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

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


Global Big Data in Logistics Market will witness 21.5% CAGR between 2024 and 2032, fueled by the increasing need for data-driven decision-making in supply chain management. The surge in e-commerce activities, coupled with the growing complexity of global logistics networks, is driving the demand for Big Data solutions that enhance operational efficiency and optimize resource utilization. According to Tidio, the U.S. has over 268 million online shoppers, with 70% of the population engaging in online shopping, resulting in an annual online spending of $3,428 per capita. The adoption of advanced analytics and real-time data monitoring will further bolster market expansion. The overall Big Data in Logistics Industry is categorized based on Component, Deployment Model, Organization Size, Application, End User, and Region. The Hardware segment will experience significant growth over 2024-2032. As logistics companies increasingly rely on data-driven insights to streamline operations, the demand for hardware components such as servers, storage systems, and networking equipment is rising. These hardware solutions provide the necessary infrastructure for collecting, processing, and storing vast amounts of data generated across logistics networks. The growing investment in IoT devices and sensors to monitor logistics processes in real-time is also contributing to the demand for robust hardware solutions, ensuring the seamless integration of Big Data technologies. The Manufacturing segment will play a crucial role in driving the Big Data in Logistics Market during the forecast period. Manufacturers are increasingly leveraging Big Data analytics to optimize their supply chains, reduce operational costs, and enhance product quality. By integrating Big Data solutions, manufacturers can gain valuable insights into demand forecasting, inventory management, and production planning. The focus on enhancing supply chain visibility and agility, especially in the face of global disruptions, is driving the adoption of Big Data technologies in the manufacturing sector, positioning it as a key end-user segment in the market. Europe could lead the global Big Data in Logistics Market from 2024 to 2032, owing to its advanced technological infrastructure and strong emphasis on innovation. The region’s well-established logistics sector, coupled with the increasing adoption of digital technologies, is fostering the growth of Big Data applications. Additionally, the presence of leading logistics companies and a favorable regulatory environment are supporting market expansion in Europe. Government initiatives promoting digitalization and the integration of Big Data in logistics further strengthen the region’s market position, making Europe a critical hub for the future of logistics.


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 ($Bn)
7.1 Key trends
7.2 SMEs
7.3 Large enterprises
Chapter 8 Market Estimates and Forecast, By Application, 2021 - 2032 ($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
 

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