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市場調查報告書
商品編碼
1913340

物流領域機器學習市場機會、成長要素、產業趨勢分析及2026年至2035年預測

Machine Learning in Logistics Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2026 - 2035
出版日期: | 出版商: Global Market Insights Inc. | 英文 225 Pages | 商品交期: 2-3個工作天內

價格
簡介目錄

全球物流機器學習市場預計到 2025 年將達到 43 億美元，到 2035 年將達到 445 億美元，年複合成長率為 26.7%。

物流市場中的機器學習-IMG1

機器學習正在透過實現預測性決策、高級自動化和供應鏈網路的即時最佳化，變革物流業。數位商務的快速發展、對快速交付日益成長的期望，以及人工智慧和互聯技術的不斷進步，都在加速機器學習的普及應用。企業正在拓展機器學習的應用範圍，以提高預測準確度、最佳化運輸路線、提升倉儲效率、調整存量基準、管理車隊，並在設備故障發生前進行預測。隨著物流生態系統的日益複雜，機器學習解決方案提供的擴充性、響應速度和營運視覺性是傳統系統無法比擬的。這種變革有助於提高服務可靠性、降低成本並增強全球供應鏈的韌性，使機器學習成為未來物流的基礎技術。

市場覆蓋範圍
開始年份 2025
預測年份 2026-2035
起始值 43億美元
預測金額 445億美元
複合年成長率 26.7%

先進的機器學習模型透過實現持續學習和營運自適應，顯著提升了自動化物流系統的效能。企業越來越依賴智慧自動化來應對不斷成長的訂單量、嚴格的交貨期限和頻繁的運輸週期。機器學習驅動的工作流程提高了準確性、效率和勞動生產力，同時滿足了消費者對快速交付日益成長的期望。

預計到2025年，軟體領域將佔據64%的市場佔有率，並在2026年至2035年間以25.1%的複合年成長率成長。軟體平台提供核心的機器學習功能，有助於預測、路線規劃、資產利用率和維護計畫。它們能夠與現有的企業和倉庫系統無縫整合，進一步增強了其優勢。

到 2025 年，監督學習領域將佔據 70% 的市場佔有率，到 2035 年將以 25.6% 的複合年成長率成長。這些模型利用歷史資料來改善營運計劃、需求預測和績效預測，與傳統方法相比，在準確性方面取得了可衡量的提升。

北美在物流機器學習市場中佔據 32% 的佔有率，預計到 2035 年將以 22.4% 的複合年成長率成長。強大的數位基礎設施、早期技術應用以及對物流創新的持續投資，鞏固了該地區的領先地位。

目錄

第1章調查方法

第2章執行摘要

第3章業界考察

  • 生態系分析
    • 供應商情況
    • 利潤率分析
    • 成本結構
    • 每個階段的附加價值
    • 影響價值鏈的因素
    • 中斷
  • 產業影響因素
      • 促進要素
      • 加強供應鏈營運最佳化
      • 倉庫自動化
      • 電子商務領域的成長
      • 改善客戶體驗的需求日益成長
      • 物聯網整合、即時追蹤和先進的物流基礎設施
    • 產業潛在風險與挑戰
      • 數據品質和整合問題
      • 與舊有系統的整合
    • 市場機遇
      • 即時供應鏈可視性和動態最佳化
      • 庫存和供應鏈規劃的預測分析和需求預測
      • 倉庫自動化、智慧倉庫管理與機器人整合
      • 運輸資產的車隊管理和預測性維護
  • 成長潛力分析
  • 監管環境
  • 波特五力分析
  • PESTEL 分析
  • 科技與創新趨勢
    • 當前技術趨勢
    • 新興技術
  • 專利分析
  • 用例和成功案例
  • 永續性和環境方面
    • 永續實踐
    • 減少廢棄物策略
    • 生產中的能源效率
    • 環保舉措
    • 碳足跡考量
  • 未來前景與機遇

第4章 競爭情勢

  • 介紹
  • 公司市佔率分析
    • 北美洲
    • 歐洲
    • 亞太地區
    • 拉丁美洲
    • 中東和非洲
  • 主要市場公司的競爭分析
  • 競爭定位矩陣
  • 戰略展望矩陣
  • 重大進展
    • 併購
    • 夥伴關係與合作
    • 新產品發布
    • 企業擴張計畫和資金籌措

