Market Research

Generative AI in Logistics Market, Opportunity, Growth Drivers, Industry Trend Analysis and Forecast, 2024-2032

Best Price Guarantee Length Publisher Published Date SKU
from $4,850 270 Pages Global Market Insights July, 2024 GMI19113813
Best Price Guarantee
Price from $4,850
Length 270 Pages
Publisher Global Market Insights
Published Date July, 2024
SKU GMI19113813

Generative AI in Logistics Market, Opportunity, Growth Drivers, Industry Trend Analysis and Forecast, 2024-2032


Generative AI in Logistics Market size will depict over 33.2% CAGR from 2024 to 2032, majorly propelled by the rise in sustainability initiatives.

Of late, AI is largely leveraged to create more sustainable logistics practices, such as optimizing routes to reduce fuel consumption and emissions. The integration of AI with robotics is also offering enhanced productivity and reduced manual labor in automating warehouse operations, including sorting, packing, and shipping. The growing investments in AI technologies along with the strategic partnerships between logistics companies and AI firms to develop innovative solutions will boost the market growth. For example, in May 2024, the U.K. Government released £1.8m in funding to assist SMEs in using AI to decarbonize freight.

The generative AI in logistics industry is segmented into type, component, deployment model, application, end user, and region.

By deployment model, the industry value from the on-premises segment may witness lucrative growth through 2032. On-premise generative AI solutions offer greater customization to meet specific organizational needs and integration with existing systems. These solutions further provide greater control over sensitive data for ensuring that proprietary information and customer data remain secure and compliant with stringent data protection regulations.

With respect to application, the generative AI in logistics market size from the risk management segment will record expansion from 2024 to 2032. Generative AI models analyze historical data and current conditions to predict potential risks, such as supply chain disruptions, natural disasters, and market fluctuations. The rising adoption for identifying and mitigating potential cyber threats to logistics systems and data will also favor segment growth.

Europe generative AI in logistics industry share will expand through 2032 led by the rising need to optimize supply chain operations by predicting the demand while managing inventory and routing. Generative AI is helping logistics companies in the region optimize their operations to limit environmental impacts, such as minimizing empty runs. This aligns with the European Union’s stringent environmental regulations and sustainability targets for contributing to greener logistics practices across the continent.


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 estimates
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 Insurance providers
3.2.2 Distribution channels
3.2.3 End users
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 Supply chain and route planning optimization
3.8.1.2 Increased demand for warehouse management
3.8.1.3 Accuracy in demand forecasting
3.8.1.4 Achieving cost efficiency
3.9 Industry pitfalls and challenges
3.9.1.1 Data quality and availability
3.9.1.2 Complexity in integration
3.10 Growth potential analysis
3.11 Porter’s analysis
3.12 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 Type, 2021-2032 ($Bn)
5.1 Key trends
5.2 Variational Autoencoder (VAE)
5.3 Generative Adversarial Networks (GANs)
5.4 Recurrent Neural Networks (RNNs)
5.5 Long Short-Term Memory (LSTM) networks
5.6 Others
Chapter 6 Market Estimate and Forecast, By Component, 2021-2032 ($Bn)
6.1 Key trends
6.2 Software
6.3 Services
Chapter 7 Market Estimates and Forecast, By Deployment Mode, 2021-2032 ($Bn)
7.1 Key trends
7.2 Cloud
7.3 On-premises
Chapter 8 Market Estimates and Forecast, By Application, 2021-2032 ($Bn)
8.1 Key trends
8.2 Route optimization
8.2.1 Variational Autoencoder (VAE)
8.2.2 Generative Adversarial Networks (GANs)
8.2.3 Recurrent Neural Networks (RNNs)
8.2.4 Long Short-Term Memory (LSTM) networks
8.2.5 Others
8.3 Demand forecasting
8.3.1 Variational Autoencoder (VAE)
8.3.2 Generative Adversarial Networks (GANs)
8.3.3 Recurrent Neural Networks (RNNs)
8.3.4 Long Short-Term Memory (LSTM) networks
8.3.5 Others
8.4 Warehouse and inventory management
8.4.1 Variational Autoencoder (VAE)
8.4.2 Generative Adversarial Networks (GANs)
8.4.3 Recurrent Neural Networks (RNNs)
8.4.4 Long Short-Term Memory (LSTM) networks
8.4.5 Others
8.5 Supply chain automation
8.5.1 Variational Autoencoder (VAE)
8.5.2 Generative Adversarial Networks (GANs)
8.5.3 Recurrent Neural Networks (RNNs)
8.5.4 Long Short-Term Memory (LSTM) networks
8.5.5 Others
8.6 Predictive maintenance
8.6.1 Variational Autoencoder (VAE)
8.6.2 Generative Adversarial Networks (GANs)
8.6.3 Recurrent Neural Networks (RNNs)
8.6.4 Long Short-Term Memory (LSTM) networks
8.6.5 Others
8.7 Risk management
8.7.1 Variational Autoencoder (VAE)
8.7.2 Generative Adversarial Networks (GANs)
8.7.3 Recurrent Neural Networks (RNNs)
8.7.4 Long Short-Term Memory (LSTM) networks
8.7.5 Others
8.8 Customized logistics solutions
8.8.1 Variational Autoencoder (VAE)
8.8.2 Generative Adversarial Networks (GANs)
8.8.3 Recurrent Neural Networks (RNNs)
8.8.4 Long Short-Term Memory (LSTM) networks
8.8.5 Others
8.9 Others
8.9.1 Variational Autoencoder (VAE)
8.9.2 Generative Adversarial Networks (GANs)
8.9.3 Recurrent Neural Networks (RNNs)
8.9.4 Long Short-Term Memory (LSTM) networks
8.9.5 Others
Chapter 9 Market Estimates and Forecast, By End User, 2021-2032 ($Bn)
9.1 Key trends
9.2 Road Transportation
9.3 Railway Transport
9.4 Aviation
9.5 Shipping, and Ports
Chapter 10 Market Estimates and Forecast, By Region, 2021-2032 ($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 Russia
10.3.7 Nordics
10.3.8 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 South Africa
10.6.2 Saudi Arabia
10.6.3 UAE
10.6.4 Rest of MEA
Chapter 11 Company Profiles
11.1 Blue Yonder
11.2 C.H. Robinson
11.3 DHL
11.4 FedEx Corp
11.5 Google Cloud
11.6 IBM
11.7 LeewayHertz
11.8 Microsoft
11.9 Nexocode
11.10 PackageX
11.11 Salesforce
11.12 SAP SE
11.13 Schneider Electric
11.14 UPS (United Parcel Services)
11.15 XenonStack
11.16 XPO Logistics

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