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封面
市场调查报告书
商品编码
1658658

物流市场中的生成式人工智慧按类型、组件、部署模式、应用、最终用户和地区划分 - 全球趋势分析、竞争格局和预测（2019-2031 年）

Generative AI in Logistics Market, By Type; By Component; By Deployment Mode; By Application; By End User; By Region, Global Trend Analysis, Competitive Landscape & Forecast, 2019-2031
出版日期: | 出版商: Blueweave Consulting | 英文 511 Pages | 商品交期: 2-3个工作天内

价格
简介目录

由于越来越多地采用自动化和人工智慧技术来优化供应链流程，以及对增强物流业务决策能力的需求日益增长，全球物流市场中的生成人工智慧正在蓬勃发展。

预计2024年全球物流生成人工智慧市场规模将达11亿美元。预计在 2025-2031 年预测期内，其复合年增长率将达到 44.20%，到 2031 年达到 156 亿美元。增加对人工智慧（AI）的投资以标准化程序并改善最后一英里的交付是全球物流市场生成人工智慧的主要成长要素之一。物流行业将从多方面受益于生成式人工智慧，包括供应链自动化、需求预测、仓库管理、库存控制和路线优化，使业务相关人员能够立即做出明智的选择。

预计对人工智慧技术的投资增加和人工智慧技术的进步将为全球物流市场人工智慧提供丰厚的成长机会。人工智慧模型现在可以利用物流业务中物联网设备、GPS 和其他感测器产生的大量资料，并使用这些资料来训练系统以产生高度准确的预测和最佳化。此外，机器学习 (ML) 演算法、自然语言处理 (NLP) 和神经网路的进步正在不断提高生成​​式人工智慧分析大量资料集和自动决策的能力。这些进步使得生成式人工智慧对于物流公司来说更加容易取得且更加有效，促使其在该领域迅速得到应用。

不断加剧的地缘政治紧张局势可能会推动全球物流市场生成人工智慧的成长。全球供应链因地缘政治衝突而中断，导致贸易限制、边境关闭和航运延误。这些颠覆促使物流业使用生成式人工智慧来解决这些障碍。透过优化路线、预测需求波动以及寻找替代供应商和路线，生成式人工智慧可以预测和减轻这些干扰。然而，地缘政治衝突也可能对生成式人工智慧产业构成严重障碍，因为训练人工智慧系统所需的即时消费者资料稀缺，这可能会影响人工智慧模型的准确性。

本报告研究了全球物流生成人工智慧市场，并概述了市场及其类型、组成部分、部署模式、应用、最终用户、区域趋势、竞争格局以及参与市场的公司概况。

目录

第一章 调查框架

第 2 章执行摘要

第三章 全球物流生成人工智慧市场洞察

  • 产业价值链分析
  • DROC 分析
    • 成长动力
    • 成长抑制因素
    • 机会
    • 任务
  • 科技进步/最新趋势
  • 法律规范
  • 波特五力分析

第 4 章 全球物流市场中的生成式人工智慧：行销策略

5. 全球物流市场中的生成式人工智慧：区域分析

  • 2024 年全球物流市场生成人工智慧区域分析
  • 全球物流市场中的生成式人工智慧，市场吸引力分析，2024 年至 2031 年

6. 全球物流生成人工智慧市场概况

  • 2019 年至 2031 年市场规模及预测
  • 市场占有率和预测
    • 按类型
    • 按组件
    • 依部署方式
    • 按应用
    • 按最终用户
    • 按地区

7. 北美物流市场中的生成式人工智慧

8. 欧洲物流市场中的生成式人工智慧

9. 亚太地区物流生成人工智慧市场

10. 拉丁美洲物流市场中的生成式人工智慧

11. 中东和非洲物流市场的生成式人工智慧

第十二章 竞争格局

  • 主要参与企业及其产品列表
  • 2024 年全球人工智慧物流市场占有率分析
  • 依业务参数进行竞争性基准基准化分析
  • 重大策略发展（合併、收购、联盟）

第十三章地缘政治紧张局势加剧对全球物流生成人工智慧市场的影响

第 14 章 公司简介（公司概况、财务矩阵、竞争格局、关键人员、主要竞争对手、联繫、策略展望、SWOT 分析）

  • Blue Yonder
  • CH Robinson
  • FedEx Corp
  • Google Cloud
  • IBM
  • Microsoft
  • PackageX
  • Salesforce
  • Deutsche Post AG
  • Schneider Electric
  • AP Moller-Maersk
  • 其他的

第 15 章 关键策略建议

第十六章调查方法

简介目录
Product Code: BWC25017

Global Generative AI in Logistics Market Zooming 14X to Touch USD 16 Billion by 2031

Global Generative AI in Logistics Market is flourishing because of the rising adoption of automation and AI technologies to optimize supply chain processes and growing need for enhanced decision-making capabilities in logistics operations.

