物流与供应链中的人工智慧市场机会、成长动力、产业趋势分析及 2025 - 2034 年预测

2024年，全球人工智慧物流和供应链市场规模达201亿美元，预计到2034年将以25.9%的复合年增长率成长，达到1965.8亿美元。这得归功于对即时供应链视觉性、最佳化路线规划、精准需求预测和仓库自动化日益增长的需求。企业越来越多地将人工智慧融入其运营，以增强决策流程、降低营运成本并管理复杂的物流网路。预测分析、机器人流程自动化和自动驾驶汽车等人工智慧解决方案正在将传统供应链转变为智慧且适应性强的生态系统。

全球供应链日益复杂，催生了对预测分析和即时资料的需求，使企业能够分析来自感测器、GPS 和企业资源规划 (ERP) 系统的大量资料，从而优化库存管理并降低成本。人工智慧 (AI) 可协助企业快速适应市场变化、防止中断并提高客户满意度。电子商务和全通路零售的扩张进一步凸显了对速度、准确性和灵活性的需求，而人工智慧技术有助于简化订单处理、自动化交付计划并预测客户行为。

2024年，软体产业以56%的市占率领先市场，预计到2034年将以26%的复合年增长率成长。软体有助于增强整个供应链的智慧决策、自动化和即时资料分析。人工智慧驱动的软体解决方案，包括路线优化、需求预测和仓库自动化，已被物流供应商广泛采用，以优化营运、降低成本并提高效率。这些解决方案对于提高规划准确性、最大限度地减少人为错误以及快速适应市场波动至关重要。对预测分析和即时可视性的重视极大地促进了对人工智慧软体应用程式日益增长的需求。

机器学习 (ML) 领域在 2024 年占据了 47% 的市场。它能够处理大量资料集并即时产生可操作的洞察，这对于分析来自物联网设备、GPS 系统和客户互动的结构化和非结构化资料至关重要。 ML 演算法可以优化库存管理，发现需求模式，消除营运瓶颈，从而提高效率和成本效益。这些演算法不断发展，提供超越传统系统的预测洞察和自动化机会。

由于先进的数位基础设施和新兴技术的广泛应用，美国在物流和供应链市场占据了85%的份额，2024年创造了62亿美元的市场规模。美国物流公司是首批将人工智慧应用于路线优化、需求预测、仓库自动化和预测性维护等解决方案的公司之一。大型科技公司和人工智慧供应商的加入进一步巩固了美国的领先地位，加速了人工智慧在物流领域的应用。公营和私营部门对人工智慧研发的投入，加上《国家人工智慧倡议法案》等政府倡议，推动了人工智慧技术在物流和供应链领域的应用。

物流和供应链市场人工智慧的知名企业包括亚马逊网路服务、甲骨文、Blue Yonder、SAP SE、FourKites、C3.ai、Google、微软、IBM 和曼哈顿联合公司。为了巩固市场地位，各公司正专注于策略合作伙伴关係和收购，以增强其人工智慧能力并拓宽服务范围。利用尖端技术，这些公司将机器学习、机器人技术和自动化整合到物流和供应链营运中，以提高效率并降低成本。许多公司投资于人工智慧驱动的软体解决方案，用于即时分析、路线优化和需求预测，使它们在快速发展的市场中保持竞争力。此外，人工智慧解决方案提供者正越来越关注电子商务领域，确保快速、灵活且准确的交付系统，以满足日益增长的消费者期望。

