Generative AI in Logistics Market, By Type; By Component; By Deployment Mode; By Application; By End User; By Region, Global Trend Analysis, Competitive Landscape & Forecast, 2019-2031

Description

Global Generative AI in Logistics Market Zooming 14X to Touch USD 16 Billion by 2031

Global Generative AI in Logistics Market is flourishing because of the rising adoption of automation and AI technologies to optimize supply chain processes and growing need for enhanced decision-making capabilities in logistics operations.

BlueWeave Consulting, a leading strategic consulting and market research firm, in its recent study, estimated Global Generative AI in Logistics Market size at USD 1.10 billion in 2024. During the forecast period between 2025 and 2031, BlueWeave expects Global Generative AI in Logistics Market size to expand at a robust CAGR of 44.20% reaching a value of USD 15.60 billion by 2031. Increasing investments in artificial intelligence (AI) to standardize procedures and improve last-mile delivery is one of the key growth drivers for Global Generative AI in Logistics Market. The logistics industry benefits from generative AI in a number of ways, including supply chain automation, demand forecasting, warehousing and inventory management, and route optimization, which enables business actors to make informed choices instantly.

Opportunity - Advancements in AI Technology and Data Availability

Rising investments in and evolution of AI technologies are projected to present lucrative growth opportunities for Global Generative AI in Logistics Market. AI models are now able to leverage vast amounts of data being generated from IoT devices, GPS, and other sensors in logistics operations that can be used to train these systems and generate highly accurate predictions and optimizations. Furthermore, advancements in machine learning (ML) algorithms, natural language processing (NLP), and neural networks are constantly improving the ability of generative AI to analyze vast datasets and automate decision-making. These advancements make generative AI more accessible and effective for logistics companies, leading to their rapid adoption across the sector.

Impact of Escalating Geopolitical Tensions on Global Generative AI in Logistics Market

Intensifying geopolitical tensions could propel the growth of Global Generative AI in Logistics Market. The global supply chain is disrupted by geopolitical conflicts because of trade restrictions, border closures, and delays in transit. These disruptions pushed the use of generative AI in the logistics industry to address these obstacles. Through route optimization, demand fluctuation predictions, and the discovery of other suppliers and routes, generative AI is being utilized to anticipate and lessen these interruptions. Geopolitical conflicts, however, may also present serious obstacles for the generative AI industry because of the scarcity of real-time consumer data needed to train these AI systems, which might affect the accuracy of AI models.

Route Optimization Leads Global Generative AI Logistics Market

The route optimization segment holds the largest share of Global Generative AI in Logistics Market. In the logistics industry, generative AI is frequently used to improve routes by analyzing historical data, current traffic conditions, and other variables. In order to cut down on delivery times and transportation expenses, the analysis is then utilized to create effective transportation strategies. The demand forecasting segment also covers substantial market share. Supply chain managers may use generative AI to automate ordering plans to keep inventory levels up to date and forecast future trends based on historical data.

North America Dominates Global Generative AI in Logistics Market

North America holds a major market share in Global Generative AI in Logistics Market. The adoption of generative AI in the logistics sector is directly fueled by the presence of industry giants in this field, such as Google, AWS, OpenAI, and IBM in the region. Logistics companies in the United States are employing modern technologies, such as generative AI, for numerous objectives, such as tracking customer behavior and historical sales data, optimizing production planning, and conducting risk anticipation. Such cases increase the logistics industry's operational resilience and productivity, which encourages this sector to incorporate generative AI into their operations.

Competitive Landscape

The major industry players of global Generative AI in Logistics market include Blue Yonder, C. H. Robinson, FedEx Corp., Google Cloud, IBM, Microsoft, PackageX, Salesforce, Deutsche Post AG, Schneider Electric, and A.P. Moller - Maersk. The presence of high number of companies intensify the market competition as they compete to gain a significant market share. These companies employ various strategies, including mergers and acquisitions, partnerships, joint ventures, license agreements, and new product launches to further enhance their market share.

