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封面
市场调查报告书
商品编码
1683331

全球物流市场人工智慧 - 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个工作天内

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简介目录

概述

2024 年物流市场人工智慧规模达 152.8 亿美元，预计到 2032 年将达到 3,067.6 亿美元，2025-2032 年复合年增长率为 42%。人工智慧技术的进步、蓬勃发展的电子商务产业以及物流营运效率和成本优化的需求推动了这一扩张。

物流行业人工智慧发展趋势

为提高效率并解决劳动力短缺问题，物流业正在经历向自动驾驶汽车（尤其是自动驾驶卡车）整合的重大转变。 Aurora Innovation 等公司正在率先在达拉斯和休斯顿等主要路线之间部署无人驾驶卡车进行货物运输。这些卡车配备了先进的传感器和人工智慧系统，旨在实现「4 级」自动驾驶，能够在特定区域无需人工干预即可运行。

为了应对最近的全球动盪，企业越来越多地采用人工智慧解决方案来增强供应链的弹性。人工智慧技术可以实现对运输中产品的即时监控、需求预测的预测分析以及物流运营的最佳化。

动态的

驱动因素－电子商务扩张推动人工智慧应用

电子商务领域的快速扩张是物流领域采用人工智慧的主要驱动力。随着网上购物越来越流行，对高效、可靠的物流服务的需求也激增。

人工智慧技术促进即时追踪、库存管理和路线优化，确保及时交货并提高客户满意度。电子商务活动的激增需要复杂的物流解决方案，从而推动人工智慧在该领域的整合。

人工智慧技术促进即时追踪、库存管理和路线优化，确保及时交货并提高客户满意度。电子商务活动的激增需要复杂的物流解决方案，从而推动人工智慧在该领域的整合。

限制——高昂的实施成本和整合挑战

儘管有好处，但在物流领域实施人工智慧技术所需的高额初始投资构成了巨大障碍。由于预算限制，中小企业可能会发现为人工智慧整合分配资源具有挑战性。

此外，将人工智慧系统与现有基础设施结合可能很复杂，需要专业知识，并可能在过渡期间扰乱当前的营运。这些因素可能会阻碍人工智慧在物流领域的广泛应用，尤其是在行业中的小型企业中。

目录

第 1 章：方法与范围

第 2 章：定义与概述

第 3 章：执行摘要

第 4 章：动态

  • 影响因素
    • 驱动程式
      • 电子商务扩张推动人工智慧应用
    • 限制
      • 实施成本高，整合挑战大
    • 机会
    • 影响分析

第五章：产业分析

  • 波特五力分析
  • 供应链分析
  • 价值链分析
  • 定价分析
  • 监理与合规性分析
  • 人工智慧与自动化影响分析
  • 研发与创新分析
  • 永续性与绿色技术分析
  • 网路安全分析
  • 下一代技术分析
  • 技术路线图
  • DMI 意见

第 6 章：按技术

  • 机器学习
  • 自然语言处理
  • 情境感知计算
  • 电脑视觉
  • 其他的

第 7 章：按部署类型

  • 本地
  • 基于云端

第 8 章：按组织规模

  • 大型企业
  • 中小企业

第九章：按应用

  • 自动驾驶汽车和堆高机
  • 规划和预测
  • 机器与人类的协作
  • 订购和处理自动化
  • 其他的

第 10 章：依最终用途产业

  • 汽车
  • 食品和饮料
  • 製造业
  • 卫生保健
  • 零售
  • 其他的

第 11 章：按地区

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 义大利
    • 西班牙
    • 欧洲其他地区
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地区
  • 亚太
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 亚太其他地区
  • 中东和非洲

第 12 章：竞争格局

  • 竞争格局
  • 市场定位/份额分析
  • 併购分析

第 13 章：公司简介

  • NVIDIA
    • 公司概况
    • 产品组合和描述
    • 财务概览
    • 主要进展
  • Amazon Web Services, Inc.
  • UPS
  • DHL
  • Microsoft Corporation
  • Infosys
  • IBM Corporation
  • Intel Corporation
  • FedEx Corporation
  • SAP SE

第 14 章：附录

简介目录
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

Why Choose DataM?

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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