Machine Learning in Logistics Market, Opportunity, Growth Drivers, Industry Trend Analysis and Forecast, 2024-2032
Machine learning in logistics market size is anticipated to witness over 23% CAGR between 2024 and 2032 led by strong demand for improved operational efficiency and cost savings. By leveraging machine learning (ML) algorithms, logistics firms can analyze extensive data sets to forecast demand, refine route planning, and enhance inventory management.
With machine learning, logistics providers can deliver precise delivery estimates, monitor shipments in real-time, and customize services based on customer history and preferences. The booming e-commerce sector, coupled with rising demands for swift and reliable deliveries, intensifies the need for ML solutions that bolster responsiveness and agility. For example, in January 2024, Lloyd List Intelligence unveiled an 'air traffic control' system for global commercial shipping, offering timely data on vessel arrivals, departures, and berth times to mitigate supply chain challenges.
The overall industry is divided into component, technique, organization size, deployment model, application, end user, and region.
Based on component, the machine learning in logistics market size from the services segment is slated to witness significant growth during 2024-2032 due to its critical role in implementing, managing, and optimizing ML solutions within the logistics sector. Services like consulting, system integration, and management are vital for firms to adeptly implement machine learning, customize solutions, and integrate them with pre-existing systems.
Machine learning in logistics market value from the fleet management segment will foresee considerable growth up to 2032. This is driven by the need for harnessing advanced analytics to optimize vehicle operations and improve overall efficiency. ML algorithms analyze data from various sources, such as GPS, telematics, and driver behavior, to enhance route planning, monitor vehicle performance, and predict maintenance needs.
Asia Pacific machine learning in logistics industry size is anticipated to witness substantial growth through 2032, fueled by swift economic progress, surging e-commerce, and a focus on supply chain refinement. With urbanization and industrial growth on the rise, APAC nations are increasingly turning to advanced logistics solutions to adeptly manage intricate supply chains and high goods volumes in the region.
- Chapter 1 Methodology and Scope
- 1.1 Market scope and definition
- 1.2 Research design
- 1.2.1 Research approach
- 1.2.2 Data collection methods
- 1.3 Base estimates and calculations
- 1.3.1 Base year calculation
- 1.3.2 Key trends for market estimation
- 1.4 Forecast model
- 1.5 Primary research and validation
- 1.5.1 Primary sources
- 1.5.2 Data mining sources
- 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 Platform provider
- 3.2.2 Software provider
- 3.2.3 Service Provider
- 3.2.4 Distribution channel
- 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 Increased optimization of supply chain operations
- 3.8.1.2 Automation of warehousing operations
- 3.8.1.3 Growth of e-commerce sector
- 3.8.1.4 Rising need for enhanced customer experience
- 3.8.2 Industry pitfalls and challenges
- 3.8.2.1 Data quality and integration concern
- 3.8.2.2 Integration with legacy systems
- 3.9 Growth potential analysis
- 3.10 Porter’s analysis
- 3.10.1 Supplier power
- 3.10.2 Buyer power
- 3.10.3 Threat of new entrants
- 3.10.4 Threat of substitutes
- 3.10.5 Industry rivalry
- 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 ($Bn)
- 5.1 Key trends
- 5.2 Software
- 5.3 Services
- 5.3.1 Managed
- 5.3.2 Professional
- Chapter 6 Market Estimates and Forecast, By Technique, 2021 - 2032 ($Bn)
- 6.1 Key trends
- 6.2 Supervised learning
- 6.3 Unsupervised learning
- Chapter 7 Market Estimates and Forecast, By Organization Size, 2021 - 2032 ($Bn)
- 7.1 Key trends
- 7.2 Large enterprises
- 7.3 Small and medium-sized enterprises (SMEs)
- Chapter 8 Market Estimates and Forecast, By Deployment Model, 2021 - 2032 ($Bn)
- 8.1 Key trends
- 8.2 Cloud-based
- 8.3 On-premises
- Chapter 9 Market Estimates and Forecast, By Application, 2021 - 2032 ($Bn)
- 9.1 Key trends
- 9.2 Inventory management
- 9.3 Supply chain planning
- 9.4 Transportation management
- 9.5 Warehouse management
- 9.6 Fleet management
- 9.7 Risk management and security
- 9.8 Others
- Chapter 10 Market Estimates and Forecast, By End User, 2021 - 2032 ($Bn)
- 10.1 Key trends
- 10.2 Retail and e-commerce
- 10.3 Manufacturing
- 10.4 Healthcare
- 10.5 Automotive
- 10.6 Food and beverage
- 10.7 Consumer goods
- 10.8 Others
- Chapter 11 Market Estimates and Forecast, By Region, 2021 - 2032 ($Bn)
- 11.1 Key trends
- 11.2 North America
- 11.2.1 U.S.
- 11.2.2 Canada
- 11.3 Europe
- 11.3.1 UK
- 11.3.2 Germany
- 11.3.3 France
- 11.3.4 Italy
- 11.3.5 Spain
- 11.3.6 Russia
- 11.3.7 Nordics
- 11.3.8 Rest of Europe
- 11.4 Asia Pacific
- 11.4.1 China
- 11.4.2 India
- 11.4.3 Japan
- 11.4.4 Australia
- 11.4.5 South Korea
- 11.4.6 Southeast Asia
- 11.4.7 Rest of Asia Pacific
- 11.5 Latin America
- 11.5.1 Brazil
- 11.5.2 Mexico
- 11.5.3 Argentina
- 11.5.4 Rest of Latin America
- 11.6 MEA
- 11.6.1 UAE
- 11.6.2 South Africa
- 11.6.3 Saudi Arabia
- 11.6.4 Rest of MEA
- Chapter 12 Company Profiles
- 12.1 Amazon Web Services, Inc. (AWS)
- 12.2 Blue Yonder Group, Inc.
- 12.3 C.H. Robinson Worldwide, Inc.
- 12.4 Convoy, Inc.
- 12.5 Coupa Software Inc.
- 12.6 DHL Supply Chain
- 12.7 FedEx Corporation
- 12.8 Flexport, Inc.
- 12.9 Google LLC
- 12.10 Infor, Inc.
- 12.11 International Business Machines Corporation (IBM)
- 12.12 Locus Robotics Corporation
- 12.13 Manhattan Associates, Inc.
- 12.14 Microsoft Corporation
- 12.15 Oracle Corporation
- 12.16 SAP SE
- 12.17 Trimble Inc.
- 12.18 Uber Technologies, Inc.
- 12.19 United Parcel Service, Inc.
- 12.20 Waymo LLC
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