第5章 按組件分類的市場估算與預測，2022-2035年

  • 軟體
  • 服務
    • 管理
    • 面向專業人士

第6章 按技術分類的市場估計與預測，2022-2035年

  • 監督式學習
  • 無監督學習

第7章 依公司規模分類的市場估計與預測，2022-2035年

  • 主要企業
  • 中小企業

第8章 按車型分類的市場估計與預測，2022-2035年

  • 基於雲端的
  • 本地部署

第9章 按應用領域分類的市場估算與預測，2022-2035年

  • 庫存管理
  • 供應鏈規劃
  • 運輸管理
  • 倉庫管理
  • 車隊管理
  • 風險管理與安全
  • 其他

第10章 依最終用途分類的市場估計與預測，2022-2035年

  • 零售與電子商務
  • 製造業
  • 衛生保健
  • 車
  • 食品/飲料
  • 消費品
  • 其他

第11章 2022-2035年各地區市場估計與預測

  • 北美洲
    • 美國
    • 加拿大
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 義大利
    • 西班牙
    • 俄羅斯
    • 北歐國家
    • 比荷盧經濟聯盟
  • 亞太地區
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 韓國
    • 新加坡
    • 泰國
    • 印尼
    • 越南
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
    • 哥倫比亞
  • 中東和非洲
    • 南非
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國

第12章：公司簡介

  • 世界玩家
    • Amazon Web Services
    • DHL Supply Chain
    • FedEx
    • Google Cloud Platform（GCP）
    • International Business Machines（IBM）
    • Microsoft
    • Oracle
    • SAP SE
    • Uber Technologies
    • United Parcel Service
  • 區域玩家
    • Blue Yonder Group
    • CH Robinson Worldwide
    • Convoy
    • Coupa Software
    • Flexport
    • Infor
    • Locus Robotics
    • Manhattan Associates
    • Trimble
  • 新興科技創新者
    • ClearMetal
    • FourKites
    • Project44
    • Shipwell
    • Waymo LLC
簡介目錄
Product Code: 10157

The Global Machine Learning in Logistics Market was valued at USD 4.3 billion in 2025 and is estimated to grow at a CAGR of 26.7% to reach USD 44.5 billion by 2035.

Machine Learning in Logistics Market - IMG1

Machine learning is transforming logistics by enabling predictive decision-making, advanced automation, and real-time optimization across supply chain networks. Rapid digital commerce expansion, rising expectations for faster deliveries, and continued progress in artificial intelligence and connected technologies are accelerating adoption. Organizations are increasingly applying machine learning to enhance forecasting accuracy, optimize transportation routes, improve warehouse efficiency, balance inventory levels, manage fleets, and anticipate equipment issues before disruptions occur. As logistics ecosystems become more complex, machine learning solutions provide scalability, responsiveness, and operational visibility that traditional systems cannot deliver. This evolution supports improved service reliability, reduced costs, and stronger resilience across global supply chains, positioning machine learning as a foundational technology for the future of logistics.

Market Scope
Start Year2025
Forecast Year2026-2035
Start Value$4.3 Billion
Forecast Value$44.5 Billion
CAGR26.7%

Advanced machine learning models significantly improve the performance of automated logistics systems by enabling continuous learning and operational adaptation. Businesses increasingly rely on intelligent automation to handle higher order volumes, tighter delivery timelines, and frequent shipment cycles. Machine learning-driven workflows enhance accuracy, efficiency, and workforce productivity while supporting growing consumer expectations for rapid fulfillment.

The software segment held a 64% share in 2025 and is expected to grow at a CAGR of 25.1% from 2026 to 2035. Software platforms deliver core machine learning capabilities that support forecasting, routing, asset utilization, and maintenance planning. Their ability to integrate seamlessly with existing enterprise and warehouse systems reinforces their dominance.

The supervised learning segment held a 70% share in 2025 and is growing at a CAGR of 25.6% through 2035. These models leverage historical data to improve operational planning, demand estimation, and performance prediction, delivering measurable gains in accuracy compared to traditional approaches.

North America Machine Learning in Logistics Market held a 32% share and is forecast to grow at a CAGR of 22.4% through 2035. Strong digital infrastructure, early technology adoption, and sustained investment in logistics innovation support regional leadership.

Major companies operating in the Global Machine Learning in Logistics Market include SAP SE, Oracle, IBM, Microsoft Azure, Google Cloud Platform, Amazon Web Services, Blue Yonder, Manhattan Associates, DHL Supply Chain, and FedEx Corporation. Companies in the Global Machine Learning in Logistics Market strengthen their position through continuous innovation, platform integration, and strategic partnerships. Firms invest heavily in scalable cloud-based solutions that support real-time analytics and automation across supply chains. Focus on interoperability with existing enterprise systems to enhance adoption and customer retention. Many players prioritize advanced data security, predictive capabilities, and customizable solutions to meet diverse logistics requirements. Expansion into emerging markets, along with industry-specific applications, supports revenue growth.