BlueWeave Consulting, a leading strategic consulting and market research firm, in its recent study, estimated Global Generative AI in Logistics Market size at USD 1.10 billion in 2024. During the forecast period between 2025 and 2031, BlueWeave expects Global Generative AI in Logistics Market size to expand at a robust CAGR of 44.20% reaching a value of USD 15.60 billion by 2031. Increasing investments in artificial intelligence (AI) to standardize procedures and improve last-mile delivery is one of the key growth drivers for Global Generative AI in Logistics Market. The logistics industry benefits from generative AI in a number of ways, including supply chain automation, demand forecasting, warehousing and inventory management, and route optimization, which enables business actors to make informed choices instantly.

Opportunity - Advancements in AI Technology and Data Availability

Rising investments in and evolution of AI technologies are projected to present lucrative growth opportunities for Global Generative AI in Logistics Market. AI models are now able to leverage vast amounts of data being generated from IoT devices, GPS, and other sensors in logistics operations that can be used to train these systems and generate highly accurate predictions and optimizations. Furthermore, advancements in machine learning (ML) algorithms, natural language processing (NLP), and neural networks are constantly improving the ability of generative AI to analyze vast datasets and automate decision-making. These advancements make generative AI more accessible and effective for logistics companies, leading to their rapid adoption across the sector.

Impact of Escalating Geopolitical Tensions on Global Generative AI in Logistics Market

Intensifying geopolitical tensions could propel the growth of Global Generative AI in Logistics Market. The global supply chain is disrupted by geopolitical conflicts because of trade restrictions, border closures, and delays in transit. These disruptions pushed the use of generative AI in the logistics industry to address these obstacles. Through route optimization, demand fluctuation predictions, and the discovery of other suppliers and routes, generative AI is being utilized to anticipate and lessen these interruptions. Geopolitical conflicts, however, may also present serious obstacles for the generative AI industry because of the scarcity of real-time consumer data needed to train these AI systems, which might affect the accuracy of AI models.

Route Optimization Leads Global Generative AI Logistics Market

The route optimization segment holds the largest share of Global Generative AI in Logistics Market. In the logistics industry, generative AI is frequently used to improve routes by analyzing historical data, current traffic conditions, and other variables. In order to cut down on delivery times and transportation expenses, the analysis is then utilized to create effective transportation strategies. The demand forecasting segment also covers substantial market share. Supply chain managers may use generative AI to automate ordering plans to keep inventory levels up to date and forecast future trends based on historical data.

North America Dominates Global Generative AI in Logistics Market

North America holds a major market share in Global Generative AI in Logistics Market. The adoption of generative AI in the logistics sector is directly fueled by the presence of industry giants in this field, such as Google, AWS, OpenAI, and IBM in the region. Logistics companies in the United States are employing modern technologies, such as generative AI, for numerous objectives, such as tracking customer behavior and historical sales data, optimizing production planning, and conducting risk anticipation. Such cases increase the logistics industry's operational resilience and productivity, which encourages this sector to incorporate generative AI into their operations.

Competitive Landscape

The major industry players of global Generative AI in Logistics market include Blue Yonder, C. H. Robinson, FedEx Corp., Google Cloud, IBM, Microsoft, PackageX, Salesforce, Deutsche Post AG, Schneider Electric, and A.P. Moller - Maersk. The presence of high number of companies intensify the market competition as they compete to gain a significant market share. These companies employ various strategies, including mergers and acquisitions, partnerships, joint ventures, license agreements, and new product launches to further enhance their market share.

