Logistics Automation Market Outlook, By Retail and E-Commerce (2023-2034) ($MN)
  • Table 60 Global AI in Logistics Automation Market Outlook, By Manufacturing (2023-2034) ($MN)
  • Table 61 Global AI in Logistics Automation Market Outlook, By Healthcare and Pharmaceuticals (2023-2034) ($MN)
  • Table 62 Global AI in Logistics Automation Market Outlook, By Automotive (2023-2034) ($MN)
  • Table 63 Global AI in Logistics Automation Market Outlook, By Food and Beverage (2023-2034) ($MN)
  • Table 64 Global AI in Logistics Automation Market Outlook, By Third-Party Logistics (3PL) and Freight Forwarders (2023-2034) ($MN)
  • Table 65 Global AI in Logistics Automation Market Outlook, By Aerospace and Defense (2023-2034) ($MN)
  • Table 66 Global AI in Logistics Automation Market Outlook, By Consumer Goods (2023-2034) ($MN)
  • Table 67 Global AI in Logistics Automation Market Outlook, By Oil and Gas (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.