目录

第一章：方法论与范围

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
  • 技术提供者
  • 系统整合商和顾问公司
  • 物流技术提供商
  • 硬体和机器人公司
  • 託管服务提供者 (MSP)
• 利润率分析
• 川普政府关税的影响
  • 对贸易的影响
    • 贸易量中断
    • 其他国家的报復措施
  • 对产业的影响
    • 主要材料价格波动
    • 供应链重组
    • 提供成本影响
  • 策略产业反应
    • 供应链重组
    • 定价和供应策略
• 技术与创新格局
• 价格趋势
• 成本細項分析
• 专利分析
• 重要新闻和倡议
• 监管格局
• 衝击力
  • 成长动力
    • 对即时供应链可视性的需求不断增长
    • 电子商务和全通路零售的成长
    • 预测分析和机器学习的进步
    • 物联网与人工智慧集成，实现智慧仓储
    • 采用自动驾驶汽车和无人机
  • 产业陷阱与挑战
    • 初期实施成本高
    • 资料隐私和安全问题
• 成长潜力分析
• 波特的分析
• PESTEL分析

第四章：竞争格局

• 介绍
• 公司市占率分析
• 竞争定位矩阵
• 战略展望矩阵

第五章：市场估计与预测：按组件，2021 - 2034 年

• 主要趋势
• 硬体
  • 感应器
  • 机器人（例如自动导引车、无人机）
• 软体
  • 预测分析
  • 运输管理系统
  • 库存管理
  • 仓库管理
• 服务
  • 託管服务
  • 专业服务
    • 部署与集成
    • 咨询
    • 支援与维护

第六章：市场估计与预测：依技术分类，2021 - 2034 年

• 主要趋势
• 机器学习
• 自然语言处理（NLP）
• 电脑视觉
• 情境感知计算
• 机器人流程自动化（RPA）

第七章：市场估计与预测：按应用，2021 - 2034 年

• 主要趋势
• 车队管理
• 供应链规划
• 库存和仓库管理
• 货运经纪与风险管理
• 需求预测
• 客户服务（聊天机器人、虚拟助理）
• 订单履行和最后一英里交付

第八章：市场估计与预测：依最终用途，2021 - 2034 年

• 主要趋势
• 零售与电子商务
• 製造业
• 汽车
• 食品和饮料
• 医疗保健和製药
• 运输与物流
• 能源与公用事业
• 其他的

第九章：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

第十章：公司简介

• Amazon Web Services
• Blue Yonder
• C3.ai
• ClearMetal
• Fetch Robotics
• FourKites
• GE Digital
• Google
• Honeywell International
• Infor
• Korber Supply Chain
• Llamasoft
• Manhattan Associates
• Microsoft Corporation
• NVIDIA Corporation
• SAP SE
• Siemens AG
• Zebra Technologies

The Global AI in Logistics and Supply Chain Market was valued at USD 20.1 billion in 2024 and is estimated to grow at a CAGR of 25.9% to reach USD 196.58 billion by 2034, driven by the increasing need for real-time supply chain visibility, optimized route planning, accurate demand forecasting, and automation in warehouses. Companies are increasingly incorporating AI into their operations to enhance decision-making processes, reduce operational costs, and manage complex logistics networks. AI-enabled solutions such as predictive analytics, robotic process automation, and autonomous vehicles are transforming traditional supply chains into intelligent, adaptable ecosystems.

The growing intricacy of global supply chains has created a need for predictive analytics and real-time data, allowing businesses to analyze massive amounts of data from sensors, GPS, and enterprise resource planning (ERP) systems to optimize inventory management and reduce costs. AI helps companies adapt quickly to shifts in market conditions, prevent disruptions, and improve customer satisfaction. The expansion of e-commerce and omnichannel retail further emphasizes the need for speed, accuracy, and flexibility, where AI technologies help streamline order processing, automate delivery schedules, and forecast customer behavior.

In 2024, the software sector led the market with a share of 56%, anticipated to grow at a CAGR of 26% through 2034. Software helps in empowering intelligent decision-making, automation, and real-time data analysis throughout the supply chain. AI-driven software solutions, including route optimization, demand forecasting, and warehouse automation, are widely adopted by logistics providers to optimize operations, reduce costs, and enhance efficiency. These solutions are key to improving planning accuracy, minimizing human error, and quickly adjusting to market fluctuations. The emphasis on predictive analytics and real-time visibility significantly contributes to the growing demand for AI-powered software applications.