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, 2022 - 2035
  • 2.2 Key market trends
    • 2.2.1 Regional
    • 2.2.2 Component
    • 2.2.3 Technique
    • 2.2.4 Organization Size
    • 2.2.5 Deployment Model
    • 2.2.6 Application
    • 2.2.7 End Use
  • 2.3 TAM Analysis, 2026-2035
  • 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 analysis
    • 3.1.3 Cost structure
    • 3.1.4 Value addition at each stage
    • 3.1.5 Factor affecting the value chain
    • 3.1.6 Disruptions
  • 3.2 Industry impact forces
      • 3.2.1.1 Growth drivers
      • 3.2.1.2 Increased optimization of supply chain operations
      • 3.2.1.3 Automation of warehousing operations
      • 3.2.1.4 Growth of e-commerce sector
      • 3.2.1.5 Rising need for enhanced customer experience
      • 3.2.1.6 Integration with IoT, real-time tracking, and advanced logistic infrastructure
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 Data quality and integration concern
      • 3.2.2.2 Integration with legacy systems
    • 3.2.3 Market opportunities
      • 3.2.3.1 Real-time supply-chain visibility & dynamic optimization
      • 3.2.3.2 Predictive analytics & demand forecasting for inventory and supply-chain planning
      • 3.2.3.3 Warehouse automation, smart warehousing & robotics integration
      • 3.2.3.4 Fleet management & predictive maintenance for transport assets
  • 3.3 Growth potential analysis
  • 3.4 Regulatory landscape
  • 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 Patent analysis
  • 3.9 Use cases & success stories
  • 3.10 Sustainability and environmental aspects
    • 3.10.1 Sustainable practices
    • 3.10.2 Waste reduction strategies
    • 3.10.3 Energy efficiency in production
    • 3.10.4 Eco-friendly Initiatives
    • 3.10.5 Carbon footprint considerations
  • 3.11 Future outlook and opportunities

Chapter 4 Competitive Landscape, 2025

  • 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 Component, 2022 - 2035 ($Bn)

  • 5.1 Key trends
  • 5.2 Software
  • 5.3 Services
    • 5.3.1 Managed
    • 5.3.2 Professional

Chapter 6 Market Estimates & Forecast, By Technique, 2022 - 2035 ($Bn)

  • 6.1 Key trends
  • 6.2 Supervised learning
  • 6.3 Unsupervised learning

Chapter 7 Market Estimates & Forecast, By Organization Size, 2022 - 2035 ($Bn)

  • 7.1 Key trends
  • 7.2 Large enterprises
  • 7.3 Small and medium-sized enterprises (SMEs)

Chapter 8 Market Estimates & Forecast, By Deployment Model, 2022 - 2035 ($Bn)

  • 8.1 Key trends
  • 8.2 Cloud-based
  • 8.3 On-premises

Chapter 9 Market Estimates & Forecast, By Application, 2022 - 2035 ($Bn)

  • 9.1 Key trends
  • 9.2 Inventory management
  • 9.3 Supply chain planning
  • 9.4 Transportation management
  • 9.5 Warehouse management
  • 9.6 Fleet management
  • 9.7 Risk management and security
  • 9.8 Others

Chapter 10 Market Estimates & Forecast, By End Use, 2022 - 2035 ($Bn)

  • 10.1 Key trends
  • 10.2 Retail and e-commerce
  • 10.3 Manufacturing
  • 10.4 Healthcare
  • 10.5 Automotive
  • 10.6 Food & beverage
  • 10.7 Consumer goods
  • 10.8 Others

Chapter 11 Market Estimates & Forecast, By Region, 2022 - 2035 (USD Mn, Units)

  • 11.1 Key trends
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 France
    • 11.3.4 Italy
    • 11.3.5 Spain
    • 11.3.6 Russia
    • 11.3.7 Nordics
    • 11.3.8 Benelux
  • 11.4 Asia Pacific
    • 11.4.1 China
    • 11.4.2 India
    • 11.4.3 Japan
    • 11.4.4 Australia
    • 11.4.5 South Korea
    • 11.4.6 Singapore
    • 11.4.7 Thailand
    • 11.4.8 Indonesia
    • 11.4.9 Vietnam
  • 11.5 Latin America
    • 11.5.1 Brazil
    • 11.5.2 Mexico
    • 11.5.3 Argentina
    • 11.5.4 Colombia
  • 11.6 MEA
    • 11.6.1 South Africa
    • 11.6.2 Saudi Arabia
    • 11.6.3 UAE

Chapter 12 Company Profiles

  • 12.1 Global Players
    • 12.1.1 Amazon Web Services
    • 12.1.2 DHL Supply Chain
    • 12.1.3 FedEx
    • 12.1.4 Google Cloud Platform (GCP)
    • 12.1.5 International Business Machines (IBM)
    • 12.1.6 Microsoft
    • 12.1.7 Oracle
    • 12.1.8 SAP SE
    • 12.1.9 Uber Technologies
    • 12.1.10 United Parcel Service
  • 12.2 Regional Players
    • 12.2.1 Blue Yonder Group
    • 12.2.2 C.H. Robinson Worldwide
    • 12.2.3 Convoy
    • 12.2.4 Coupa Software
    • 12.2.5 Flexport
    • 12.2.6 Infor
    • 12.2.7 Locus Robotics
    • 12.2.8 Manhattan Associates
    • 12.2.9 Trimble
  • 12.3 Emerging Technology Innovators
    • 12.3.1 ClearMetal
    • 12.3.2 FourKites
    • 12.3.3 Project44
    • 12.3.4 Shipwell
    • 12.3.5 Waymo LLC