The in-depth analysis of the report provides information about growth potential, upcoming trends, and Global Generative AI in Logistics Market. It also highlights the factors driving forecasts of total market size. The report promises to provide recent technology trends in Global Generative AI in Logistics Market and industry insights to help decision-makers make sound strategic decisions. Furthermore, the report also analyzes the growth drivers, challenges, and competitive dynamics of the market.

Product Code: BWC25017

1. Research Framework

1.1. Research Objective
1.2. Product Overview
1.3. Market Segmentation

2. Executive Summary

3. Global Generative AI in Logistics Market Insights

3.1. Industry Value Chain Analysis
3.2. DROC Analysis
- 3.2.1. Growth Drivers
  - 3.2.1.1. Rising Adoption of Automation and AI Technologies to Optimize Supply Chain Processes
  - 3.2.1.2. Growing Need for Enhanced Decision-Making Capabilities in Logistics Operations
  - 3.2.1.3. Increasing Use of Predictive Analytics for Demand Forecasting and Route Optimization
- 3.2.2. Restraints
  - 3.2.2.1. High Implementation Costs of Generative AI Solutions for Logistics Companies
  - 3.2.2.2. Limited AI Expertise and Skilled Workforce to Operate and Manage AI Technologies
- 3.2.3. Opportunities
  - 3.2.3.1. Integration of Generative AI with IoT, Blockchain, and Robotics to Enhance Supply Chain Efficiency
  - 3.2.3.2. Development of AI-driven Autonomous Vehicles and Drones for Logistics Operations
  - 3.2.3.3. Growing Adoption of Generative AI in Warehouse Management and Inventory Optimization
- 3.2.4. Challenges
  - 3.2.4.1. Managing Data Quality and Standardization Across Fragmented Supply Chain Networks.
  - 3.2.4.2. Data Privacy and Cybersecurity Concerns in Handling Sensitive Logistics Data
3.3. Technological Advancements/Recent Developments
3.4. Regulatory Framework
3.5. Porter's Five Forces Analysis
- 3.5.1. Bargaining Power of Suppliers
- 3.5.2. Bargaining Power of Buyers
- 3.5.3. Threat of New Entrants
- 3.5.4. Threat of Substitutes
- 3.5.5. Intensity of Rivalry

4. Global Generative AI in Logistics Market: Marketing Strategies

5. Global Generative AI in Logistics Market: Geographical Analysis

5.1. Global Generative AI in Logistics Market, Geographical Analysis, 2024
5.2. Global Generative AI in Logistics Market, Market Attractiveness Analysis, 2024-2031

6. Global Generative AI in Logistics Market Overview

6.1. Market Size & Forecast, 2019-2031
- 6.1.1. By Value (USD Billion)
6.2. Market Share & Forecast
- 6.2.1. By Type
  - 6.2.1.1. Variational Autoencoder (VAE)
  - 6.2.1.2. Generative Adversarial Networks (GANs)
  - 6.2.1.3. Recurrent Neural Networks (RNNs)
  - 6.2.1.4. Long Short-Term Memory (LSTM) networks
  - 6.2.1.5. Others
- 6.2.2. By Component
  - 6.2.2.1. Software
  - 6.2.2.2. Services
- 6.2.3. By Deployment Mode
  - 6.2.3.1. Cloud
  - 6.2.3.2. On-premises
- 6.2.4. By Application
  - 6.2.4.1. Route Optimization
  - 6.2.4.2. Demand Forecasting
  - 6.2.4.3. Warehouse & Inventory Management
  - 6.2.4.4. Supply Chain Automation
  - 6.2.4.5. Predictive Maintenance
  - 6.2.4.6. Risk Management
  - 6.2.4.7. Customized Logistics Solutions
  - 6.2.4.8. Others
- 6.2.5. By End User
  - 6.2.5.1. Road Transportation
  - 6.2.5.2. Railway Transportation
  - 6.2.5.3. Aviation
  - 6.2.5.4. Shipping & Ports
- 6.2.6. By Region
  - 6.2.6.1. North America
  - 6.2.6.2. Europe
  - 6.2.6.3. Asia Pacific (APAC)
  - 6.2.6.4. Latin America (LATAM)
  - 6.2.6.5. Middle East and Africa (MEA)