The machine learning (ML) segment held a 47% share in 2024. Its capability to process massive datasets and generate actionable insights in real time makes it essential for analyzing structured and unstructured data from IoT devices, GPS systems, and customer interactions. ML algorithms optimize inventory management, uncover demand patterns, and eliminate operational bottlenecks, thus enhancing efficiency and cost-effectiveness. These algorithms evolve continuously, providing predictive insights and automation opportunities that outperform traditional systems.

United States AI in the Logistics and Supply Chain Market held an 85% share and generated USD 6.2 billion in 2024 due to its advanced digital infrastructure and widespread adoption of emerging technologies. U.S.-based logistics firms are among the first to integrate AI for solutions such as route optimization, demand forecasting, warehouse automation, and predictive maintenance. The country's leading position is further bolstered by the presence of major tech companies and AI providers, accelerating AI adoption in logistics. Public and private sector investments in AI research and development, coupled with government initiatives like the National AI Initiative Act, support the adoption of AI technologies across the logistics and supply chain landscape.

Prominent players in the AI in Logistics and Supply Chain Market include Amazon Web Services, Oracle, Blue Yonder, SAP SE, FourKites, C3.ai, Google, Microsoft, IBM, and Manhattan Associates. To strengthen their market position, companies are focusing on strategic partnerships and acquisitions to enhance their AI capabilities and broaden service offerings. Leveraging cutting-edge technologies, these companies are integrating machine learning, robotics, and automation into logistics and supply chain operations to improve efficiency and reduce costs. Many firms invest in AI-driven software solutions for real-time analytics, route optimization, and demand forecasting, allowing them to stay competitive in a rapidly evolving market. Additionally, AI solution providers are increasing their focus on the e-commerce sector, ensuring quick, flexible, and accurate delivery systems to meet growing consumer expectations.

Table of Contents

Chapter 1 Methodology & Scope

• 1.1 Research design
  • 1.1.1 Research approach
  • 1.1.2 Data collection methods
• 1.2 Base estimates and calculations
  • 1.2.1 Base year calculation
  • 1.2.2 Key trends for market estimates
• 1.3 Forecast model
• 1.4 Primary research & validation
  • 1.4.1 Primary sources
  • 1.4.2 Data mining sources
• 1.5 Market definitions

Chapter 2 Executive Summary

• 2.1 Industry 360⁰ synopsis, 2021 - 2034

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
• 3.2 Supplier landscape
  • 3.2.1 Technology providers
  • 3.2.2 System integrators and consulting firms
  • 3.2.3 Logistics technology providers
  • 3.2.4 Hardware and robotics companies
  • 3.2.5 Managed service providers (MSPs)
• 3.3 Profit margin analysis
• 3.4 Impact of Trump administration tariffs
  • 3.4.1 Impact on trade
    • 3.4.1.1 Trade volume disruptions
    • 3.4.1.2 Retaliatory measures by other countries
  • 3.4.2 Impact on the industry
    • 3.4.2.1 Price Volatility in key materials
    • 3.4.2.2 Supply chain restructuring
    • 3.4.2.3 Offering cost implications
  • 3.4.3 Strategic industry responses
    • 3.4.3.1 Supply chain reconfiguration
    • 3.4.3.2 Pricing and Offering strategies
• 3.5 Technology & innovation landscape
• 3.6 Price trends
• 3.7 Cost breakdown analysis
• 3.8 Patent analysis
• 3.9 Key news & initiatives
• 3.10 Regulatory landscape
• 3.11 Impact forces
  • 3.11.1 Growth drivers
    • 3.11.1.1 Rising demand for real-time supply chain visibility
    • 3.11.1.2 Growth of e-commerce and omnichannel retailing
    • 3.11.1.3 Advancements in predictive analytics and machine learning
    • 3.11.1.4 Integration of IoT and AI for smart warehousing
    • 3.11.1.5 Adoption of autonomous vehicles and drones
  • 3.11.2 Industry pitfalls & challenges
    • 3.11.2.1 High initial implementation costs
    • 3.11.2.2 Data privacy and security concerns
• 3.12 Growth potential analysis
• 3.13 Porter's analysis
• 3.14 PESTEL analysis

