Global Artificial Intelligence in Transportation and Logistics Market Size, Share & Trends Analysis Report, By Components (Software, Hardware, Services), By Application (Route optimization and scheduling, Predictive maintenance, Autonomous vehicles, Warehouse automation, inventory management), By Region (North America, Europe, APAC, and Others), and Segment Forecasts, 2023 – 2030
  • Published Date: Nov, 2023
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  • Pages: 200
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The AI in Transportation and Logistics Market, often referred to as the Artificial Intelligence (AI) in Transportation and Logistics Market, encompasses the application of artificial intelligence technologies within the transportation and logistics sector. This market focuses on the development, integration, and utilization of AI-powered solutions and systems to enhance various aspects of transportation, including road, rail, air, and maritime logistics, as well as warehouse management and supply chain operations. AI in Transportation and Logistics involves the use of advanced algorithms, machine learning, predictive analytics, and automation to optimize processes and improve efficiency across the entire logistics chain.

Key applications within this market include route optimization, predictive maintenance, autonomous vehicles, real-time tracking, demand forecasting, inventory management, and last-mile delivery solutions, among others. These AI-driven applications are designed to streamline operations, reduce costs, enhance safety, and provide a superior level of customer service. The market serves a wide range of stakeholders, including logistics and transportation companies, manufacturers, e-commerce platforms, government agencies, and consumers. AI technologies are leveraged to address various challenges in the transportation and logistics sector, such as route planning, congestion management, fuel efficiency, cargo security, and compliance with safety regulations.

Some of the benefits of designing an Artificial Intelligence in Transportation and Logistics Market include:

  • Streamlined Efficiency: AI streamlines route planning, load optimization, and resource allocation, resulting in reduced delivery times and lower operational costs. This streamlined efficiency fosters swifter and more cost-effective transportation and logistics operations.
  • Elevated Precision: AI-driven systems offer meticulous data analysis, minimizing errors in tasks like demand forecasting, inventory management, and order processing. This precision diminishes the risk of expensive errors and elevates the overall quality of service.
  • Economical Operations: Through improved route optimization, reduced fuel consumption, and the efficient allocation of resources, AI contributes to cost savings for transportation and operational activities. These savings can have a substantial impact on profit margins.
  • Real-Time Decision Support: AI rapidly processes copious amounts of data in real-time, equipping businesses with the tools to make informed, data-driven decisions promptly. This capability is particularly invaluable for adapting to dynamic conditions and effectively addressing unforeseen challenges.
  • Proactive Maintenance: AI anticipates equipment failures, facilitating proactive maintenance strategies. This approach minimizes downtime, trims maintenance expenses, and ensures the optimal condition of vehicles and equipment.
  • Enriched Customer Experience: AI-driven systems offer personalized services, real-time updates, and enhanced communication channels, culminating in an enriched customer experience that fosters satisfaction and cultivates customer loyalty.
  • Streamlined Supply Chain Operations: AI empowers businesses to streamline their supply chain operations, rendering them more agile and responsive to market shifts. This flexibility enables companies to adeptly adapt to fluctuations in demand and supply.

Global Artificial Intelligence in Transportation and Logistics Market was valued at US $ 2.8 Billion in 2022 and is expected to reach US $ 15.2 Billion by 2030 growing at a CAGR of 23.4% during the forecast period 2023 – 2030.

COVID -19 Impact

The COVID-19 pandemic has wrought profound and far-reaching effects on the Transportation and Logistics sector, with Artificial Intelligence (AI) emerging as a transformative force in navigating the challenges and opportunities that have arisen. One of the most striking impacts of the pandemic has been the heightened demand for contactless solutions. As physical interaction became a source of concern, AI-powered systems played a pivotal role in facilitating touchless operations within transportation and logistics. This transformation included the advent of contactless delivery mechanisms, digital documentation, and the automation of various processes within warehouses. These innovations not only improved safety but also accelerated the adoption of AI-driven technologies.

Supply chain disruptions were another conspicuous consequence of the pandemic. The fragility of global supply chains was exposed as lockdowns, border closures, and other unforeseen events disrupted the flow of goods. In response, AI was harnessed to enhance supply chain visibility and resilience. Predictive analytics driven by AI models helped businesses forecast shifts in demand and supply, enabling them to adapt swiftly to the ever-changing landscape of customer demands. AI has also played a pivotal role in enforcing safety measures and compliance with COVID-19 regulations. This was especially pertinent in the context of public transportation, where AI was used to monitor social distancing, mask usage, and other safety protocols. By facilitating compliance, AI bolstered passenger safety and confidence in these crucial services.

