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市场调查报告书
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1782148

物流市场中的生成人工智慧机会、成长动力、产业趋势分析及 2025 - 2034 年预测

Generative AI in Logistics Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025 - 2034
出版日期: | 出版商: Global Market Insights Inc. | 英文 190 Pages | 商品交期: 2-3个工作天内

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简介目录

2024年，全球物流生成式人工智慧市场规模达13亿美元，预计到2034年将以33.7%的复合年增长率成长，达到231亿美元。这项技术透过提供即时情报和长期战略预测，正在从根本上改变供应链营运。透过模拟数千条配送路线和运输场景，物流供应商可以优化库存计划，降低运费，并为意外中断做好准备。人工智慧驱动的需求预测还能简化资源使用，而动态路线规划工具则可缩短交付时间。随着营运效率和成本控制变得越来越重要，生成式人工智慧的整合已成为塑造市场未来的关键力量。

物流市场中的生成式人工智慧 - IMG1

生成式人工智慧使物流公司能够透过分析客户行为和偏好来增强服务个人化。这些智慧系统可以触发即时警报，推荐理想的配送时间，并根据客户互动自动调整服务。这种程度的客製化提升了客户满意度和忠诚度，同时也使企业能够收取更高的价格。在竞争激烈的行业中，由人工智慧驱动的个人化物流体验持续推动着产业发展。此外，随着降低燃料成本和排放的压力日益增大，物流车队越来越依赖人工智慧，利用交通模式、天气预测和历史资料来推荐优化路线，从而使更清洁、更精简的营运成为行业标准。

市场范围
起始年份 2024
预测年份 2025-2034
起始值 13亿美元
预测值 231亿美元
复合年增长率 33.7%

2024年，软体领域占据了66%的市场份额，预计到2034年将以32%的复合年增长率成长。物流团队优先考虑人工智慧驱动的预测工具，这些工具可以模拟各种供应链中断情况，例如库存短缺、交付延误或需求突然激增。这些工具可协助企业主动调整营运，进而提高效率并降低成本。这些现代解决方案比旧模型更快地提供结果，并且易于与传统系统集成，因此比耗时的客製化方案更具吸引力。

云端部署领域在2024年占据了67%的份额，预计到2034年将维持32%的复合年增长率的强劲成长。随着物流业务在地理上日益分散，企业正在选择灵活的、基于云端的人工智慧解决方案，这些解决方案可以根据不断变化的业务需求即时扩展。与传统的伺服器设定不同，云端平台能够在需求激增时提供即时运算能力和资料存储，尤其是在季节性高峰或市场意外波动期间。这种适应性使云端系统对全球供应链至关重要，巩固了其在该领域的主导地位。

北美物流市场生成式人工智慧占85%的市场份额，2024年产值达3.552亿美元。在IBM、微软、亚马逊、甲骨文、Palantir Technologies、SAP、NVIDIA和谷歌等主要科技公司的支持下，美国已成为供应链先进人工智慧应用的中心枢纽。这些公司提供企业级人工智慧基础设施，使物流供应商能够立即获得尖端功能，加速演算法的开发和部署。这种快速的创新週期使美国成为全球物流人工智慧领域的领跑者。

物流市场生成式人工智慧的领导企业正在加倍投入战略云端合作伙伴关係、可扩展的人工智慧模型和行业特定的机器学习工具。他们也专注于模组化人工智慧解决方案，以快速适应特定区域和产业的物流挑战。透过 API 整合增强用户可存取性、建立即插即用平台以及实现即时资料视觉性是这些企业的共同目标。这些公司投资于敏捷开发环境，并提供低延迟运算以满足即时物流需求。客製化能力、以永续性为重点的路线优化和预测分析被优先考虑，以提高客户参与度并降低营运风险，从而帮助品牌在快速发展的市场格局中获得竞争优势。

目录

第一章：方法论

  • 市场范围和定义
  • 研究设计
    • 研究方法
    • 资料收集方法
  • 资料探勘来源
    • 全球的
    • 地区/国家
  • 基础估算与计算
    • 基准年计算
    • 市场评估的主要趋势
  • 初步研究和验证
    • 主要来源
  • 预测模型
  • 研究假设和局限性