Chapter 4 Competitive Landscape, 2024

• 4.1 Introduction
• 4.2 Company market share analysis
• 4.3 Competitive positioning matrix
• 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2034 ($Mn)

• 5.1 Key trends
• 5.2 Hardware
  • 5.2.1 Sensors
  • 5.2.2 Robots (e.g., automated guided vehicles, drones)
• 5.3 Software
  • 5.3.1 Predictive analytics
  • 5.3.2 Transportation management systems
  • 5.3.3 Inventory management
  • 5.3.4 Warehouse management
• 5.4 Services
  • 5.4.1 Managed services
  • 5.4.2 Professional services
    • 5.4.2.1 Deployment & integration
    • 5.4.2.2 Consulting
    • 5.4.2.3 Support & maintenance

Chapter 6 Market Estimates & Forecast, By Technology, 2021 - 2034 ($Mn)

• 6.1 Key trends
• 6.2 Machine learning
• 6.3 Natural language processing (NLP)
• 6.4 Computer vision
• 6.5 Context-aware computing
• 6.6 Robotics process automation (RPA)

Chapter 7 Market Estimates & Forecast, By Application, 2021 - 2034 ($Mn)

• 7.1 Key trends
• 7.2 Fleet management
• 7.3 Supply chain planning
• 7.4 Inventory & warehouse management
• 7.5 Freight brokerage & risk management
• 7.6 Demand forecasting
• 7.7 Customer service (chatbots, virtual assistants)
• 7.8 Order fulfillment & last-mile delivery

Chapter 8 Market Estimates & Forecast, By End Use, 2021 - 2034 ($Mn)

• 8.1 Key trends
• 8.2 Retail & e-commerce
• 8.3 Manufacturing
• 8.4 Automotive
• 8.5 Food & beverage
• 8.6 Healthcare & pharmaceuticals
• 8.7 Transportation & logistics
• 8.8 Energy & utilities
• 8.9 Others

Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2034 ($Mn, Units)

• 9.1 Key trends
• 9.2 North America
  • 9.2.1 U.S.
  • 9.2.2 Canada
• 9.3 Europe
  • 9.3.1 UK
  • 9.3.2 Germany
  • 9.3.3 France
  • 9.3.4 Italy
  • 9.3.5 Spain
  • 9.3.6 Russia
  • 9.3.7 Nordics
• 9.4 Asia Pacific
  • 9.4.1 China
  • 9.4.2 India
  • 9.4.3 Japan
  • 9.4.4 South Korea
  • 9.4.5 ANZ
  • 9.4.6 Southeast Asia
• 9.5 Latin America
  • 9.5.1 Brazil
  • 9.5.2 Mexico
  • 9.5.3 Argentina
• 9.6 MEA
  • 9.6.1 UAE
  • 9.6.2 Saudi Arabia
  • 9.6.3 South Africa

Chapter 10 Company Profiles

• 10.1 Amazon Web Services
• 10.2 Blue Yonder
• 10.3 C3.ai
• 10.4 ClearMetal
• 10.5 Fetch Robotics
• 10.6 FourKites
• 10.7 GE Digital
• 10.8 Google
• 10.9 Honeywell International
• 10.10 Infor
• 10.11 Korber Supply Chain
• 10.12 Llamasoft
• 10.13 Manhattan Associates
• 10.14 Microsoft Corporation
• 10.15 NVIDIA Corporation
• 10.16 SAP SE
• 10.17 Siemens AG
• 10.18 Zebra Technologies