7. North America Generative AI in Logistics Market

7.1. Market Size & Forecast, 2019-2031
- 7.1.1. By Value (USD Billion)
7.2. Market Share & Forecast
- 7.2.1. By Type
- 7.2.2. By Component
- 7.2.3. By Deployment Mode
- 7.2.4. By Application
- 7.2.5. By End User
- 7.2.6. By Country
  - 7.2.6.1. United States
  - 7.2.6.1.1. By Type
  - 7.2.6.1.2. By Component
  - 7.2.6.1.3. By Deployment Mode
  - 7.2.6.1.4. By Application
  - 7.2.6.1.5. By End User
  - 7.2.6.2. Canada
  - 7.2.6.2.1. By Type
  - 7.2.6.2.2. By Component
  - 7.2.6.2.3. By Deployment Mode
  - 7.2.6.2.4. By Application
  - 7.2.6.2.5. By End User

8. Europe Generative AI in Logistics Market

8.1. Market Size & Forecast, 2019-2031
- 8.1.1. By Value (USD Billion)
8.2. Market Share & Forecast
- 8.2.1. By Type
- 8.2.2. By Component
- 8.2.3. By Deployment Mode
- 8.2.4. By Application
- 8.2.5. By End User
- 8.2.6. By Country
  - 8.2.6.1. Germany
  - 8.2.6.1.1. By Type
  - 8.2.6.1.2. By Component
  - 8.2.6.1.3. By Deployment Mode
  - 8.2.6.1.4. By Application
  - 8.2.6.1.5. By End User
  - 8.2.6.2. United Kingdom
  - 8.2.6.2.1. By Type
  - 8.2.6.2.2. By Component
  - 8.2.6.2.3. By Deployment Mode
  - 8.2.6.2.4. By Application
  - 8.2.6.2.5. By End User
  - 8.2.6.3. Italy
  - 8.2.6.3.1. By Type
  - 8.2.6.3.2. By Component
  - 8.2.6.3.3. By Deployment Mode
  - 8.2.6.3.4. By Application
  - 8.2.6.3.5. By End User
  - 8.2.6.4. France
  - 8.2.6.4.1. By Type
  - 8.2.6.4.2. By Component
  - 8.2.6.4.3. By Deployment Mode
  - 8.2.6.4.4. By Application
  - 8.2.6.4.5. By End User
  - 8.2.6.5. Spain
  - 8.2.6.5.1. By Type
  - 8.2.6.5.2. By Component
  - 8.2.6.5.3. By Deployment Mode
  - 8.2.6.5.4. By Application
  - 8.2.6.5.5. By End User
  - 8.2.6.6. Belgium
  - 8.2.6.6.1. By Type
  - 8.2.6.6.2. By Component
  - 8.2.6.6.3. By Deployment Mode
  - 8.2.6.6.4. By Application
  - 8.2.6.6.5. By End User
  - 8.2.6.7. Russia
  - 8.2.6.7.1. By Type
  - 8.2.6.7.2. By Component
  - 8.2.6.7.3. By Deployment Mode
  - 8.2.6.7.4. By Application
  - 8.2.6.7.5. By End User
  - 8.2.6.8. The Netherlands
  - 8.2.6.8.1. By Type
  - 8.2.6.8.2. By Component
  - 8.2.6.8.3. By Deployment Mode
  - 8.2.6.8.4. By Application
  - 8.2.6.8.5. By End User
  - 8.2.6.9. Rest of Europe
  - 8.2.6.9.1. By Type
  - 8.2.6.9.2. By Component
  - 8.2.6.9.3. By Deployment Mode
  - 8.2.6.9.4. By Application
  - 8.2.6.9.5. By End User

