Generative AI in Logistics Market size was valued at USD 816.15 million in 2024 and is expected to reach USD 13621.30 million by 2032, growing at a CAGR of 36.93% from 2025-2032.
Generative AI in Logistics growth is influenced by soaring demand for automating operations, optimizing routes, making decisions in real time, and predictive modeling. Better efficiency of supply chain, lowered operation cost, and better customer satisfaction also catalyze the push to adoption. Its integration with IoT and big data also causes accelerated market growth in every sector.
C.H. Robinson uses generative AI to automate 10,000+ daily email-based logistics tasks, including quotes, load tenders, and appointments, while piloting AI for in-transit visibility, significantly reducing response times and enhancing efficiency across 26,000+ shipment locations.
U.S. Generative AI in Logistics Market size was valued at USD 349.23 million in 2024 and is expected to reach USD 4273.06 million by 2032, growing at a CAGR of 36.76% from 2025-2032.
The U.S. Generative AI in logistics market is expanding with heightened demand for automation, route optimization, and data-driven decision-making. Developments in AI, IoT integration, and predictive analytics are increasing supply chain efficiency, lowering costs, and enhancing customer satisfaction.
According to EY, 30% increase in workforce productivity has been achieved by a major U.S. logistics company using GenAI for warehouse route optimization, and 40% of supply chain organizations are investing in GenAI, primarily for knowledge management applications.
Drivers
Automation reduces manual workload and increases operational efficiency across routing, warehousing, and transportation logistics processes significantly
Generative AI streamlines logistics by automating processes like route optimization, inventory handling, and shipping scheduling, saving labor costs and human mistakes. AI models are trained on historic data, adjust dynamically, and provide real-time optimized choices. For example, AI predicts delayed deliveries and provides alternative routes due to traffic and weather conditions. This automation lessens disruptions, improves the dependability of the supply chain, and allows for scalability. As logistics processes get more and more complicated, AI can deal with complex variables without getting tired, resulting in better productivity, customer satisfaction, and cost-effectiveness in the long term, placing logistics providers ahead in the digital economy.
According to EY, 62% of organizations have adopted AI for sustainability tracking and measurement, while 65% of vendors prefer negotiating with GenAI bots over human representatives due to efficiency and structure. Furthermore, a 37% to 39% expected productivity increase in customer interactions and 34% to 36% in production and assembly is anticipated through GenAI.
Restraints
High implementation costs and infrastructure needs delay adoption of AI solutions by small and mid-sized logistics companies globally
Implementing generative AI in logistics is a high-cost affair in terms of hardware, software, and trained resources. Small and mid-size firms lack the financial resources to bear these costs, including IT system updates, cloud computing, cybersecurity, and training. These expenses deter AI deployment, particularly in low-margin businesses, with gains only accruing to well-funded organizations. Economical, scalable solutions are not available, preventing mass adoption, and a digital divide exists that prevents innovation across the logistics sector.
Custom Generative AI development ranges from USD 500,000 to USD 5 million based on project complexity. Upfront costs of a dedicated AI team can amount to as much as USD 320,000 per annum. On-premise infrastructure setup for Generative AI generally involves a one-time setup cost of about USD 21,000.
Opportunities
Growing e-commerce volumes drive demand for AI-enabled optimization of last-mile delivery and personalized customer logistics experiences
The growth of cross-border e-commerce has increased the pressure on logistics infrastructure to deliver accuracy and speed. Best last mile delivery solution with AI It leverages the generative AI prediction for traffic, weather, and customer trends to create the most efficient routes possible. Aside from that, it allows dynamic pricing, parcel tracking and ETA notifications, which contribute to higher customer satisfaction. In addition, AI generates delivery recommendations based on previously analyzed data. In addition, AI creates personalized delivery recommendations based on previous behaviour. With the increasing competition, adopting AI helps organizations to provide personalized delivery, thus gaining an essential market share making the generative AI an integral part of e-commerce logistics solutions.