The in-depth analysis of the report provides information about growth potential, upcoming trends, and Global Generative AI in Logistics Market. It also highlights the factors driving forecasts of total market size. The report promises to provide recent technology trends in Global Generative AI in Logistics Market and industry insights to help decision-makers make sound strategic decisions. Furthermore, the report also analyzes the growth drivers, challenges, and competitive dynamics of the market.

Table of Contents

1. Research Framework

  • 1.1. Research Objective
  • 1.2. Product Overview
  • 1.3. Market Segmentation

2. Executive Summary

3. Global Generative AI in Logistics Market Insights

  • 3.1. Industry Value Chain Analysis
  • 3.2. DROC Analysis
    • 3.2.1. Growth Drivers
      • 3.2.1.1. Rising Adoption of Automation and AI Technologies to Optimize Supply Chain Processes
      • 3.2.1.2. Growing Need for Enhanced Decision-Making Capabilities in Logistics Operations
      • 3.2.1.3. Increasing Use of Predictive Analytics for Demand Forecasting and Route Optimization
    • 3.2.2. Restraints
      • 3.2.2.1. High Implementation Costs of Generative AI Solutions for Logistics Companies
      • 3.2.2.2. Limited AI Expertise and Skilled Workforce to Operate and Manage AI Technologies
    • 3.2.3. Opportunities
      • 3.2.3.1. Integration of Generative AI with IoT, Blockchain, and Robotics to Enhance Supply Chain Efficiency
      • 3.2.3.2. Development of AI-driven Autonomous Vehicles and Drones for Logistics Operations
      • 3.2.3.3. Growing Adoption of Generative AI in Warehouse Management and Inventory Optimization
    • 3.2.4. Challenges
      • 3.2.4.1. Managing Data Quality and Standardization Across Fragmented Supply Chain Networks.
      • 3.2.4.2. Data Privacy and Cybersecurity Concerns in Handling Sensitive Logistics Data
  • 3.3. Technological Advancements/Recent Developments
  • 3.4. Regulatory Framework
  • 3.5. Porter's Five Forces Analysis
    • 3.5.1. Bargaining Power of Suppliers
    • 3.5.2. Bargaining Power of Buyers
    • 3.5.3. Threat of New Entrants
    • 3.5.4. Threat of Substitutes
    • 3.5.5. Intensity of Rivalry

4. Global Generative AI in Logistics Market: Marketing Strategies

5. Global Generative AI in Logistics Market: Geographical Analysis

  • 5.1. Global Generative AI in Logistics Market, Geographical Analysis, 2024
  • 5.2. Global Generative AI in Logistics Market, Market Attractiveness Analysis, 2024-2031

6. Global Generative AI in Logistics Market Overview

  • 6.1. Market Size & Forecast, 2019-2031
    • 6.1.1. By Value (USD Billion)
  • 6.2. Market Share & Forecast
    • 6.2.1. By Type
      • 6.2.1.1. Variational Autoencoder (VAE)
      • 6.2.1.2. Generative Adversarial Networks (GANs)
      • 6.2.1.3. Recurrent Neural Networks (RNNs)
      • 6.2.1.4. Long Short-Term Memory (LSTM) networks
      • 6.2.1.5. Others
    • 6.2.2. By Component
      • 6.2.2.1. Software
      • 6.2.2.2. Services
    • 6.2.3. By Deployment Mode
      • 6.2.3.1. Cloud
      • 6.2.3.2. On-premises
    • 6.2.4. By Application
      • 6.2.4.1. Route Optimization
      • 6.2.4.2. Demand Forecasting
      • 6.2.4.3. Warehouse & Inventory Management
      • 6.2.4.4. Supply Chain Automation
      • 6.2.4.5. Predictive Maintenance
      • 6.2.4.6. Risk Management
      • 6.2.4.7. Customized Logistics Solutions
      • 6.2.4.8. Others
    • 6.2.5. By End User
      • 6.2.5.1. Road Transportation
      • 6.2.5.2. Railway Transportation
      • 6.2.5.3. Aviation
      • 6.2.5.4. Shipping & Ports
    • 6.2.6. By Region
      • 6.2.6.1. North America
      • 6.2.6.2. Europe
      • 6.2.6.3. Asia Pacific (APAC)
      • 6.2.6.4. Latin America (LATAM)
      • 6.2.6.5. Middle East and Africa (MEA)

