물류용 빅데이터 시장, 기회, 성장 촉진요인, 산업 동향 분석과 예측(2024-2032년)

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.

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 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

Chapter 11 Company Profiles

• 11.1 Alteryx
• 11.2 AWS
• 11.3 Blue Yonder
• 11.4 Cloudera
• 11.5 IBM
• 11.6 Infor
• 11.7 Manhattan Associates
• 11.8 Microsoft Corporation
• 11.9 Oracle Corporation
• 11.10 Palantir
• 11.11 Qlik
• 11.12 SAP
• 11.13 Snowflake
• 11.14 Splunk
• 11.15 Teradata