In 2024, Blue Yonder said 91% of worldwide organizations reported that generative AI was effective in streamlining supply chain processes and decision-making. The company also makes 10 billion predictions every day, demonstrating the scope of its AI integration.
Challenges
Complexity of integrating generative AI into legacy logistics systems delays transformation and impacts operational consistency
The majority of logistics firms continue to operate outdated IT infrastructures that hinder advanced integration of AI due to the fact that they incur compatibility issues, delays in data transfer, and incompatibility with modularity. Retrofitting AI means having siloed processes, data silos, and downtime. Upgrade involves not only technical improvements but also cultural and organizational adjustments, for which most companies are not ready. Without a clear digital roadmap, AI adoption projects stall, replicating efforts and costs. To overcome such challenges, money needs to be spent on change management, training, and infrastructure, which is a hindrance to digital transformation in traditional logistics businesses.
Approximately 59% of teams struggle with extracting data from legacy systems due to format mismatches, and 64% face challenges moving data from mainframes. Additionally, 31% of organizations encounter system limitations with outdated hardware.
Approximately 74% are not ready for AI integration, and 30% of logistics firms identify data privacy and security as major challenges to AI adoption. These are the factors that prevent the smooth implementation and scaling of AI technologies in logistics.
By Type
Variational Autoencoders (VAEs) commanded the highest revenue of approximately 30% in the Generative AI in Logistics Market in 2024 owing largely to their efficiency and stability of handling high-dimensional logistics data. Their capacity for producing realistic synthetic data to serve demand forecasting, route optimization, and anomaly detection purposes rendered VAEs appealing to early innovators. Moreover, VAEs easily embed into legacy platforms, guaranteeing wider adoption to existing logistics setups.
Generative Adversarial Networks (GANs) are expected to register the fastest CAGR of about 39.62% during the period between 2025 and 2032, fueled by their better performance in mimicking intricate logistics situations and creating realistic data to train autonomous systems. With supply chains embracing AI in making real-time decisions, GANs' ability to improve simulation environments, warehouse layout optimization, and predictability helps them become critical tools in future-proof logistics solutions.
By End User
The road transportation segment led the Generative AI in Logistics Market share in 2024, generating approximately 34%, due to its enormous size and operational complexity. Generative AI technologies were adopted at a very fast pace in this segment for routing optimization, predictive maintenance, and fuel optimization. Due to its direct consumer influence and vast network, road transport emerged as the most tangible and fertile terrain for AI-powered performance and cost optimization.
The shipping and ports segment is forecasted to expand at the highest CAGR of approximately 39.94% during the period from 2025 to 2032 driven by rising automation and digitalization in maritime logistics. Increased need for real-time vessel tracking, port congestion control, and predictive maintenance has created a necessity for generative AI. Port operators and shipping lines are scrambling to invest in AI as global trade picks up and mounting sustainability regulations mean environment compliance becomes a priority.
By Deployment Mode
The cloud segment dominated the Generative AI in Logistics Market with a revenue share of 68% in 2024 and is poised to progress at the highest CAGR of 37.70% between 2025 and 2032. This is due to the scalability, cost-effectiveness, and rapidity of deployment of cloud infrastructure that allow logistics companies to deploy and grow AI solutions without significant capital investment. With the logistics sector increasingly reliant on real-time data processing, predictive analytics, and automation, cloud platforms enable seamless updates of AI models, geographically hassle-free integration, and rapid responses to dynamic supply chain demands.
Regional Analysis
North America led the Generative AI in Logistics Market in 2024 with the largest revenue share of around 44%. This leadership was due to the strong establishment of key technology giants, early adoption of AI-based solutions, and extensive logistics infrastructure. The region is also supported by huge investments in AI R&D and a high-tech customer base, which fast-tracks the adoption of generative AI in multiple logistics and supply chain activities.
In 2024, the US led the Generative AI in Logistics Market due to world-class infrastructure, early adoption of AI, high tech presence, and extensive automation investment.