7. North America Generative AI in Logistics Market

  • 7.1. Market Size & Forecast, 2019-2031
    • 7.1.1. By Value (USD Billion)
  • 7.2. Market Share & Forecast
    • 7.2.1. By Type
    • 7.2.2. By Component
    • 7.2.3. By Deployment Mode
    • 7.2.4. By Application
    • 7.2.5. By End User
    • 7.2.6. By Country
      • 7.2.6.1. United States
      • 7.2.6.1.1. By Type
      • 7.2.6.1.2. By Component
      • 7.2.6.1.3. By Deployment Mode
      • 7.2.6.1.4. By Application
      • 7.2.6.1.5. By End User
      • 7.2.6.2. Canada
      • 7.2.6.2.1. By Type
      • 7.2.6.2.2. By Component
      • 7.2.6.2.3. By Deployment Mode
      • 7.2.6.2.4. By Application
      • 7.2.6.2.5. By End User

8. Europe Generative AI in Logistics Market

  • 8.1. Market Size & Forecast, 2019-2031
    • 8.1.1. By Value (USD Billion)
  • 8.2. Market Share & Forecast
    • 8.2.1. By Type
    • 8.2.2. By Component
    • 8.2.3. By Deployment Mode
    • 8.2.4. By Application
    • 8.2.5. By End User
    • 8.2.6. By Country
      • 8.2.6.1. Germany
      • 8.2.6.1.1. By Type
      • 8.2.6.1.2. By Component
      • 8.2.6.1.3. By Deployment Mode
      • 8.2.6.1.4. By Application
      • 8.2.6.1.5. By End User
      • 8.2.6.2. United Kingdom
      • 8.2.6.2.1. By Type
      • 8.2.6.2.2. By Component
      • 8.2.6.2.3. By Deployment Mode
      • 8.2.6.2.4. By Application
      • 8.2.6.2.5. By End User
      • 8.2.6.3. Italy
      • 8.2.6.3.1. By Type
      • 8.2.6.3.2. By Component
      • 8.2.6.3.3. By Deployment Mode
      • 8.2.6.3.4. By Application
      • 8.2.6.3.5. By End User
      • 8.2.6.4. France
      • 8.2.6.4.1. By Type
      • 8.2.6.4.2. By Component
      • 8.2.6.4.3. By Deployment Mode
      • 8.2.6.4.4. By Application
      • 8.2.6.4.5. By End User
      • 8.2.6.5. Spain
      • 8.2.6.5.1. By Type
      • 8.2.6.5.2. By Component
      • 8.2.6.5.3. By Deployment Mode
      • 8.2.6.5.4. By Application
      • 8.2.6.5.5. By End User
      • 8.2.6.6. Belgium
      • 8.2.6.6.1. By Type
      • 8.2.6.6.2. By Component
      • 8.2.6.6.3. By Deployment Mode
      • 8.2.6.6.4. By Application
      • 8.2.6.6.5. By End User
      • 8.2.6.7. Russia
      • 8.2.6.7.1. By Type
      • 8.2.6.7.2. By Component
      • 8.2.6.7.3. By Deployment Mode
      • 8.2.6.7.4. By Application
      • 8.2.6.7.5. By End User
      • 8.2.6.8. The Netherlands
      • 8.2.6.8.1. By Type
      • 8.2.6.8.2. By Component
      • 8.2.6.8.3. By Deployment Mode
      • 8.2.6.8.4. By Application
      • 8.2.6.8.5. By End User
      • 8.2.6.9. Rest of Europe
      • 8.2.6.9.1. By Type
      • 8.2.6.9.2. By Component
      • 8.2.6.9.3. By Deployment Mode
      • 8.2.6.9.4. By Application
      • 8.2.6.9.5. By End User