第二章：执行摘要

第三章：行业洞察

  • 产业生态系统分析
    • 供应商格局
    • 利润率
    • 成本结构
    • 每个阶段的增值
    • 影响价值链的因素
    • 中断
  • 产业衝击力
    • 成长动力
      • 增强供应链优化
      • 重复流程的自动化
      • 客户的个人化体验
      • 具成本效益的车队和航线管理
    • 产业陷阱与挑战
      • 资料隐私和安全风险
      • 与遗留系统的整合复杂性
    • 市场机会
      • 人工智慧驱动的需求预测与库存优化
      • 智慧仓储的数位孪生创建
      • 自主路线规划与车队管理
  • 成长潜力分析
  • 监管格局
    • 北美洲
    • 欧洲
    • 亚太地区
    • 拉丁美洲
    • 中东和非洲
  • 波特的分析
  • PESTEL分析
  • 技术和创新格局
    • 当前的技术趋势
    • 新兴技术
  • 案例研究
  • 用例
  • 成本細項分析
  • 专利分析
  • 永续性和环境方面
    • 永续实践
    • 减少废弃物的策略
    • 生产中的能源效率
    • 环保倡议
    • 碳足迹考量

第四章：竞争格局

  • 介绍
  • 公司市占率分析
    • 北美洲
    • 欧洲
    • 亚太地区
    • 拉丁美洲
    • MEA
  • 主要市场参与者的竞争分析
  • 竞争定位矩阵
  • 战略展望矩阵
  • 关键进展
    • 併购
    • 伙伴关係与合作
    • 新产品发布
    • 扩张计划和资金

第五章：市场估计与预测：按类型，2021 - 2034 年

  • 主要趋势
  • 变分自动编码器
  • 生成对抗网络
  • 循环神经网络
  • 长短期记忆网络
  • 变形金刚

第六章：市场估计与预测：按组件，2021 - 2034 年

  • 主要趋势
  • 软体
  • 服务

第七章：市场估计与预测：按部署模式，2021 - 2034 年

  • 主要趋势
  • 云
  • 本地

第八章：市场估计与预测：按应用，2021 - 2034 年

  • 主要趋势
  • 路线优化
  • 需求预测
  • 仓库和库存管理
  • 供应链自动化
  • 预测性维护
  • 风险管理
  • 客製化物流解决方案
  • 其他的

第九章：市场估计与预测：依最终用途，2021-2034

  • 主要趋势
  • 第三方物流供应商
  • 货运代理
  • 电子商务公司
  • 製造商

第十章：市场估计与预测：按地区，2021 - 2034 年

  • 主要趋势
  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 俄罗斯
    • 北欧人
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 韩国
    • 澳新银行
    • 东南亚
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • MEA
    • 阿联酋
    • 沙乌地阿拉伯
    • 南非

第 11 章：公司简介

  • Amazon Web Services
  • DHL Group
  • FedEx
  • Flexport
  • Four Kites
  • Google
  • IBM
  • Locus
  • Maersk
  • Microsoft
  • NVIDIA
  • Open AI
  • Optimal Dynamics
  • Oracle
  • Palantir Technologies
  • Project44
  • Salesforce
  • SAP
  • UPS
  • XPO Logistics
简介目录
Product Code: 10098

The Global Generative AI in Logistics Market was valued at USD 1.3 billion in 2024 and is estimated to grow at a CAGR of 33.7% to reach USD 23.1 billion by 2034. This technology is fundamentally transforming supply chain operations by delivering both real-time intelligence and long-term strategic forecasting. By simulating thousands of delivery routes and transport scenarios, logistics providers can fine-tune inventory planning, lower freight expenses, and stay prepared for unexpected disruptions. AI-powered demand forecasting also streamlines resource use, while dynamic routing tools improve delivery timelines. As operational efficiency and cost control become more important, the integration of generative AI has emerged as a key force shaping the market's future.

Generative AI in Logistics Market - IMG1

Generative AI enables logistics firms to enhance service personalization by analyzing customer behavior and preferences. These intelligent systems can trigger real-time alerts, recommend ideal delivery windows, and automatically adjust services based on client interactions. This level of customization boosts customer satisfaction and loyalty while allowing businesses to charge premium prices. In a competitive industry, personalized logistics experiences powered by AI continue to drive momentum. Moreover, with growing pressure to reduce fuel costs and emissions, logistics fleets increasingly rely on AI to suggest optimized routes using traffic patterns, weather predictions, and historical data, making cleaner and leaner operations the standard.

