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物流におけるAIの世界市場：2025年～2032年

Global AI in Logistics Market - 2025-2032

出版日
ページ情報
英文 180 Pages
納期
即日から翌営業日
カスタマイズ可能
適宜更新あり
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価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=144.06円
物流におけるAIの世界市場：2025年～2032年
出版日: 2025年03月20日
発行: DataM Intelligence
ページ情報: 英文 180 Pages
納期: 即日から翌営業日
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  • 概要
  • 目次
概要

物流におけるAIの市場規模は、2024年に152億8,000万米ドルに達しました。同市場は、2032年には3,067億6,000万米ドルに達し、2025年～2032年のCAGRは42％になると予測されています。AI技術の進歩、急成長するeコマース分野、物流業務の効率化とコスト最適化のニーズがこの拡大に拍車をかけています。

物流業界は、効率性を高め労働力不足に対処するため、自律走行車、特に自動運転トラックの統合に向けて大きくシフトしています。Aurora Innovationのような企業は、ダラスとヒューストンのような主要ルート間の貨物輸送のために、運転手のいないトラックの配備を先駆的に進めています。これらのトラックには高度なセンサーとAIシステムが搭載され、特定のエリアでは人間の介入なしに運行できる「レベル4」の自律性を目指しています。

近年の世界の混乱を受けて、企業はサプライチェーンの回復力を高めるためにAIソリューションの採用を増やしています。AI技術は、輸送中の製品のリアルタイム監視、需要予測のための予測分析、物流業務の最適化を可能にします。

eコマース分野の急速な拡大は、物流におけるAI導入の主要な推進力となっています。オンラインショッピングの人気が高まるにつれ、効率的で信頼性の高い物流サービスへの需要が急増しています。

AI技術はリアルタイムの追跡、在庫管理、ルート最適化を容易にし、タイムリーな配送を保証し、顧客満足度を高める。このようなeコマース活動の急増により、洗練された物流・ソリューションが必要とされ、それによってこの分野でのAIの統合が推進されています。

AI技術はリアルタイムの追跡、在庫管理、ルート最適化を容易にし、タイムリーな配送を確保し、顧客満足度を高める。このようなeコマース活動の急増は、洗練された物流・ソリューションを必要とし、それによってこの分野でのAIの統合を推進しています。

当レポートでは、世界の物流におけるAI市場について調査し、市場の概要とともに、技術別、展開タイプ別、組織規模別、用途別、最終用途産業別、地域別動向、競合情勢、および市場に参入する企業のプロファイルなどを提供しています。

目次

第1章 調査手法と範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
    • 抑制要因
    • 機会
    • 影響分析

第5章 業界分析

第6章 技術別

  • 機械学習
  • 自然言語処理
  • コンテキスト認識コンピューティング
  • コンピュータビジョン
  • その他

第7章 展開タイプ別

  • オンプレミス
  • クラウドベース

第8章 組織規模別

  • 大企業
  • 中小企業

第9章 用途別

  • 自動運転車両とフォークリフト
  • 計画と予測
  • 機械と人間のコラボレーション
  • 発注と処理の自動化
  • その他

第10章 最終用途産業別

  • 自動車
  • 食品・飲料
  • 製造業
  • ヘルスケア
  • 小売
  • その他

第11章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • スペイン
    • その他
  • 南米
    • ブラジル
    • アルゼンチン
    • その他
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他
  • 中東・アフリカ

第12章 競合情勢

第13章 企業プロファイル

  • NVIDIA
  • Amazon Web Services, Inc.
  • UPS
  • DHL
  • Microsoft Corporation
  • Infosys
  • IBM Corporation
  • Intel Corporation
  • FedEx Corporation
  • SAP SE

第14章 付録

目次
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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  • Value of DataM Reports: Our reports offer specialized insights tailored to the latest trends and specific business inquiries. This personalized approach provides a deeper, strategic perspective, ensuring you receive the precise information necessary to make informed decisions. These insights complement and go beyond what is typically available in generic databases.

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