9. Asia Pacific Generative AI in Logistics Market

  • 9.1. Market Size & Forecast, 2019-2031
    • 9.1.1. By Value (USD Billion)
  • 9.2. Market Share & Forecast
    • 9.2.1. By Type
    • 9.2.2. By Component
    • 9.2.3. By Deployment Mode
    • 9.2.4. By Application
    • 9.2.5. By End User
    • 9.2.6. By Country
      • 9.2.6.1. China
      • 9.2.6.1.1. By Type
      • 9.2.6.1.2. By Component
      • 9.2.6.1.3. By Deployment Mode
      • 9.2.6.1.4. By Application
      • 9.2.6.1.5. By End User
      • 9.2.6.2. India
      • 9.2.6.2.1. By Type
      • 9.2.6.2.2. By Component
      • 9.2.6.2.3. By Deployment Mode
      • 9.2.6.2.4. By Application
      • 9.2.6.2.5. By End User
      • 9.2.6.3. Japan
      • 9.2.6.3.1. By Type
      • 9.2.6.3.2. By Component
      • 9.2.6.3.3. By Deployment Mode
      • 9.2.6.3.4. By Application
      • 9.2.6.3.5. By End User
      • 9.2.6.4. South Korea
      • 9.2.6.4.1. By Type
      • 9.2.6.4.2. By Component
      • 9.2.6.4.3. By Deployment Mode
      • 9.2.6.4.4. By Application
      • 9.2.6.4.5. By End User
      • 9.2.6.5. Australia & New Zealand
      • 9.2.6.5.1. By Type
      • 9.2.6.5.2. By Component
      • 9.2.6.5.3. By Deployment Mode
      • 9.2.6.5.4. By Application
      • 9.2.6.5.5. By End User
      • 9.2.6.6. Indonesia
      • 9.2.6.6.1. By Type
      • 9.2.6.6.2. By Component
      • 9.2.6.6.3. By Deployment Mode
      • 9.2.6.6.4. By Application
      • 9.2.6.6.5. By End User
      • 9.2.6.7. Malaysia
      • 9.2.6.7.1. By Type
      • 9.2.6.7.2. By Component
      • 9.2.6.7.3. By Deployment Mode
      • 9.2.6.7.4. By Application
      • 9.2.6.7.5. By End User
      • 9.2.6.8. Singapore
      • 9.2.6.8.1. By Type
      • 9.2.6.8.2. By Component
      • 9.2.6.8.3. By Deployment Mode
      • 9.2.6.8.4. By Application
      • 9.2.6.8.5. By End User
      • 9.2.6.9. Vietnam
      • 9.2.6.9.1. By Type
      • 9.2.6.9.2. By Component
      • 9.2.6.9.3. By Deployment Mode
      • 9.2.6.9.4. By Application
      • 9.2.6.9.5. By End User
      • 9.2.6.10. Rest of APAC
      • 9.2.6.10.1. By Type
      • 9.2.6.10.2. By Component
      • 9.2.6.10.3. By Deployment Mode
      • 9.2.6.10.4. By Application
      • 9.2.6.10.5. By End User

10. Latin America Generative AI in Logistics Market

  • 10.1. Market Size & Forecast, 2019-2031
    • 10.1.1. By Value (USD Billion)
  • 10.2. Market Share & Forecast
    • 10.2.1. By Type
    • 10.2.2. By Component
    • 10.2.3. By Deployment Mode
    • 10.2.4. By Application
    • 10.2.5. By End User
    • 10.2.6. By Country
      • 10.2.6.1. Brazil
      • 10.2.6.1.1. By Type
      • 10.2.6.1.2. By Component
      • 10.2.6.1.3. By Deployment Mode
      • 10.2.6.1.4. By Application
      • 10.2.6.1.5. By End User
      • 10.2.6.2. Mexico
      • 10.2.6.2.1. By Type
      • 10.2.6.2.2. By Component
      • 10.2.6.2.3. By Deployment Mode
      • 10.2.6.2.4. By Application
      • 10.2.6.2.5. By End User
      • 10.2.6.3. Argentina
      • 10.2.6.3.1. By Type
      • 10.2.6.3.2. By Component
      • 10.2.6.3.3. By Deployment Mode
      • 10.2.6.3.4. By Application
      • 10.2.6.3.5. By End User
      • 10.2.6.4. Peru
      • 10.2.6.4.1. By Type
      • 10.2.6.4.2. By Component
      • 10.2.6.4.3. By Deployment Mode
      • 10.2.6.4.4. By Application
      • 10.2.6.4.5. By End User
      • 10.2.6.5. Rest of LATAM
      • 10.2.6.5.1. By Type
      • 10.2.6.5.2. By Component
      • 10.2.6.5.3. By Deployment Mode
      • 10.2.6.5.4. By Application
      • 10.2.6.5.5. By End User

