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市場調查報告書
商品編碼
1658658

物流市場中的生成式人工智慧按類型、組件、部署模式、應用、最終用戶和地區分類 - 全球趨勢分析、競爭格局和預測（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個工作天內

價格
簡介目錄

由於擴大採用自動化和人工智慧技術來最佳化供應鏈流程，以及對增強物流業務決策能力的需求日益成長，全球物流市場中的生成人工智慧正在蓬勃發展。

預計2024年全球物流生成人工智慧市場規模將達11億美元。預計在 2025-2031 年預測期內，其複合年成長率將達到 44.20%，到 2031 年達到 156 億美元。增加對人工智慧（AI）的投資以標準化程序並改善最後一英里的交付是全球物流市場生成人工智慧的主要成長要素之一。物流行業將從多方面受益於生成式人工智慧，包括供應鏈自動化、需求預測、倉庫管理、庫存控制和路線最佳化，使業務相關人員能夠立即做出明智的選擇。

預計對人工智慧技術的投資增加和人工智慧技術的進步將為全球物流市場人工智慧提供豐厚的成長機會。人工智慧模型現在可以利用物流業務中物聯網設備、GPS 和其他感測器產生的大量資料，並使用這些資料來訓練系統以產生高度準確的預測和最佳化。此外，機器學習 (ML) 演算法、自然語言處理 (NLP) 和神經網路的進步正在不斷提高生成​​式人工智慧分析大量資料集和自動決策的能力。這些進步使得生成式人工智慧對於物流公司來說更加容易取得且更加有效，促使其在該領域迅速得到應用。

不斷加劇的地緣政治緊張局勢可能會推動全球物流市場生成人工智慧的成長。全球供應鏈因地緣政治衝突而中斷，導致貿易限制、邊境關閉和航運延誤。這些顛覆促使物流業使用生成式人工智慧來解決這些障礙。透過最佳化路線、預測需求波動以及尋找替代供應商和路線，生成式人工智慧可以預測和減輕這些干擾。然而，地緣政治衝突也可能對生成式人工智慧產業構成嚴重障礙，因為訓練人工智慧系統所需的即時消費者資料稀缺，這可能會影響人工智慧模型的準確性。

本報告研究了全球物流生成人工智慧市場，並概述了市場及其類型、組成部分、部署模式、應用、最終用戶、區域趨勢、競爭格局以及參與市場的公司概況。

目錄

第1章 調查框架

第 2 章執行摘要

第3章 全球物流生成人工智慧市場洞察

  • 產業價值鏈分析
  • DROC 分析
    • 成長動力
    • 成長抑制因素
    • 機會
    • 任務
  • 科技進步/最新趨勢
  • 法律規範
  • 波特五力分析

第 4 章 全球物流市場中的生成式人工智慧：行銷策略

5. 全球物流市場中的生成式人工智慧：區域分析

  • 2024 年全球物流市場生成人工智慧區域分析
  • 全球物流市場中的生成式人工智慧，市場吸引力分析，2024 年至 2031 年

6. 全球物流生成人工智慧市場概況

  • 2019 年至 2031 年市場規模及預測
  • 市場佔有率和預測
    • 按類型
    • 按組件
    • 依部署方式
    • 按應用
    • 按最終用戶
    • 按地區

7. 北美物流市場中的生成式人工智慧

8. 歐洲物流市場中的生成式人工智慧

9. 亞太地區物流生成人工智慧市場

10. 拉丁美洲物流市場中的生成式人工智慧

11. 中東和非洲物流市場的生成式人工智慧

第12章 競爭格局

  • 主要參與企業及其產品列表
  • 2024 年全球人工智慧物流市場佔有率分析
  • 依業務參數進行競爭性基準基準化分析
  • 重大策略發展（合併、收購、聯盟）

第13章地緣政治緊張局勢加劇對全球物流生成人工智慧市場的影響

第 14 章 公司簡介（公司概況、財務矩陣、競爭格局、關鍵人員、主要競爭對手、聯繫、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

第 15 章 關鍵策略建議

第16章調查方法

簡介目錄
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