Asia Pacific is expected to advance at the highest CAGR of approximately 39.18% during 2025-2032 based on swift digitalization, growing e-commerce market, and rising investments in AI technologies in emerging economies such as China and India. The increasing demand for effective logistics in the region, coupled with government incentives for smart infrastructure and AI development, is driving the demand for generative AI solutions that optimize routes, improve demand forecasting, and automate supply chains.
China is dominating the Generative AI in Logistics Market within Asia Pacific due to its massive e-commerce ecosystem, strong government support for AI, and rapid digital infrastructure development.
Europe has a strong presence in the Generative AI in Logistics Market due to robust logistics networks, supportive AI regulations, widespread digital transformation, and significant investments aimed at boosting supply chain efficiency and sustainability.
Germany is dominating the Generative AI in Logistics Market in Europe due to its advanced industrial base, strong logistics infrastructure, and early adoption of AI technologies.
Middle East & Africa and Latin America have a growing presence in the Generative AI in Logistics Market, driven by increasing investments in smart infrastructure, rising e-commerce activities, and efforts to modernize supply chains through AI-powered automation and predictive technologies.
Generative AI in Logistics Market Companies include Deutsche Post AG, UPS (United Parcel Services), Schneider Electric, C.H. Robinson, XPO Logistics, FedEx Corp, A.P. Moller - Maersk AS, Blue Yonder, Google Cloud, International Business Machines (IBM), Microsoft, PackageX, Salesforce.
Recent Developments:
September 2024, Salesforce advanced its AI capabilities with agentic AI in Einstein GPT, automating complex customer service tasks such as processing returns and refunds, and introduced an AI benchmark to guide businesses in selecting efficient large language models
April 2024, Google expanded its in-house chip efforts with the Axion CPU, aiming to improve performance and energy efficiency in AI tasks, reducing reliance on external vendors and enhancing its cloud infrastructure.
July 2023, Microsoft researchers developed OptiGuide, a framework leveraging large language models to interpret supply chain optimization outcomes, enabling stakeholders to understand and trust AI-driven decisions without compromising proprietary data.
| Report Attributes | Details |
|---|---|
| Market Size in 2024 | USD 816.15 Million |
| Market Size by 2032 | USD 13621.30 Million |
| CAGR | CAGR of 36.93% From 2025 to 2032 |
| Base Year | 2024 |
| Forecast Period | 2025-2032 |
| Historical Data | 2021-2023 |
| Report Scope & Coverage | Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
| Key Segments | • By Type (Variational Autoencoder (VAE), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Others) • By Deployment Mode (Cloud Based, On-premise) • By End User (Road Transportation, Railway Transportation, Aviation, Shipping and Ports) |
| Regional Analysis/Coverage | North America (US, Canada, Mexico), Europe (Germany, France, UK, Italy, Spain, Poland, Turkey, Rest of Europe), Asia Pacific (China, India, Japan, South Korea, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (UAE, Saudi Arabia, Qatar, South Africa, Rest of Middle East & Africa), Latin America (Brazil, Argentina, Rest of Latin America) |
| Company Profiles | Deutsche Post AG, UPS (United Parcel Services), Schneider Electric, C.H. Robinson, XPO Logistics, FedEx Corp, A.P. Moller - Maersk AS, Blue Yonder, Google Cloud, International Business Machines (IBM), Microsoft, PackageX, Salesforce |
Ans: Generative AI in Logistics Market was valued at USD 816.15 million in 2024 and is expected to reach USD 13621.30 million by 2032, growing at a CAGR of 36.93% from 2025-2032.
Ans: Key drivers include automation demand, route optimization, predictive analytics, and real-time decision-making across logistics operations.
Ans: The U.S. market was USD 349.23 million in 2024 and will likely reach USD 4,273.06 million by 2032.
Ans: Road transportation held 34% market share due to large-scale operations and early adoption of AI for routing and fuel optimization.
Ans: Asia Pacific will grow fastest at a 39.18% CAGR, fueled by e-commerce expansion, AI investment, and digital transformation.