11. Middle East & Africa Generative AI in Logistics Market

  • 11.1. Market Size & Forecast, 2019-2031
    • 11.1.1. By Value (USD Billion)
  • 11.2. Market Share & Forecast
    • 11.2.1. By Type
    • 11.2.2. By Component
    • 11.2.3. By Deployment Mode
    • 11.2.4. By Application
    • 11.2.5. By End User
    • 11.2.6. By Country
      • 11.2.6.1. Saudi Arabia
      • 11.2.6.1.1. By Type
      • 11.2.6.1.2. By Component
      • 11.2.6.1.3. By Deployment Mode
      • 11.2.6.1.4. By Application
      • 11.2.6.1.5. By End User
      • 11.2.6.2. UAE
      • 11.2.6.2.1. By Type
      • 11.2.6.2.2. By Component
      • 11.2.6.2.3. By Deployment Mode
      • 11.2.6.2.4. By Application
      • 11.2.6.2.5. By End User
      • 11.2.6.3. Qatar
      • 11.2.6.3.1. By Type
      • 11.2.6.3.2. By Component
      • 11.2.6.3.3. By Deployment Mode
      • 11.2.6.3.4. By Application
      • 11.2.6.3.5. By End User
      • 11.2.6.4. Kuwait
      • 11.2.6.4.1. By Type
      • 11.2.6.4.2. By Component
      • 11.2.6.4.3. By Deployment Mode
      • 11.2.6.4.4. By Application
      • 11.2.6.4.5. By End User
      • 11.2.6.5. South Africa
      • 11.2.6.5.1. By Type
      • 11.2.6.5.2. By Component
      • 11.2.6.5.3. By Deployment Mode
      • 11.2.6.5.4. By Application
      • 11.2.6.5.5. By End User
      • 11.2.6.6. Nigeria
      • 11.2.6.6.1. By Type
      • 11.2.6.6.2. By Component
      • 11.2.6.6.3. By Deployment Mode
      • 11.2.6.6.4. By Application
      • 11.2.6.6.5. By End User
      • 11.2.6.7. Algeria
      • 11.2.6.7.1. By Type
      • 11.2.6.7.2. By Component
      • 11.2.6.7.3. By Deployment Mode
      • 11.2.6.7.4. By Application
      • 11.2.6.7.5. By End User
      • 11.2.6.8. Rest of MEA
      • 11.2.6.8.1. By Type
      • 11.2.6.8.2. By Component
      • 11.2.6.8.3. By Deployment Mode
      • 11.2.6.8.4. By Application
      • 11.2.6.8.5. By End User

12. Competitive Landscape

  • 12.1. List of Key Players and Their Offerings
  • 12.2. Global Generative AI in Logistics Company Market Share Analysis, 2024
  • 12.3. Competitive Benchmarking, By Operating Parameters
  • 12.4. Key Strategic Developments (Mergers, Acquisitions, Partnerships)

13. Impact of Escalating Geopolitical Tensions on Global Generative AI in Logistics Market

14. Company Profile (Company Overview, Financial Matrix, Competitive Landscape, Key Personnel, Key Competitors, Contact Address, Strategic Outlook, SWOT Analysis)

  • 14.1. Blue Yonder
  • 14.2. C. H. Robinson
  • 14.3. FedEx Corp
  • 14.4. Google Cloud
  • 14.5. IBM
  • 14.6. Microsoft
  • 14.7. PackageX
  • 14.8. Salesforce
  • 14.9. Deutsche Post AG
  • 14.10. Schneider Electric
  • 14.11. A.P. Moller - Maersk
  • 14.12. Other Prominent Players

15. Key Strategic Recommendations

16. Research Methodology

  • 16.1. Qualitative Research
    • 16.1.1. Primary & Secondary Research
  • 16.2. Quantitative Research
  • 16.3. Market Breakdown & Data Triangulation
    • 16.3.1. Secondary Research
    • 16.3.2. Primary Research
  • 16.4. Breakdown of Primary Research Respondents, By Region
  • 16.5. Assumptions & Limitations

*Financial information of non-listed companies can be provided as per availability.

**The segmentation and the companies are subject to modifications based on in-depth secondary research for the final deliverable