Machine Learning in Logistics Market
Description
Machine Learning in Logistics Market
Machine Learning in Logistics market size was valued at USD 2.8 billion in 2023 and is estimated to register a CAGR of over 23% 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.
Table of Contents
168 Pages
- Chapter 1 Research Methodology
- 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 estimates
- 1.3 Forecast model
- 1.4 Primary research & 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
- 2.2 Business trends
- 2.2.1 Total Addressable Market (TAM), 2024 - 2032
- 2.2.1.1 TAM trends
- 2.3 Regional trends
- 2.4 Component trends
- 2.5 Technique trends
- 2.6 Organization size trends
- 2.7 Deployment model trends
- 2.8 Application trends
- 2.9 End-user trends
- Chapter 3 Industry Insights
- 3.1 Industry ecosystem
- 3.1.1 Platform provider
- 3.1.2 Software provider
- 3.1.3 Service provider
- 3.1.4 Distribution channel
- 3.1.5 End-user
- 3.2 Supplier landscape
- 3.2.1 Supplier landscape
- 3.3 Technology and innovation landscape
- 3.3.1 Cloud computing 3.3.2 Big data analytics
- 3.3.3 Robotic process automation (RPA)
- 3.3.4 Predictive analytics
- 3.4 Patent analysis
- 3.5 Key news and initiatives
- 3.6 Regulatory landscape
- 3.6.1 North America
- 3.6.1.1 California Consumer Privacy Act (CCPA)
- 3.6.1.2 Gramm-Leach-Bliley Act (GLBA)
- 3.6.1.3 Federal Trade Commission (FTC) Regulations
- 3.6.1.4 Personal Information Protection and Electronic Documents Act (PIPEDA)
- 3.6.1.5 Transportation Regulations
- 3.6.2 Europe
- 3.6.2.1 UK General Data Protection Regulation (UK GDPR)
- 3.6.2.2 Federal Data Protection Act (Bundesdatenschutzgesetz, BDSG)
- 3.6.2.3 French Data Protection Act (Loi Informatique et Libertés)
- 3.6.2.4 Italian Data Protection Code (Codice in materia di protezione dei dati personali)
- 3.6.2.5 Organic Law on Data Protection and Digital Rights Guarantee (LOPDGDD)
- 3.6.2.6 Federal Law on Personal Data (No. 152-FZ)
- 3.6.2.7 National Data Protection Laws
- 3.6.3 Asia Pacific
- 3.6.3.1 Cybersecurity Law
- 3.6.3.2 National Strategy for Artificial Intelligence
- 3.6.3.3 Strategic Innovation Promotion Program (SIP)
- 3.6.3.4 Smart Logistics Initiative
- 3.6.3.5 AI Ethics Framework
- 3.6.3.6 Law No. 27 of 2022 on Personal Data Protection (PDP Law)
- 3.6.4 Latin America
- 3.6.4.1 General Data Protection Law (Lei Geral de Proteção de Dados - LGPD)
- 3.6.4.2 Federal Law on Protection of Personal Data Held by Private Parties (Ley Federal de Protección de Datos Personales en Posesión de los Particulares)
- 3.6.4.3 Personal Data Protection Law (Law No.
- 25.326)
- 3.6.5 MEA
- 3.6.5.1 Federal Decree-Law No. 45 of 2021 on the Protection of Personal Data
- 3.6.5.2 Personal Data Protection Law (PDPL)
- 3.6.5.3 Protection of Personal Information Act (POPIA)
- 3.7 Industry impact forces
- 3.7.1 Growth drivers
- 3.7 .
