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市場調查報告書
商品編碼
1683331

全球物流市場人工智慧 - 2025 至 2032 年

Global AI in Logistics Market - 2025-2032
出版日期: | 出版商: DataM Intelligence | 英文 180 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

概述

2024 年物流市場人工智慧規模達 152.8 億美元，預計到 2032 年將達到 3,067.6 億美元，2025-2032 年複合年成長率為 42%。人工智慧技術的進步、蓬勃發展的電子商務產業以及物流營運效率和成本最佳化的需求推動了這一擴張。

物流行業人工智慧發展趨勢

為提高效率並解決勞動力短缺問題，物流業正在經歷向自動駕駛汽車（尤其是自動駕駛卡車）整合的重大轉變。 Aurora Innovation 等公司正在率先在達拉斯和休斯頓等主要路線之間部署無人駕駛卡車進行貨物運輸。這些卡車配備了先進的感測器和人工智慧系統，旨在實現「4 級」自動駕駛，能夠在特定區域無需人工干預即可運行。

為了應對最近的全球動盪，企業擴大採用人工智慧解決方案來增強供應鏈的彈性。人工智慧技術可以實現對運輸中產品的即時監控、需求預測的預測分析以及物流營運的最佳化。

動態的

促進因素－電子商務擴張推動人工智慧應用

電子商務領域的快速擴張是物流領域採用人工智慧的主要驅動力。隨著網上購物越來越流行，對高效、可靠的物流服務的需求也激增。

人工智慧技術促進即時追蹤、庫存管理和路線最佳化，確保及時交貨並提高客戶滿意度。電子商務活動的激增需要複雜的物流解決方案，從而推動人工智慧在該領域的整合。

人工智慧技術促進即時追蹤、庫存管理和路線最佳化，確保及時交貨並提高客戶滿意度。電子商務活動的激增需要複雜的物流解決方案，從而推動人工智慧在該領域的整合。

限制——高昂的實施成本和整合挑戰

儘管有好處，但在物流領域實施人工智慧技術所需的高額初始投資構成了巨大障礙。由於預算限制，中小企業可能會發現為人工智慧整合分配資源具有挑戰性。

此外，將人工智慧系統與現有基礎設施結合可能很複雜，需要專業知識，並可能在過渡期間擾亂當前的營運。這些因素可能會阻礙人工智慧在物流領域的廣泛應用，尤其是在行業中的小型企業中。

目錄

第 1 章：方法與範圍

第 2 章：定義與概述

第 3 章：執行摘要

第 4 章：動態

  • 影響因素
    • 驅動程式
      • 電子商務擴張推動人工智慧應用
    • 限制
      • 實施成本高，整合挑戰大
    • 機會
    • 影響分析

第5章：產業分析

  • 波特五力分析
  • 供應鏈分析
  • 價值鏈分析
  • 定價分析
  • 監理與合規性分析
  • 人工智慧與自動化影響分析
  • 研發與創新分析
  • 永續性與綠色技術分析
  • 網路安全分析
  • 下一代技術分析
  • 技術路線圖
  • DMI 意見

第 6 章：按技術

  • 機器學習
  • 自然語言處理
  • 情境感知計算
  • 電腦視覺
  • 其他

第 7 章：按部署類型

  • 本地
  • 基於雲端

第 8 章：按組織規模

  • 大型企業
  • 中小企業

第9章：按應用

  • 自動駕駛汽車和堆高機
  • 規劃和預測
  • 機器與人類的協作
  • 訂購和處理自動化
  • 其他

第 10 章：依最終用途產業

  • 汽車
  • 食品和飲料
  • 製造業
  • 衛生保健
  • 零售
  • 其他

第 11 章：按地區

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 義大利
    • 西班牙
    • 歐洲其他地區
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地區
  • 亞太
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 亞太其他地區
  • 中東和非洲

第 12 章：競爭格局

  • 競爭格局
  • 市場定位/佔有率分析
  • 併購分析

第 13 章：公司簡介

  • NVIDIA
    • 公司概況
    • 產品組合和描述
    • 財務概覽
    • 主要進展
  • Amazon Web Services, Inc.
  • UPS
  • DHL
  • Microsoft Corporation
  • Infosys
  • IBM Corporation
  • Intel Corporation
  • FedEx Corporation
  • SAP SE

第 14 章：附錄

簡介目錄
Product Code: ICT9324

Overview

AI in logistics market reached US$15.28 billion in 2024 and is expected to reach US$306.76 billion by 2032, growing with a CAGR of 42% from 2025-2032. Advancements in AI technologies, the burgeoning e-commerce sector, and the need for efficiency and cost optimization in logistics operations fuel this expansion.