Table of Contents:
1. Introduction
1.1 Market Definition
1.2 Scope (Inclusion and Exclusions)
1.3 Research Assumptions
2. Executive Summary
2.1 Market Overview
2.2 Regional Synopsis
2.3 Competitive Summary
3. Research Methodology
3.1 Top-Down Approach
3.2 Bottom-up Approach
3.3. Data Validation
3.4 Primary Interviews
4. Market Dynamics Impact Analysis
4.1 Market Driving Factors Analysis
4.1.1 Drivers
4.1.2 Restraints
4.1.3 Opportunities
4.1.4 Challenges
4.2 PESTLE Analysis
4.3 Porter’s Five Forces Model
5. Statistical Insights and Trends Reporting
5.1 Time-to-Implementation Statistics
5.2 Predictive Accuracy Improvements
5.3 Supply Chain Risk Mitigation
5.4 AI Training Data Volumes
5.5 Carbon Footprint Reduction
6. Competitive Landscape
6.1 List of Major Companies, By Region
6.2 Market Share Analysis, By Region
6.3 Product Benchmarking
6.3.1 Product specifications and features
6.3.2 Pricing
6.4 Strategic Initiatives
6.4.1 Marketing and promotional activities
6.4.2 Distribution and supply chain strategies
6.4.3 Expansion plans and new product launches
6.4.4 Strategic partnerships and collaborations
6.5 Technological Advancements
6.6 Market Positioning and Branding
7. Generative AI in Logistics Market Segmentation, By Type
7.1 Chapter Overview
7.2 Variational Autoencoder (VAE)
7.2.1 Variational Autoencoder (VAE) Market Trends Analysis (2020-2032)
7.2.2 Variational Autoencoder (VAE) Market Size Estimates and Forecasts to 2032 (USD Billion)
7.3 Generative Adversarial Networks (GANs)
7.3.1 Generative Adversarial Networks (GANs) Market Trends Analysis (2020-2032)
7.3.2 Generative Adversarial Networks (GANs) Market Size Estimates and Forecasts to 2032 (USD Billion)
7.4 Recurrent Neural Networks (RNNs)
7.4.1 Recurrent Neural Networks (RNNs) Market Trends Analysis (2020-2032)
7.4.2 Recurrent Neural Networks (RNNs) Market Size Estimates and Forecasts to 2032 (USD Billion)
7.5 Long Short-Term Memory (LSTM) networks
7.5.1 Long Short-Term Memory (LSTM) networks Market Trends Analysis (2020-2032)
7.5.2 Long Short-Term Memory (LSTM) networks Market Size Estimates and Forecasts to 2032 (USD Billion)
7.6 Others
7.6.1 Others Market Trends Analysis (2020-2032)
7.6.2 Others Market Size Estimates and Forecasts to 2032 (USD Billion)
8. Generative AI in Logistics Market Segmentation, By Deployment Mode
8.1 Chapter Overview
8.2 Cloud Based
8.2.1 Cloud Based Market Trends Analysis (2020-2032)
8.2.2 Cloud Based Market Size Estimates and Forecasts to 2032 (USD Billion)
8.3 On-premise
8.3.1 On-premise Market Trends Analysis (2020-2032)
8.3.2 On-premise Market Size Estimates and Forecasts to 2032 (USD Billion)
9. Generative AI in Logistics Market Segmentation, By End User
9.1 Chapter Overview
9.2 Road transportation
9.2.1 Road transportation Market Trends Analysis (2020-2032)
9.2.2 Road transportation Market Size Estimates and Forecasts to 2032 (USD Billion)
9.3 Railway transportation
9.3.1 Railway transportation Market Trends Analysis (2020-2032)
9.3.2 Railway transportation Market Size Estimates and Forecasts to 2032 (USD Billion)
9.4 Aviation
9.4.1 Aviation Market Trends Analysis (2020-2032)
9.4.2 Aviation Market Size Estimates and Forecasts to 2032 (USD Billion)
9.5 Shipping, and ports
9.5.1 Shipping, and ports Market Trends Analysis (2020-2032)
9.5.2 Shipping, and ports Market Size Estimates and Forecasts to 2032 (USD Billion)
10. Regional Analysis
10.1 Chapter Overview
10.2 North America
10.2.1 Trends Analysis
10.2.2 North America Generative AI in Logistics Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
10.2.3 North America Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.2.4 North America Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.2.5 North America Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.2.6 USA
10.2.6.1 USA Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.2.6.2 USA Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.2.6.3 USA Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.2.7 Canada
10.2.7.1 Canada Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.2.7.2 Canada Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.2.7.3 Canada Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.2.8 Mexico
10.2.8.1 Mexico Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.2.8.2 Mexico Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.2.8.3 Mexico Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.3 Europe
10.3.1 Trends Analysis
10.3.2 Eastern Europe Generative AI in Logistics Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
10.3.3 Eastern Europe Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.3.4 Eastern Europe Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.3.5 Eastern Europe Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.3.6 Poland