Market Scope
Start Year2024
Forecast Year2025-2034
Start Value$1.3 Billion
Forecast Value$23.1 Billion
CAGR33.7%

In 2024, the software segment held a 66% share and is set to grow at a CAGR of 32% through 2034. Logistics teams have prioritized AI-driven predictive tools that simulate numerous supply chain disruptions like stock shortages, delivery hold-ups, or sudden demand spikes. These tools help firms adjust operations proactively, improving both efficiency and cost outcomes. These modern solutions offer faster results than older models and integrate easily with legacy systems, making them more attractive than time-consuming, custom-built options.

The cloud deployment segment held a 67% share in 2024 and is expected to maintain strong growth at a CAGR of 32% through 2034. As logistics operations become more geographically dispersed, firms are choosing flexible, cloud-based AI solutions that scale instantly based on fluctuating business needs. Unlike traditional server setups, cloud platforms provide real-time computing power and data storage as demand surges, especially during seasonal peaks or unexpected market shifts. This adaptability makes cloud systems critical for global supply chains, reinforcing their dominance in the sector.

North America Generative AI In Logistics Market held 85% share and generated USD 355.2 million in 2024. The country has emerged as a central hub for advanced AI adoption in supply chains, backed by major tech firms like IBM, Microsoft, Amazon, Oracle, Palantir Technologies, SAP, NVIDIA, and Google. These companies offer enterprise-ready AI infrastructure, giving logistics providers immediate access to cutting-edge capabilities that accelerate algorithm development and deployment. This rapid innovation cycle positions the U.S. as a frontrunner in logistics AI worldwide.

Leading firms in the Generative AI in Logistics Market are doubling down on strategic cloud partnerships, scalable AI models, and industry-specific machine learning tools. They're also focusing on modular AI solutions that adapt quickly to regional and sector-specific logistics challenges. Enhancing user accessibility through API integration, building plug-and-play platforms, and enabling real-time data visibility are common goals. These companies invest in agile development environments and provide low-latency computing to meet real-time logistics demands. Customization capabilities, sustainability-focused route optimization, and predictive analytics are being prioritized to improve customer engagement and reduce operational risks, giving brands a competitive edge in a fast-evolving market landscape.

Table of Contents

Chapter 1 Methodology

  • 1.1 Market scope and definition
  • 1.2 Research design
    • 1.2.1 Research approach
    • 1.2.2 Data collection methods
  • 1.3 Data mining sources
    • 1.3.1 Global
    • 1.3.2 Regional/Country
  • 1.4 Base estimates and calculations
    • 1.4.1 Base year calculation
    • 1.4.2 Key trends for market estimation
  • 1.5 Primary research and validation
    • 1.5.1 Primary sources
  • 1.6 Forecast model
  • 1.7 Research assumptions and limitations

Chapter 2 Executive Summary

  • 2.1 Industry 360° synopsis
  • 2.2 Key market trends
    • 2.2.1 Regional
    • 2.2.2 Type
    • 2.2.3 Component
    • 2.2.4 Deployment mode
    • 2.2.5 Application
    • 2.2.6 End Use
  • 2.3 TAM Analysis, 2025-2034
  • 2.4 CXO perspectives: strategic imperatives
    • 2.4.1 Executive decision points
    • 2.4.2 Critical success factors
  • 2.5 Future outlook and strategic recommendations

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
    • 3.1.1 Supplier landscape
    • 3.1.2 Profit margin
    • 3.1.3 Cost structure
    • 3.1.4 Value addition at each stage
    • 3.1.5 Factors affecting the value chain
    • 3.1.6 Disruptions
  • 3.2 Industry impact forces
    • 3.2.1 Growth drivers
      • 3.2.1.1 Enhanced supply chain optimization
      • 3.2.1.2 Automation of repetitive process
      • 3.2.1.3 Personalized experience of the customers
      • 3.2.1.4 Cost-efficient fleet & route management
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 Data privacy and security risks
      • 3.2.2.2 Integration complexity with legacy systems
    • 3.2.3 Market opportunities
      • 3.2.3.1 AI driven demand forecasting and inventory optimization
      • 3.2.3.2 Digital twin creation for smart warehousing
      • 3.2.3.3 Autonomous route planning and fleet management
  • 3.3 Growth potential analysis
  • 3.4 Regulatory landscape
    • 3.4.1 North America
    • 3.4.2 Europe
    • 3.4.3 Asia Pacific
    • 3.4.4 Latin America
    • 3.4.5 Middle East & Africa
  • 3.5 Porter’s analysis
  • 3.6 PESTEL analysis
  • 3.7 Technology and innovation landscape
    • 3.7.1 Current technological trends
    • 3.7.2 Emerging technologies
  • 3.8 Case studies
  • 3.9 Use cases
  • 3.10 Cost breakdown analysis
  • 3.11 Patent analysis
  • 3.12 Sustainability and environmental aspects
    • 3.12.1 Sustainable practices
    • 3.12.2 Waste reduction strategies
    • 3.12.3 Energy efficiency in production
    • 3.12.4 Eco-friendly initiatives
    • 3.12.5 Carbon footprint considerations