AI in Logistics Trends

The logistics industry is witnessing a significant shift towards the integration of autonomous vehicles, particularly self-driving trucks, to enhance efficiency and address labor shortages. Companies like Aurora Innovation are pioneering the deployment of driverless trucks for freight haulage between major routes such as Dallas and Houston. These trucks are equipped with advanced sensors and AI systems, aiming for "level 4" autonomy, capable of operating without human intervention in specific areas.

In response to recent global disruptions, companies are increasingly adopting AI solutions to enhance supply chain resilience. AI technologies enable real-time monitoring of products in transit, predictive analytics for demand forecasting, and optimization of logistics operations.

Dynamic

Driver - E-commerce Expansion Fueling AI Adoption

The rapid expansion of the e-commerce sector is a primary driver for AI adoption in logistics. As online shopping becomes increasingly popular, the demand for efficient and reliable logistics services has surged.

AI technologies facilitate real-time tracking, inventory management, and route optimization, ensuring timely deliveries and enhancing customer satisfaction. This surge in e-commerce activities necessitates sophisticated logistics solutions, thereby propelling the integration of AI in the sector.

AI technologies facilitate real-time tracking, inventory management, and route optimization, ensuring timely deliveries and enhancing customer satisfaction. This surge in e-commerce activities necessitates sophisticated logistics solutions, thereby propelling the integration of AI in the sector.

Restraint - High Implementation Costs and Integration Challenges

Despite the benefits, the high initial investment required for implementing AI technologies in logistics poses a significant barrier. Small and medium-sized enterprises (SMEs) may find it challenging to allocate resources for AI integration due to budget constraints.

Additionally, integrating AI systems with existing infrastructure can be complex, requiring specialized expertise and potentially disrupting current operations during the transition period. These factors may hinder the widespread adoption of AI in logistics, particularly among smaller players in the industry.

Segment Analysis

The global AI in logistics market is segmented based on technology, deployment type, organization size, application, end-use industry, and region.

AI in self-driving vehicles and forklifts represents a significant segment within the logistics industry, offering transformative potential for operational efficiency and safety.

The self-driving vehicles, particularly autonomous trucks, are at the forefront of AI applications in logistics. The trucking industry in the United States alone generates approximately US$740 billion in revenue annually, highlighting the economic significance of this sector. The adoption of autonomous trucks also addresses the critical issue of driver shortages, which is projected to reach alarming figures by 2030 in the US and 2028 in Europe.

In warehousing and distribution centers, autonomous forklifts equipped with AI are revolutionizing material handling processes. These forklifts can independently navigate warehouse environments, manage inventory, and transport goods, thereby reducing labor costs and minimizing errors associated with manual operations. The implementation of AI-driven forklifts enhances efficiency, allowing for 24/7 operations without fatigue-related performance declines.

Geographical Penetration

North America leads the AI in logistics market, attributed to its advanced technological infrastructure, significant investments in AI research and development, and a robust ecosystem of tech companies.

North America's dominance in AI-driven logistics is fueled by substantial investments in infrastructure and AI innovation. The US government, through agencies like the National Institute of Standards and Technology (NIST) and the Department of Transportation (DOT), is actively funding AI research and smart transportation projects. According to the U.S. Department of Energy, AI-powered logistics solutions have the potential to reduce energy consumption in freight transportation by up to 15%, improving overall efficiency and sustainability.

Major logistics companies in North America are heavily investing in AI-powered automation. FedEx, UPS, and DHL are leveraging AI for route optimization, predictive maintenance, and real-time package tracking. FedEx, for instance, has introduced AI-driven systems for sorting packages, reducing errors, and improving delivery speed. Additionally, autonomous truck trials are being conducted across key freight corridors, such as those connecting California and Texas, to test AI-powered long-haul transport.

Technology Roadmap

The global AI in logistics market is expected to evolve significantly over the coming years, driven by advancements in network infrastructure, the expansion of IoT, and the increasing adoption of artificial intelligence (AI) at the logistics. Government initiatives, regulatory frameworks, and private sector investments are set to accelerate AI adoption in cybersecurity across multiple industries.

Competitive Landscape

The major players in the market include NVIDIA, Amazon Web Services, Inc., UPS, DHL, Microsoft Corporation, Infosys, IBM Corporation, Intel Corporation, FedEx Corporation, and SAP SE.