10.3.6.1 Poland Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.3.6.2 Poland Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.3.6.3 Poland Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.3.7 Turkey
10.3.7.1 Turkey Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.3.7.2 Turkey Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.3.7.3 Turkey Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.3.8 Germany
10.3.8.1 Germany Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.3.8.2 Germany Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.3.8.3 Germany Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.3.9 France
10.3.9.1 France Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.3.9.2 France Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.3.9.3 France Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.3.10 UK
10.3.10.1 UK Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.3.10.2 UK Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.3.10.3 UK Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.3.11 Italy
10.3.11.1 Italy Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.3.11.2 Italy Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.3.11.3 Italy Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.3.12 Spain
10.3.12.1 Spain Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.3.12.2 Spain Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.3.12.3 Spain Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.3.13 Rest of Europe
10.3.13.1 Rest of Europe Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.3.13.2 Rest of Europe Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.3.13.3 Rest of Europe Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.4 Asia Pacific
10.4.1 Trends Analysis
10.4.2 Asia Pacific Generative AI in Logistics Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
10.4.3 Asia Pacific Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.4.4 Asia Pacific Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.4.5 Asia Pacific Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.4.6 China
10.4.6.1 China Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.4.6.2 China Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.4.6.3 China Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.4.7 India
10.4.7.1 India Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.4.7.2 India Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.4.7.3 India Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.4.8 Japan
10.4.8.1 Japan Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.4.8.2 Japan Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.4.8.3 Japan Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.4.9 South Korea
10.4.9.1 South Korea Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.4.9.2 South Korea Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.4.9.3 South Korea Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.4.10 Singapore
10.4.10.1 Singapore Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.4.10.2 Singapore Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.4.10.3 Singapore Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.4.11 Australia
10.4.11.1 Australia Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.4.11.2 Australia Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.4.11.3 Australia Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.4.12 Rest of Asia Pacific
10.4.12.1 Rest of Asia Pacific Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.4.12.2 Rest of Asia Pacific Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.4.12.3 Rest of Asia Pacific Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.5 Middle East and Africa
10.5.1 Trends Analysis
10.5.2 Middle East Generative AI in Logistics Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
10.5.3 Middle East Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.5.4 Middle East Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.5.5 Middle East Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.5.6 UAE
10.5.6.1 UAE Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.5.6.2 UAE Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.5.6.3 UAE Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.5.7 Saudi Arabia
10.5.7.1 Saudi Arabia Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.5.7.2 Saudi Arabia Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.5.7.3 Saudi Arabia Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.5.8 Qatar
10.5.8.1 Qatar Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.5.8.2 Qatar Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.5.8.3 Qatar Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.5.9 South Africa
10.5.9.1 South Africa Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.5.9.2 South Africa Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.5.9.3 South Africa Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.5.10 Rest of Africa
