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運輸/物流

市場調查報告書

商品編碼

1570872

物流市場巨量資料、機會、成長動力、產業趨勢分析與預測，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個工作天內

價格

簡介目錄

2023資料，全球物流巨量資料市場價值為 43 億美元，預計 2024 年至 2032 年複合年成長率將超過 21.5%。進行有效管理的分析。巨量資料使物流公司能夠透過提供對庫存水準、需求預測和貨運追蹤的即時洞察來最佳化供應鏈營運。這可以實現更有效率的路線規劃、降低燃料成本並縮短交貨時間。

即時資料有助於識別和減輕干擾，例如自然災害或港口擁塞。巨量資料還透過提高效率、降低成本和提高客戶滿意度來改變物流行業。例如，2024年3月，美國交通部發布了一份報告，強調了巨量資料在改善國家物流基礎設施方面的好處。

整個產業分為組件、部署模型、組織規模、應用程式、最終用戶和區域。

根據組件，市場分為硬體、軟體和服務。 2023年，軟體佔據的市佔率將超過51%。巨量資料物流市場中的軟體部分包括基本元件，例如資料管理、分析、運輸管理系統（TMS）、倉庫管理系統（WMS）和供應鏈管理解決方案。對即時資料分析和預測洞察的需求不斷成長，極大地推動了資料管理和分析軟體的採用。這些工具使物流公司能夠最佳化路線、管理庫存、預測需求並提高整體供應鏈效率。

根據部署模型，物流市場巨量資料分為雲端和本地。到 2032 年，基於雲端的解決方案預計將超過 186 億美元。它提供可擴展性、靈活性和成本效益，這對於管理物流營運中產生的大量資料至關重要。這些解決方案允許根據需求擴展資源，從而減少對硬體進行大量資本投資的必要性。

北美在物流市場的巨量資料中佔有很大佔有率，到 2023 年將佔收入佔有率的 35% 左右。美國因其先進的基礎設施和強勁的經濟而佔據主導地位，加拿大也對市場做出了重大貢獻。公路憑藉其靈活性和廣泛的網路覆蓋範圍在該地區的物流市場中佔據主導地位。這種模式對於最後一哩配送和進入偏遠地區至關重要。

產業生態系統分析
供應商格局
- 硬體提供者
- 軟體供應商
- 服務商
- 技術提供者
- 終端用戶
利潤率分析
技術與創新格局
專利分析
重要新聞和舉措
監管環境
衝擊力
- 成長動力
  - 對供應鏈可視性的需求不斷成長
  - 節省成本並提高營運效率
  - 不斷成長的電子商務市場
  - 監理合規要求
- 產業陷阱與挑戰
  - 資料品質、完整性、安全性和隱私
  - 實施成本高
成長潛力分析
波特的分析
PESTEL分析

第 4 章：競爭格局

介紹
公司市佔率分析
競爭定位矩陣
戰略展望矩陣

第 5 章：市場估計與預測：按組成部分，2021 - 2032 年

主要趨勢
硬體
軟體
服務
- 專業服務
- 託管服務

第 6 章：市場估計與預測：按部署模型，2021 - 2032 年

主要趨勢
本地
基於雲端

第 7 章：市場估計與預測：按組織規模，2021 - 2032 年

主要趨勢
中小企業
大型企業

第 8 章：市場估計與預測：按應用分類，2021 - 2032

主要趨勢
供應鏈最佳化
倉庫管理
車隊管理
預測分析
其他

第 9 章：市場估計與預測：按最終用戶分類，2021 - 2032 年

主要趨勢
運輸和船運公司
製造業
零售
第三方物流
其他

第 10 章：市場估計與預測：按地區，2021 - 2032

主要趨勢
北美洲
- 美國
- 加拿大
歐洲
- 英國
- 德國
- 法國
- 義大利
- 西班牙
- 北歐人
- 歐洲其他地區
亞太地區
- 中國
- 印度
- 日本
- 韓國
- 澳新銀行
- 東南亞
- 亞太地區其他地區
拉丁美洲
- 巴西
- 墨西哥
- 阿根廷
- 拉丁美洲其他地區
MEA
- 阿拉伯聯合大公國
- 南非
- 沙烏地阿拉伯
- MEA 的其餘部分

第 11 章：公司簡介

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

簡介目錄

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