By Technology

  • Machine Learning
  • Natural Language Processing
  • Context Awareness Computing
  • Computer Vision
  • Others

By Deployment Type

  • On-Premise
  • Cloud-based

By Organization Size

  • Large enterprises
  • Small & medium sized enterprises

By Application

  • Self-driving Vehicles and Forklifts
  • Planning and Forecasting
  • Machine and Human Collaboration
  • Automation of Ordering and Processing
  • Others

By End-Use Industry

  • Automotive
  • Food and Beverages
  • Manufacturing
  • Healthcare
  • Retail
  • Others

By Region

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • France
    • Italy
    • Spain
    • Rest of Europe
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Asia-Pacific
    • China
    • India
    • Japan
    • Australia
    • Rest of Asia-Pacific
  • Middle East and Africa

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Target Audience 2024

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet by Technology
  • 3.2. Snippet by Deployment Type
  • 3.3. Snippet by Organization Size
  • 3.4. Snippet by Application
  • 3.5. Snippet by End-Use Industry
  • 3.6. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. E-commerce Expansion Fueling AI Adoption
    • 4.1.2. Restraints
      • 4.1.2.1. High Implementation Costs and Integration Challenges
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Value Chain Analysis
  • 5.4. Pricing Analysis
  • 5.5. Regulatory and Compliance Analysis
  • 5.6. AI & Automation Impact Analysis
  • 5.7. R&D and Innovation Analysis
  • 5.8. Sustainability & Green Technology Analysis
  • 5.9. Cybersecurity Analysis
  • 5.10. Next Generation Technology Analysis
  • 5.11. Technology Roadmap
  • 5.12. DMI Opinion

6. By Technology

  • 6.1. Introduction
    • 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 6.1.2. Market Attractiveness Index, By Technology
  • 6.2. Machine Learning*
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 6.3. Natural Language Processing
  • 6.4. Context Awareness Computing
  • 6.5. Computer Vision
  • 6.6. Others

7. By Deployment Type

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
    • 7.1.2. Market Attractiveness Index, By Deployment Type
  • 7.2. On-premises*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Cloud-based

8. By Organization Size

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 8.1.2. Market Attractiveness Index, By Organization Size
  • 8.2. Large enterprises*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. Small & Medium Sized Enterprises

9. By Application

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 9.1.2. Market Attractiveness Index, By Application
  • 9.2. Self-driving Vehicles and Forklifts*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. Planning and Forecasting
  • 9.4. Machine and Human Collaboration
  • 9.5. Automation of Ordering and Processing
  • 9.6. Others

10. By End-Use Industry

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-Use Industry
    • 10.1.2. Market Attractiveness Index, By End-Use Industry
  • 10.2. Automotive*
    • 10.2.1. Introduction
    • 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 10.3. Food and Beverages
  • 10.4. Manufacturing
  • 10.5. Healthcare
  • 10.6. Retail
  • 10.7. Others

11. By Region

  • 11.1. Introduction
    • 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 11.1.2. Market Attractiveness Index, By Region
  • 11.2. North America
    • 11.2.1. Introduction
    • 11.2.2. Key Region-Specific Dynamics
    • 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
    • 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-Use Industry
    • 11.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.2.8.1. US
      • 11.2.8.2. Canada
      • 11.2.8.3. Mexico
  • 11.3. Europe
    • 11.3.1. Introduction
    • 11.3.2. Key Region-Specific Dynamics
    • 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
    • 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-Use Industry
    • 11.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.3.8.1. Germany
      • 11.3.8.2. UK
      • 11.3.8.3. France
      • 11.3.8.4. Italy
      • 11.3.8.5. Spain
      • 11.3.8.6. Rest of Europe
  • 11.4. South America
    • 11.4.1. Introduction
    • 11.4.2. Key Region-Specific Dynamics
    • 11.4.3. Key Region-Specific Dynamics
    • 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
    • 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-Use Industry
    • 11.4.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.4.9.1. Brazil
      • 11.4.9.2. Argentina
      • 11.4.9.3. Rest of South America
  • 11.5. Asia-Pacific
    • 11.5.1. Introduction
    • 11.5.2. Key Region-Specific Dynamics
    • 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
    • 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-Use Industry
    • 11.5.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.5.8.1. China
      • 11.5.8.2. India
      • 11.5.8.3. Japan
      • 11.5.8.4. Australia
      • 11.5.8.5. Rest of Asia-Pacific
  • 11.6. Middle East and Africa
    • 11.6.1. Introduction
    • 11.6.2. Key Region-Specific Dynamics
    • 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
    • 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-Use Industry

12. Competitive Landscape

  • 12.1. Competitive Scenario
  • 12.2. Market Positioning/Share Analysis
  • 12.3. Mergers and Acquisitions Analysis

13. Company Profiles

  • 13.1. NVIDIA*
    • 13.1.1. Company Overview
    • 13.1.2. Product Portfolio and Description
    • 13.1.3. Financial Overview
    • 13.1.4. Key Developments
  • 13.2. Amazon Web Services, Inc.
  • 13.3. UPS
  • 13.4. DHL
  • 13.5. Microsoft Corporation
  • 13.6. Infosys
  • 13.7. IBM Corporation
  • 13.8. Intel Corporation
  • 13.9. FedEx Corporation
  • 13.10. SAP SE

LIST NOT EXHAUSTIVE

14. Appendix

  • 14.1. About Us and Services
  • 14.2. Contact Us