10.5.10.1 Rest of Africa Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.5.10.2 Rest of Africa Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.5.10.3 Rest of Africa Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.6 Latin America
10.6.1 Trends Analysis
10.6.2 Latin America Generative AI in Logistics Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
10.6.3 Latin America Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.6.4 Latin America Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.6.5 Latin America Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.6.6 Brazil
10.6.6.1 Brazil Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.6.6.2 Brazil Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.6.6.3 Brazil Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.6.7 Argentina
10.6.7.1 Argentina Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.6.7.2 Argentina Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.6.7.3 Argentina Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
10.6.8 Rest of Latin America
10.6.8.1 Rest of Latin America Generative AI in Logistics Market Estimates and Forecasts, By Type (2020-2032) (USD Billion)
10.6.8.2 Rest of Latin America Generative AI in Logistics Market Estimates and Forecasts, By Deployment Mode (2020-2032) (USD Billion)
10.6.8.3 Rest of Latin America Generative AI in Logistics Market Estimates and Forecasts, By End User (2020-2032) (USD Billion)
11. Company Profiles
11.1 Deutsche Post AG
11.1.1 Company Overview
11.1.2 Financial
11.1.3 Products/ Services Offered
11.1.4 SWOT Analysis
11.2 UPS (United Parcel Services)
11.2.1 Company Overview
11.2.2 Financial
11.2.3 Products/ Services Offered
11.2.4 SWOT Analysis
11.3 Schneider Electric
11.3.1 Company Overview
11.3.2 Financial
11.3.3 Products/ Services Offered
11.3.4 SWOT Analysis
11.4 C.H. Robinson
11.4.1 Company Overview
11.4.2 Financial
11.4.3 Products/ Services Offered
11.4.4 SWOT Analysis
11.5 XPO Logistics
11.5.1 Company Overview
11.5.2 Financial
11.5.3 Products/ Services Offered
11.5.4 SWOT Analysis
11.6 FedEx Corp
11.6.1 Company Overview
11.6.2 Financial
11.6.3 Products/ Services Offered
11.6.4 SWOT Analysis
11.7 A.P. Moller - Maersk AS
11.7.1 Company Overview
11.7.2 Financial
11.7.3 Products/ Services Offered
11.7.4 SWOT Analysis
11.8 Blue Yonder
11.8.1 Company Overview
11.8.2 Financial
11.8.3 Products/ Services Offered
11.8.4 SWOT Analysis
11.9 Google Cloud
11.9.1 Company Overview
11.9.2 Financial
11.9.3 Products/ Services Offered
11.9.4 SWOT Analysis
11.10 International Business Machines (IBM)
11.10.1 Company Overview
11.10.2 Financial
11.10.3 Products/ Services Offered
11.10.4 SWOT Analysis
12. Use Cases and Best Practices
13. Conclusion
An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.
Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.
The 5 steps process:
Step 1: Secondary Research:
Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.
Step 2: Primary Research
When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data. This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.
We at SNS Insider have divided Primary Research into 2 parts.
Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.
This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.
Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.
Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.
Step 3: Data Bank Validation
Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.
Step 4: QA/QC Process
After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.
Step 5: Final QC/QA Process:
This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.
Key Segments:
By Type
Variational Autoencoder (VAE)
Generative Adversarial Networks (GANs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory (LSTM) networks
Others
By Deployment Mode
Cloud Based
On-premise
By End User
Road transportation
Railway transportation
Aviation
Shipping, and ports
Request for Segment Customization as per your Business Requirement: Segment Customization Request
Regional Coverage:
North America
US
Canada
Mexico
Europe
Germany
France
UK
Italy
Spain
Poland
Turkey
Rest of Europe
Asia Pacific
China
India
Japan
South Korea
Singapore
Australia
Rest of Asia Pacific
Middle East & Africa
UAE
Saudi Arabia
Qatar
South Africa
Rest of Middle East & Africa
Latin America
Brazil
Argentina
Rest of Latin America
Request for Country Level Research Report: Country Level Customization Request
Available Customization
With the given market data, SNS Insider offers customization as per the company’s specific needs. The following customization options are available for the report:
Detailed Volume Analysis
Criss-Cross segment analysis (e.g. Product X Application)
Competitive Product Benchmarking
Geographic Analysis
Additional countries in any of the regions
Customized Data Representation
Detailed analysis and profiling of additional market players