Chapter 4 Competitive Landscape, 2024

  • 4.1 Introduction
  • 4.2 Company market share analysis
    • 4.2.1 North America
    • 4.2.2 Europe
    • 4.2.3 Asia Pacific
    • 4.2.4 LATAM
    • 4.2.5 MEA
  • 4.3 Competitive analysis of major market players
  • 4.4 Competitive positioning matrix
  • 4.5 Strategic outlook matrix
  • 4.6 Key developments
    • 4.6.1 Mergers & acquisitions
    • 4.6.2 Partnerships & collaborations
    • 4.6.3 New product launches
    • 4.6.4 Expansion plans and funding

Chapter 5 Market Estimates & Forecast, By Type, 2021 - 2034 (USD Million)

  • 5.1 Key trends
  • 5.2 Variational autoencoder
  • 5.3 Generative adversarial networks
  • 5.4 Recurrent neural networks
  • 5.5 Long short-term memory networks
  • 5.6 Transformers

Chapter 6 Market Estimates & Forecast, By Component, 2021 - 2034 (USD Million)

  • 6.1 Key trends
  • 6.2 Software
  • 6.3 Services

Chapter 7 Market Estimates & Forecast, By Deployment Mode, 2021 - 2034 (USD Million)

  • 7.1 Key trends
  • 7.2 Cloud
  • 7.3 On-premises

Chapter 8 Market Estimates & Forecast, By Application, 2021 - 2034 (USD Million)

  • 8.1 Key trends
  • 8.2 Route optimization
  • 8.3 Demand forecasting
  • 8.4 Warehouse and inventory management
  • 8.5 Supply chain automation
  • 8.6 Predictive maintenance
  • 8.7 Risk management
  • 8.8 Customized logistics solution
  • 8.9 Others

Chapter 9 Market Estimates & Forecast, By End Use, 2021- 2034 (USD Million)

  • 9.1 Key trends
  • 9.2 Third party logistics providers
  • 9.3 Freight forwarders
  • 9.4 E-commerce companies
  • 9.5 Manufacturers

Chapter 10 Market Estimates & Forecast, By Region, 2021 - 2034 (USD Million)

  • 10.1 Key trends
  • 10.2 North America
    • 10.2.1 U.S.
    • 10.2.2 Canada
  • 10.3 Europe
    • 10.3.1 UK
    • 10.3.2 Germany
    • 10.3.3 France
    • 10.3.4 Italy
    • 10.3.5 Spain
    • 10.3.6 Russia
    • 10.3.7 Nordics
  • 10.4 Asia Pacific
    • 10.4.1 China
    • 10.4.2 India
    • 10.4.3 Japan
    • 10.4.4 South Korea
    • 10.4.5 ANZ
    • 10.4.6 Southeast Asia
  • 10.5 Latin America
    • 10.5.1 Brazil
    • 10.5.2 Mexico
    • 10.5.3 Argentina
  • 10.6 MEA
    • 10.6.1 UAE
    • 10.6.2 Saudi Arabia
    • 10.6.3 South Africa

Chapter 11 Company Profiles

  • 11.1 Amazon Web Services
  • 11.2 DHL Group
  • 11.3 FedEx
  • 11.4 Flexport
  • 11.5 Four Kites
  • 11.6 Google
  • 11.7 IBM
  • 11.8 Locus
  • 11.9 Maersk
  • 11.10 Microsoft
  • 11.11 NVIDIA
  • 11.12 Open AI
  • 11.13 Optimal Dynamics
  • 11.14 Oracle
  • 11.15 Palantir Technologies
  • 11.16 Project44
  • 11.17 Salesforce
  • 11.18 SAP
  • 11.19 UPS
  • 11.20 XPO Logistics