物流市场数位孪生、机会、成长动力、产业趋势分析与预测，2024-2032

2023 年，物流市场的全球数位孪生估值为 12 亿美元，预计 2024 年至 2032 年复合年增长率将超过 25.7%。到路线优化，透过即时洞察显着提高营运效率。

最终用户越来越多地将数位孪生与人工智慧 (AI) 和机器学习 (ML) 技术整合在一起。这种融合增强了数位孪生的预测能力，从而实现更敏锐的预测和优化。人工智慧和机器学习演算法筛选来自数位孪生的大量资料，辨别模式并做出即时决策。例如，在路线优化中，人工智慧增强的数位孪生可以考虑交通、天气和历史资料，即时修改送货路线。

物流业的数位孪生分为组件、部署模型、应用程式、最终用户和区域。

市场依组件分为软体和服务。 2023 年，软体领域的销售额约为 8.93 亿美元。物联网 (IoT) 设备和感测器的整合显着增强了数位孪生软体的功能。这些增强功能有助于从物流网路内的资产、车辆和基础设施收集即时资料。这些详细的资料对于製作有形系统的精确数位复製品至关重要。例如，2024 年 3 月，DHL 利用数位孪生技术製作其仓库的虚拟模型。

市场根据部署模式将物流中的数位孪生分为基于云端的和本地的。预计到 2032 年，基于云端的细分市场将超过 75 亿美元。在高峰时期或不可预见的高峰期间，企业可以迅速升级其基础设施，而无需大量资本支出。这种适应性不仅可以确保最佳性能，还可以提高效率和客户满意度。

2023年，北美在物流市场的数位孪生中处于领先地位，占据约31%的收入份额。在美国的引领下，该地区处于技术进步的前沿。物联网、人工智慧和巨量资料分析的快速发展和采用对于推动数位孪生在物流领域的应用至关重要。该地区的公司利用这些技术来提高营运效率、完善决策并确保竞争优势。

目录

第 1 章：方法与范围

第 2 章：执行摘要

第 3 章：产业洞察

• 产业生态系统分析
• 供应商格局
  • 软体供应商
  • 物流服务商
  • 技术提供者
  • 终端用户
• 利润率分析
• 技术和创新格局
• 专利分析
• 重要新闻和倡议
• 监管环境
• 衝击力
  • 成长动力
    • 对物流营运即时洞察的需求不断增长
    • 对数据驱动决策的需求不断增长
    • 物流业的技术进步
    • 物流企业越来越注重降低成本
  • 产业陷阱与挑战
    • 数据整合挑战
    • 数位孪生实施复杂性
• 成长潜力分析
• 波特的分析
• PESTEL分析

第 4 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 5 章：市场估计与预测：按组成部分，2021 - 2032 年

• 主要趋势
• 软体
• 服务
  • 託管服务
  • 专业服务
    • 咨询服务
    • 整合和实施服务
    • 支援和维护服务

第 6 章：市场估计与预测：按部署模型，2021 - 2032 年

• 主要趋势
• 基于云端
• 本地

第 7 章：市场估计与预测：按应用分类，2021 - 2032

• 主要趋势
• 路线优化
• 仓库和库存管理
• 预测性维护
• 资产追踪
• 其他的

第 8 章：市场估计与预测：按最终用户划分，2021 - 2032 年

• 主要趋势
• 汽车
• 航太和国防
• 製造业
• 零售及电子商务
• 能源和公用事业
• 其他的

第 9 章：市场估计与预测：按地区，2021 - 2032

• 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
  • 欧洲其他地区
• 亚太地区
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳新银行
  • 东南亚
  • 亚太地区其他地区
• 拉丁美洲
  • 巴西
  • 墨西哥
  • 阿根廷
  • 拉丁美洲其他地区
• MEA
  • 南非
  • 沙乌地阿拉伯
  • 阿联酋
  • MEA 的其余部分

第 10 章：公司简介

• AAG IT Services
• AVEVA (Schneider Electric Group)
• Blue Yonder
• Bosch Rexroth
• Dassault Systemes
• General Electric
• IBM
• Kinaxis, Inc.
• Microsoft Solutions
• Oracle
• SAP
• Siemens Digital Industries Software
• Simio LLC
• The Anylogic Company

The Global Digital Twin in Logistics Market was valued at USD 1.2 billion in 2023 and is projected to grow at a CAGR of over 25.7% from 2024 to 2032. By creating a virtual replica of their physical logistics network, companies can monitor and analyze every facet of their operations, from warehouse management to route optimization, significantly boosting operational efficiency through real-time insights.

End-users are increasingly integrating digital twins with artificial intelligence (AI) and machine learning (ML) technologies. This fusion amplifies the predictive prowess of digital twins, leading to sharper forecasting and optimization. AI and ML algorithms sift through vast data from digital twins, discerning patterns and making instantaneous decisions. For example, in route optimization, AI-enhanced digital twins can modify delivery routes in real-time, factoring in traffic, weather, and historical data.