9. Asia Pacific Generative AI in Logistics Market

9.1. Market Size & Forecast, 2019-2031
- 9.1.1. By Value (USD Billion)
9.2. Market Share & Forecast
- 9.2.1. By Type
- 9.2.2. By Component
- 9.2.3. By Deployment Mode
- 9.2.4. By Application
- 9.2.5. By End User
- 9.2.6. By Country
  - 9.2.6.1. China
  - 9.2.6.1.1. By Type
  - 9.2.6.1.2. By Component
  - 9.2.6.1.3. By Deployment Mode
  - 9.2.6.1.4. By Application
  - 9.2.6.1.5. By End User
  - 9.2.6.2. India
  - 9.2.6.2.1. By Type
  - 9.2.6.2.2. By Component
  - 9.2.6.2.3. By Deployment Mode
  - 9.2.6.2.4. By Application
  - 9.2.6.2.5. By End User
  - 9.2.6.3. Japan
  - 9.2.6.3.1. By Type
  - 9.2.6.3.2. By Component
  - 9.2.6.3.3. By Deployment Mode
  - 9.2.6.3.4. By Application
  - 9.2.6.3.5. By End User
  - 9.2.6.4. South Korea
  - 9.2.6.4.1. By Type
  - 9.2.6.4.2. By Component
  - 9.2.6.4.3. By Deployment Mode
  - 9.2.6.4.4. By Application
  - 9.2.6.4.5. By End User
  - 9.2.6.5. Australia & New Zealand
  - 9.2.6.5.1. By Type
  - 9.2.6.5.2. By Component
  - 9.2.6.5.3. By Deployment Mode
  - 9.2.6.5.4. By Application
  - 9.2.6.5.5. By End User
  - 9.2.6.6. Indonesia
  - 9.2.6.6.1. By Type
  - 9.2.6.6.2. By Component
  - 9.2.6.6.3. By Deployment Mode
  - 9.2.6.6.4. By Application
  - 9.2.6.6.5. By End User
  - 9.2.6.7. Malaysia
  - 9.2.6.7.1. By Type
  - 9.2.6.7.2. By Component
  - 9.2.6.7.3. By Deployment Mode
  - 9.2.6.7.4. By Application
  - 9.2.6.7.5. By End User
  - 9.2.6.8. Singapore
  - 9.2.6.8.1. By Type
  - 9.2.6.8.2. By Component
  - 9.2.6.8.3. By Deployment Mode
  - 9.2.6.8.4. By Application
  - 9.2.6.8.5. By End User
  - 9.2.6.9. Vietnam
  - 9.2.6.9.1. By Type
  - 9.2.6.9.2. By Component
  - 9.2.6.9.3. By Deployment Mode
  - 9.2.6.9.4. By Application
  - 9.2.6.9.5. By End User
  - 9.2.6.10. Rest of APAC
  - 9.2.6.10.1. By Type
  - 9.2.6.10.2. By Component
  - 9.2.6.10.3. By Deployment Mode
  - 9.2.6.10.4. By Application
  - 9.2.6.10.5. By End User

10. Latin America Generative AI in Logistics Market

10.1. Market Size & Forecast, 2019-2031
- 10.1.1. By Value (USD Billion)
10.2. Market Share & Forecast
- 10.2.1. By Type
- 10.2.2. By Component
- 10.2.3. By Deployment Mode
- 10.2.4. By Application
- 10.2.5. By End User
- 10.2.6. By Country
  - 10.2.6.1. Brazil
  - 10.2.6.1.1. By Type
  - 10.2.6.1.2. By Component
  - 10.2.6.1.3. By Deployment Mode
  - 10.2.6.1.4. By Application
  - 10.2.6.1.5. By End User
  - 10.2.6.2. Mexico
  - 10.2.6.2.1. By Type
  - 10.2.6.2.2. By Component
  - 10.2.6.2.3. By Deployment Mode
  - 10.2.6.2.4. By Application
  - 10.2.6.2.5. By End User
  - 10.2.6.3. Argentina
  - 10.2.6.3.1. By Type
  - 10.2.6.3.2. By Component
  - 10.2.6.3.3. By Deployment Mode
  - 10.2.6.3.4. By Application
  - 10.2.6.3.5. By End User
  - 10.2.6.4. Peru
  - 10.2.6.4.1. By Type
  - 10.2.6.4.2. By Component
  - 10.2.6.4.3. By Deployment Mode
  - 10.2.6.4.4. By Application
  - 10.2.6.4.5. By End User
  - 10.2.6.5. Rest of LATAM
  - 10.2.6.5.1. By Type
  - 10.2.6.5.2. By Component
  - 10.2.6.5.3. By Deployment Mode
  - 10.2.6.5.4. By Application
  - 10.2.6.5.5. By End User