- 1.1 Incre ased o p t im iz at io n o f s upply cha in o per a tio n s
- 3.7.1.2 Automation of warehousing operations
- 3.7 .1. 3 Gro wth o f e-co mmer ce s ecto r
- 3.7 .1. 4 R is ing need fo r enhan ced cu sto me r expe rience
- 3.7.2 Industry pitfalls and challenges
- 3.7.2.1 Data quality and integration concern
- 3.7.2.2 Integration with legacy systems
- 3.8 Growth potential analysis
- 3.9 Porter's analysis
- 3.10 PESTEL analysis
- Chapter 4 Competitive Landscape
- 4.1 Introduction
- 4.2 Company market share analysis
- 4.3 Competitive positioning matrix
- 4.4 Strategic outlook matrix
- Chapter 5 Machine Learning in Logistics Market, By Component
- 5.1 Key trends
- 5.2 Software
- 5.3 Services
- Chapter 6 Machine Learning in Logistics Market, By Technique
- 6.1 Key trends
- 6.2 Supervised learning
- 6.3 Unsupervised learning
- Chapter 7 Machine Learning in Logistics Market, By Organization Size
- 7.1 Key trends
- 7.2 Large enterprises
- 7.3 Small and medium-sized enterprises (SMEs)
- Chapter 8 Machine Learning in Logistics Market, By Deployment Model
- 8.1 Key trends
- 8.2 Cloud-based
- 8.3 On-premises
- Chapter 9 Machine Learning in Logistics Market, By Application
- 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 Machine Learning in Logistics Market, By End Users
- 10.1 Key trends
- 10.2 Retail and E-commerce
- 10.3 Manufacturing
- 10.4 Healthcare
- 10.5 Automotive
- 10.6 Food & Beverage
- 10.7 Consumer Goods
- 10.8 Others
- Chapter 11 Machine Learning in Logistics Market, By Region
- 11.1 Key trends
- 11.2 North America
- 11.3 Europe
- 11.4 Asia Pacific
- 11.5 Latin America
- 11.6 MEA
- Chapter 12 Company Profiles
- 12.1 Amazon Web Services (AWS)
- 12.1.1 Global overview
- 12.1.2 Market/Business overview
- 12.1.3 Financial data
- 12.1.3.1 Sales revenue, 2021-2023
- 12.1.4 Product landscape
- 12.1.5 Strategic outlook
- 12.1.6 SWOT analysis
- 12.2 Blue Yonder Group, Inc.
- 12.2.1 Global overview
- 12.2.2 Market/Business overview
- 12.2.3 Financial data
- 12.2.3.1 Sales Revenue, 2022-2024
- 12.2.4 Product landscape
- 12.2.5 Strategic outlook
- 12.2.6 SWOT analysis
- 12.3 C.H. Robinson Worldwide, Inc.
- 12.3.1 Global overview
- 12.3.2 Market/Business overview
- 12.3.3 Financial data
- 12.3.3.1 Sales Revenue, 2021-2023
- 12.3.4 Product landscape
- 12.3.5 Strategic outlook
- 12.3.6 SWOT analysis
- 12.4 Coupa Software Inc.
- 12.4.1 Global overview
- 12.4.2 Market/Business overview
- 12.4.3 Financial data
- 12.4.4 Product landscape
- 12.4.5 Strategic outlook
- 12.4.6 SWOT analysis
- 12.5 DHL Supply Chain
- 12.5.1 Global Overview
- 12.5.2 Market/Business Overview
- 12.5.3 Financial Data
- 12.5.3.1 Sales Revenue, 2022-2024
- 12.5.4 Product Landscape
- 12.5.5 Strategic outlook
- 12.5.6 SWOT analysis
- 12.6 FedEx Corporation
- 12.6.1 Global overview
- 12.6.2 Market/Business overview
- 12.6.3 Financial data
- 12.6.3.1 Sales Revenue, 2021-2023
- 12.6.4 Product landscape
- 12.6.5 Strategic outlook
- 12.6.6 SWOT analysis
- 12.7 Flexport, Inc.
- 12.7.1 Global Overview
- 12.7.2 Market/Business Overview
- 12.7.3 Financial data
- 12.7.4 Product Landscape
- 12.7.5 Strategic outlook
- 12.7.6 SWOT analysis
- 12.8 Google LLC
- 12.8.1 Global Overview
- 12.8.2 Market/Business Overview
- 12.8.3 Financial Data
- 12.8.3.1 Sales Revenue, 2021-2023
- 12.8.4 Product Landscape
- 12.8.5 Strategic outlook
- 12.8.6 SWOT analysis
- 12.9 Infor, Inc.
