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运输/物流

市场调查报告书

商品编码

1570872

物流市场大数据、机会、成长动力、产业趋势分析与预测，2024-2032

Big Data in Logistics Market, Opportunity, Growth Drivers, Industry Trend Analysis and Forecast, 2024-2032

出版日期: 2024年08月06日 | 出版商:

Global Market Insights Inc. | 英文 260 Pages | 商品交期: 2-3个工作天内

价格

简介目录

2023资料，全球物流大数据市场价值为 43 亿美元，预计 2024 年至 2032 年复合年增长率将超过 21.5%。进行有效管理的分析。巨量资料使物流公司能够透过提供对库存水准、需求预测和货运追踪的即时洞察来优化供应链营运。这可以实现更有效率的路线规划、降低燃料成本并缩短交货时间。

即时资料有助于识别和减轻干扰，例如自然灾害或港口拥塞。巨量资料还透过提高效率、降低成本和提高客户满意度来改变物流行业。例如，2024年3月，美国交通部发布了一份报告，强调了巨量资料在改善国家物流基础设施方面的好处。

整个产业分为组件、部署模型、组织规模、应用程式、最终用户和区域。

根据组件，市场分为硬体、软体和服务。 2023年，软体占据的市占率将超过51%。巨量资料物流市场中的软体部分包括基本元件，例如资料管理、分析、运输管理系统（TMS）、仓库管理系统（WMS）和供应链管理解决方案。对即时资料分析和预测洞察的需求不断增长，极大地推动了资料管理和分析软体的采用。这些工具使物流公司能够优化路线、管理库存、预测需求并提高整体供应链效率。

根据部署模型，物流市场巨量资料分为云端和本地。到 2032 年，基于云端的解决方案预计将超过 186 亿美元。它提供可扩展性、灵活性和成本效益，这对于管理物流营运中产生的大量资料至关重要。这些解决方案允许根据需求扩展资源，从而减少对硬体进行大量资本投资的必要性。

北美在物流市场的巨量资料中占有很大份额，到 2023 年将占收入份额的 35% 左右。美国因其先进的基础设施和强劲的经济而占据主导地位，加拿大也对市场做出了重大贡献。公路凭藉其灵活性和广泛的网路覆盖范围在该地区的物流市场中占据主导地位。这种模式对于最后一哩配送和进入偏远地区至关重要。

产业生态系统分析
供应商格局
- 硬体提供者
- 软体供应商
- 服务商
- 技术提供者
- 终端用户
利润率分析
技术与创新格局
专利分析
重要新闻和倡议
监管环境
衝击力
- 成长动力
  - 对供应链可视性的需求不断增长
  - 节省成本并提高营运效率
  - 不断成长的电子商务市场
  - 监理合规要求
- 产业陷阱与挑战
  - 资料品质、完整性、安全性和隐私
  - 实施成本高
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
硬体
软体
服务
- 专业服务
- 託管服务

第 6 章：市场估计与预测：按部署模型，2021 - 2032 年

主要趋势
本地
基于云端

第 7 章：市场估计与预测：按组织规模，2021 - 2032 年

主要趋势
中小企业
大型企业

第 8 章：市场估计与预测：按应用分类，2021 - 2032

主要趋势
供应链优化
仓库管理
车队管理
预测分析
其他的

第 9 章：市场估计与预测：按最终用户划分，2021 - 2032 年

主要趋势
运输和船运公司
製造业
零售
第三方物流
其他的

第 10 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
- 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
- 阿联酋
- 南非
- 沙乌地阿拉伯
- MEA 的其余部分

第 11 章：公司简介

Alteryx
AWS
Blue Yonder
Cloudera
IBM
Infor
Manhattan Associates
Microsoft Corporation
Oracle Corporation
Palantir
Qlik
SAP
Snowflake
Splunk
Teradata

简介目录

Product Code: 10677

The Global Big Data in Logistics Market was valued at USD 4.3 billion in 2023 and is projected to grow at a CAGR of over 21.5% from 2024 to 2032. The expansion of global supply chains is generating vast amounts of data from multiple sources, necessitating advanced analytics for effective management. Big data enables logistics companies to optimize supply chain operations by providing real-time insights into inventory levels, demand forecasts, and shipment tracking. This leads to more efficient route planning, reduced fuel costs, and improved delivery times.