11. Middle East & Africa Generative AI in Logistics Market

11.1. Market Size & Forecast, 2019-2031
- 11.1.1. By Value (USD Billion)
11.2. Market Share & Forecast
- 11.2.1. By Type
- 11.2.2. By Component
- 11.2.3. By Deployment Mode
- 11.2.4. By Application
- 11.2.5. By End User
- 11.2.6. By Country
  - 11.2.6.1. Saudi Arabia
  - 11.2.6.1.1. By Type
  - 11.2.6.1.2. By Component
  - 11.2.6.1.3. By Deployment Mode
  - 11.2.6.1.4. By Application
  - 11.2.6.1.5. By End User
  - 11.2.6.2. UAE
  - 11.2.6.2.1. By Type
  - 11.2.6.2.2. By Component
  - 11.2.6.2.3. By Deployment Mode
  - 11.2.6.2.4. By Application
  - 11.2.6.2.5. By End User
  - 11.2.6.3. Qatar
  - 11.2.6.3.1. By Type
  - 11.2.6.3.2. By Component
  - 11.2.6.3.3. By Deployment Mode
  - 11.2.6.3.4. By Application
  - 11.2.6.3.5. By End User
  - 11.2.6.4. Kuwait
  - 11.2.6.4.1. By Type
  - 11.2.6.4.2. By Component
  - 11.2.6.4.3. By Deployment Mode
  - 11.2.6.4.4. By Application
  - 11.2.6.4.5. By End User
  - 11.2.6.5. South Africa
  - 11.2.6.5.1. By Type
  - 11.2.6.5.2. By Component
  - 11.2.6.5.3. By Deployment Mode
  - 11.2.6.5.4. By Application
  - 11.2.6.5.5. By End User
  - 11.2.6.6. Nigeria
  - 11.2.6.6.1. By Type
  - 11.2.6.6.2. By Component
  - 11.2.6.6.3. By Deployment Mode
  - 11.2.6.6.4. By Application
  - 11.2.6.6.5. By End User
  - 11.2.6.7. Algeria
  - 11.2.6.7.1. By Type
  - 11.2.6.7.2. By Component
  - 11.2.6.7.3. By Deployment Mode
  - 11.2.6.7.4. By Application
  - 11.2.6.7.5. By End User
  - 11.2.6.8. Rest of MEA
  - 11.2.6.8.1. By Type
  - 11.2.6.8.2. By Component
  - 11.2.6.8.3. By Deployment Mode
  - 11.2.6.8.4. By Application
  - 11.2.6.8.5. By End User

12. Competitive Landscape

12.1. List of Key Players and Their Offerings
12.2. Global Generative AI in Logistics Company Market Share Analysis, 2024
12.3. Competitive Benchmarking, By Operating Parameters
12.4. Key Strategic Developments (Mergers, Acquisitions, Partnerships)

13. Impact of Escalating Geopolitical Tensions on Global Generative AI in Logistics Market

14. Company Profile (Company Overview, Financial Matrix, Competitive Landscape, Key Personnel, Key Competitors, Contact Address, Strategic Outlook, SWOT Analysis)

14.1. Blue Yonder
14.2. C. H. Robinson
14.3. FedEx Corp
14.4. Google Cloud
14.5. IBM
14.6. Microsoft
14.7. PackageX
14.8. Salesforce
14.9. Deutsche Post AG
14.10. Schneider Electric
14.11. A.P. Moller - Maersk
14.12. Other Prominent Players

15. Key Strategic Recommendations

16. Research Methodology

16.1. Qualitative Research
- 16.1.1. Primary & Secondary Research
16.2. Quantitative Research
16.3. Market Breakdown & Data Triangulation
- 16.3.1. Secondary Research
- 16.3.2. Primary Research
16.4. Breakdown of Primary Research Respondents, By Region
16.5. Assumptions & Limitations

*Financial information of non-listed companies can be provided as per availability.

**The segmentation and the companies are subject to modifications based on in-depth secondary research for the final deliverable