- 12.9.1 Global Overview
- 12.9.2 Market/Business Overview
- 12.9.3 Financial data
- 12.9.4 Product Landscape
- 12.9.5 Strategic outlook
- 12.9.6 SWOT analysis
- 12.10 International Business Machines Corporation (IBM)
- 12.10.1 Global Overview
- 12.10.2 Market/Business Overview
- 12.10.3 Financial Data
- 12.10.3.1 Sales Revenue, 2021-2023
- 12.10.4 Product Landscape
- 12.10.5 Strategic Outlook
- 12.10.6 SWOT analysis
- 12.11 Locus Robotics Corporation
- 12.11.1 Global Overview
- 12.11.2 Market/Business Overview
- 12.11.3 Financial data
- 12.11.4 Product Landscape
- 12.11.5 Strategic Outlook
- 12.11.6 SWOT analysis
- 12.12 Manhattan Associates, Inc.
- 12.12.1 Global Overview
- 12.12.2 Market/Business Overview
- 12.12.3 Financial data
- 12.12.4 Sales Revenue, 2021-2023
- 12.12.5 Product Landscape
- 12.12.6 Strategic Outlook
- 12.12.7 SWOT analysis
- 12.13 Microsoft Corporation
- 12.13.1 Global Overview
- 12.13.2 Market/Business Overview
- 12.13.3 Financial Data
- 12.13.3.1 Sales Revenue, 2022-2024
- 12.13.4 Product Landscape
- 12.13.5 Strategic Outlook
- 12.13.6 SWOT analysis
- 12.14 Oracle Corporation
- 12.14.1 Global overview
- 12.14.2 Market/Business overview
- 12.14.3 Financial data
- 12.14.3.1 Sales Revenue, 2022-2024
- 12.14.4 Product landscape
- 12.14.5 Strategic Outlook
- 12.14.6 SWOT analysis
- 12.15 SAP SE
- 12.15.1 Global overview
- 12.15.2 Market/Business overview
- 12.15.3 Financial data
- 12.15.3.1 Sales Revenue, 2021-2023
- 12.15.4 Product landscape
- 12.15.5 Strategic Outlook
- 12.15.6 SWOT analysis
- 12.16 Siemens AG
- 12.16.1 Global overview
- 12.16.2 Market/Business overview
- 12.16.3 Financial data
- 12.16.3.1 Sales Revenue, 2021-2023
- 12.16.4 Product landscape
- 12.16.5 Strategic Outlook
- 12.16.6 SWOT analysis
- 12.17 Trimble Inc.
- 12.17.1 Global overview
- 12.17.2 Market/Business overview
- 12.17.3 Financial data
- 12.17.3.1 Sales Revenue, 2021-2023
- 12.17.4 Product landscape
- 12.17.5 Strategic Outlook
- 12.17.6 SWOT analysis
- 12.18 Uber Technologies, Inc
- 12.18.1 Global overview
- 12.18.2 Market/Business overview
- 12.18.3 Financial data
- 12.18.3.1 Sales Revenue, 2021-2023
- 12.18.4 Product landscape
- 12.18.5 Strategic Outlook
- 12.18.6 SWOT analysis
- 12.19 United Parcel Service, Inc.
- 12.19.1 Global overview
- 12.19.2 Market/Business overview
- 12.19.3 Financial data
- 12.19.3.1 Sales Revenue, 2021-2023
- 12.19.4 Product landscape
- 12.19.5 Strategic Outlook
- 12.19.6 SWOT analysis
- 12.20 XPO Logistics
- 12.20.1 Global Overview
- 12.20.2 Market/Business Overview
- 12.20.3 Financial Data
- 12.20.3.1 Sales Revenue, 2021-2023
- 12.20.4 Product Landscape
- 12.20.5 Strategic outlook
- 12.20.6 SWOT analysis
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