Real-time data helps identify and mitigate disruptions, such as natural disasters or port congestion. Big data is also transforming the logistics industry by enhancing efficiency, reducing costs, and improving customer satisfaction. For instance, in March 2024, the U.S. Department of Transportation released a report highlighting the benefits of big data in improving national logistics infrastructure.

The overall industry is divided into component, deployment model, organization size, application, end user, and region.

Based on component, the market is divided into hardware, software, and services. In 2023, software accounted for a market share of over 51%. The software segment within the big data logistics market includes essential components, such as data management, analytics, transportation management systems (TMS), warehouse management systems (WMS), and supply chain management solutions. The increasing demand for real-time data analysis and predictive insights has significantly driven the adoption of data management and analytics software. These tools enable logistics companies to optimize routes, manage inventory, predict demand, and enhance overall supply chain efficiency.

Based on deployment model, the big data in logistics market is categorized into cloud-based and on-premises. Cloud-based solutions are expected to hold over USD 18.6 billion by 2032. Logistics companies are leveraging big data analytics through this model, eliminating the need for extensive on-premises infrastructure. It offers scalability, flexibility, and cost-efficiency, which are essential for managing the large volumes of data generated in logistics operations. These solutions allow for resource scaling in terms of demand, reducing the necessity for significant capital investments in hardware.

North America has a significant share of the big data in logistics market with around 35% of the revenue share in 2023. This is driven by advancements in technology and increasing demand for efficient logistics solutions. The U.S. dominates due to its advanced infrastructure and robust economy, with Canada also contributing significantly to the market. Roadways dominate the logistics market in the region due to their flexibility and extensive network coverage. This mode is crucial for last-mile delivery and accessing remote areas.

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 Hardware providers
- 3.2.2 Software providers
- 3.2.3 Service provider
- 3.2.4 Technology providers
- 3.2.5 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 Rising demand for supply chain visibility
  - 3.8.1.2 Cost savings and improved operational efficiency
  - 3.8.1.3 Growing e-commerce market
  - 3.8.1.4 Regulatory compliance requirements
- 3.8.2 Industry pitfalls and challenges
  - 3.8.2.1 Data quality, integrity, security and privacy
  - 3.8.2.2 High cost of implementation
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 Hardware
5.3 Software
5.4 Services
- 5.4.1 Professional services
- 5.4.2 Managed services

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

6.1 Key trends
6.2 On-premises
6.3 Cloud-based

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

7.1 Key trends
7.2 SME
7.3 Large enterprises

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

8.1 Key trends
8.2 Supply chain optimization
8.3 Warehouse management
8.4 Fleet management
8.5 Predictive analytics
8.6 Others

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

9.1 Key trends
9.2 Transportation and shipping companies
9.3 Manufacturing
9.4 Retail
9.5 Third-party logistics
9.6 Others

Chapter 10 Market Estimates and Forecast, By Region, 2021 - 2032 ( $Bn)

10.1 Key trends
10.2 North America
- 10.2.1 U.S.
- 10.2.2 Canada
10.3 Europe
- 10.3.1 UK
- 10.3.2 Germany
- 10.3.3 France
- 10.3.4 Italy
- 10.3.5 Spain
- 10.3.6 Nordics
- 10.3.7 Rest of Europe
10.4 Asia Pacific
- 10.4.1 China
- 10.4.2 India
- 10.4.3 Japan
- 10.4.4 South Korea
- 10.4.5 ANZ
- 10.4.6 Southeast Asia
- 10.4.7 Rest of Asia Pacific
10.5 Latin America
- 10.5.1 Brazil
- 10.5.2 Mexico
- 10.5.3 Argentina
- 10.5.4 Rest of Latin America
10.6 MEA
- 10.6.1 UAE
- 10.6.2 South Africa
- 10.6.3 Saudi Arabia
- 10.6.4 Rest of MEA