The digital twin in logistics industry is bifurcated into component, deployment model, application, end user, and region.

The market is segmented by component into software and services. In 2023, the software segment accounted for roughly USD 893 million. The capabilities of digital twin software have been significantly bolstered by the integration of Internet of Things (IoT) devices and sensors. These enhancements facilitate real-time data gathering from assets, vehicles, and infrastructure within the logistics network. Such detailed data is vital for crafting precise digital replicas of tangible systems. For instance, in March 2024, DHL harnessed digital twin technology to craft virtual models of its warehouses.

The market categorizes the digital twin in logistics by deployment model into cloud-based and on-premises. The cloud-based segment is projected to surpass USD 7.5 billion by 2032. These cloud solutions offer unparalleled scalability, allowing logistics firms to modulate computing resources in response to demand shifts. During peak times or unforeseen surges, businesses can swiftly upscale their infrastructure without hefty capital outlays. This adaptability not only ensures peak performance but also bolsters efficiency and customer satisfaction.

In 2023, North America led the digital twin in logistics market, capturing about 31% of the revenue share. Spearheaded by the U.S., this region stands at the vanguard of technological advancements. The swift evolution and adoption of IoT, AI, and big data analytics are pivotal in driving the uptake of digital twins in logistics. Companies in this region harness these technologies to boost operational efficiency, refine decision-making, and secure a competitive edge.

Table of Contents

Chapter 1 Methodology and 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 estimation
• 1.3 Forecast model
• 1.4 Primary research and validation
  • 1.4.1 Primary sources
  • 1.4.2 Data mining sources
• 1.5 Market definitions

Chapter 2 Executive Summary

• 2.1 Industry 360° synopsis, 2021 - 2032

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
• 3.2 Supplier landscape
  • 3.2.1 Software providers
  • 3.2.2 Logistics service providers
  • 3.2.3 Technology providers
  • 3.2.4 End-user
• 3.3 Profit margin analysis
• 3.4 Technology and innovation landscape
• 3.5 Patent analysis
• 3.6 Key news and initiatives
• 3.7 Regulatory landscape
• 3.8 Impact forces
  • 3.8.1 Growth drivers
    • 3.8.1.1 Growing demand for real-time insights into logistics operations
    • 3.8.1.2 Rising need for data-driven decision-making
    • 3.8.1.3 Technological advancements in the logistics industry
    • 3.8.1.4 Growing focus of logistics companies on cost reduction
  • 3.8.2 Industry pitfalls and challenges
    • 3.8.2.1 Data integration challenges
    • 3.8.2.2 Digital twin implementation complexity
• 3.9 Growth potential analysis
• 3.10 Porter's analysis
• 3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

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

Chapter 5 Market Estimates and Forecast, By Component, 2021 - 2032 ($Bn)

• 5.1 Key trends
• 5.2 Software
• 5.3 Services
  • 5.3.1 Managed services
  • 5.3.2 Professional services
    • 5.3.2.1 Consulting services
    • 5.3.2.2 Integration and implementation services
    • 5.3.2.3 Support and maintenance services

Chapter 6 Market Estimates and Forecast, By Deployment Model, 2021 - 2032 ($Bn)

• 6.1 Key trends
• 6.2 Cloud-based
• 6.3 On-premises

Chapter 7 Market Estimates and Forecast, By Application, 2021 - 2032 ($Bn)

• 7.1 Key trends
• 7.2 Route optimization
• 7.3 Warehouse and inventory management
• 7.4 Predictive maintenance
• 7.5 Asset tracking
• 7.6 Others

Chapter 8 Market Estimates and Forecast, By End User, 2021 - 2032 ($Bn)

• 8.1 Key trends
• 8.2 Automotive
• 8.3 Aerospace and defense
• 8.4 Manufacturing
• 8.5 Retail and E-commerce
• 8.6 Energy and utilities
• 8.7 Others

Chapter 9 Market Estimates and Forecast, By Region, 2021 - 2032 ($Bn)

• 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
  • 9.3.8 Rest of Europe
• 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
  • 9.4.7 Rest of Asia Pacific
• 9.5 Latin America
  • 9.5.1 Brazil
  • 9.5.2 Mexico
  • 9.5.3 Argentina
  • 9.5.4 Rest of Latin America
• 9.6 MEA
  • 9.6.1 South Africa
  • 9.6.2 Saudi Arabia
  • 9.6.3 UAE
  • 9.6.4 Rest of MEA

Chapter 10 Company Profiles

• 10.1 AAG IT Services
• 10.2 AVEVA (Schneider Electric Group)
• 10.3 Blue Yonder
• 10.4 Bosch Rexroth
• 10.5 Dassault Systemes
• 10.6 General Electric
• 10.7 IBM
• 10.8 Kinaxis, Inc.
• 10.9 Microsoft Solutions
• 10.10 Oracle
• 10.11 SAP
• 10.12 Siemens Digital Industries Software
• 10.13 Simio LLC
• 10.14 The Anylogic Company