The pandemic accelerated the adoption of remote monitoring and management within the transportation and logistics sector. This was indispensable in enabling remote work and reducing the physical presence of staff in warehouses, terminals, and transportation hubs. AI-powered systems allowed for real-time monitoring of operations and rapid responses to contingencies, even when key personnel were working from home. Autonomous vehicles and drones, equipped with AI capabilities, gained prominence during the pandemic. These technologies offered efficient and contactless delivery solutions, reducing the risk of virus transmission. The rapid deployment of autonomous vehicles in last-mile delivery and the increasing use of drones for various logistics operations exemplify the adaptability and versatility of AI in the face of new challenges.

Data analysis and AI-driven insights have been instrumental in crisis management throughout the pandemic. AI technologies have analyzed vast datasets related to infection rates, travel restrictions, and border closures, providing valuable information for decision-making in logistics and transportation planning. Finally, the pandemic emphasized the paramount importance of operational efficiency. AI solutions were used to optimize route planning, reduce fuel consumption, and improve resource allocation to ensure cost-effectiveness during times of economic uncertainty. These operational enhancements were instrumental in maintaining the viability and competitiveness of businesses within the sector.

In conclusion, the COVID-19 pandemic has acted as a catalyst for the adoption of AI in the Transportation and Logistics industry. AI solutions have not only addressed the immediate challenges of the pandemic but have also laid the foundation for a more resilient, agile, and innovative future for the sector. As the world emerges from the pandemic, the integration of AI in transportation and logistics is likely to continue, driving efficiency, safety, and adaptability in the face of evolving circumstances.

Factors Driving the Market

Drivers

Rising demand for efficient and cost-effective transportation

The market for Artificial Intelligence (AI) in Transportation and Logistics is experiencing a robust upswing, primarily propelled by the surging demand for efficient and cost-effective transportation solutions. As global economies expand, there is an escalating need for transportation and logistics systems that can deliver goods and services with greater speed, precision, and cost efficiency. AI has emerged as a pivotal enabler in addressing these demands. By harnessing AI's predictive and optimization capabilities, businesses can fine-tune their logistics operations, streamline route planning, and reduce fuel consumption, resulting in substantial cost savings.

Moreover, AI's capacity to enhance supply chain management, predictive maintenance, and real-time decision-making in the transportation sector ensures that goods move seamlessly and that downtime is minimized. Consequently, the rising demand for these AI-driven efficiencies is fostering market growth, making AI in Transportation and Logistics a critical component in addressing the contemporary challenges of the transportation industry. This synergy of technology and transportation not only improves the bottom line for businesses but also augments overall service quality, making it an essential driver for the steady expansion of this transformative market.

Investment in AI research and development

Investment in AI research and development serves as the linchpin for propelling the growth of the Artificial Intelligence (AI) market in transportation and logistics. This investment powers innovation, leading to the development of more sophisticated and tailored AI solutions that address the unique needs and challenges of the industry. Moreover, it contributes to the enhanced accuracy and efficiency of AI algorithms, vital for applications like route optimization and real-time traffic analysis. Scalability is also a product of such investments, ensuring that AI solutions can grow alongside businesses. In addition to promoting safety and compliance with industry regulations, AI research fosters market expansion, as businesses recognize the potential of AI to revolutionize their operations and enhance their competitiveness. Consequently, investment in AI research and development acts as a catalyst for the ongoing growth and transformation of the transportation and logistics sector.

Challenges

High cost of AI systems

The high cost of AI systems has a significant impact on the market's growth trajectory. These elevated initial expenses can deter many businesses, particularly smaller enterprises, from embracing AI solutions. This limited adoption can create exclusivity, primarily benefiting large corporations and hindering innovation from smaller, more agile companies. It can also extend the time for businesses to realize a return on their investment, further discouraging uptake. High cost can also discourage new entrants and startups, making the market less competitive and stifling innovation. The segmented nature of the market, with AI relegated to niche industries, impedes its overall expansion. Moreover, industries or companies that can bear the high costs accelerate their digital transformation, creating disparities in competitiveness and economic growth. Overcoming these challenges may require cost reduction strategies, alternative pricing models, government support, collaborative efforts, and educational programs to promote broader AI adoption, thereby fostering market growth.

Trends

Improved customer service

Improved customer service, facilitated by the integration of Artificial Intelligence (AI) into the Transportation and Logistics sector, opens the gateway to a wealth of opportunities. AI enhances communication through the deployment of chatbots and virtual assistants, ensuring round-the-clock support and rapid issue resolution, ultimately fostering higher customer satisfaction. Personalization, made possible by AI's data analysis capabilities, tailors services to individual preferences, reinforcing customer loyalty. Predictive analytics alert customers to potential service disruptions, proactively offering alternatives and bolstering transparency and trust.

The optimization of last-mile delivery through AI results in quicker and more accurate deliveries, elevating the customer experience. Moreover, AI's ability to analyze customer feedback and sentiment informs service improvements, nurturing a customer-centric approach. By reducing errors and enhancing reliability, AI diminishes issues and returns, fortifying the overall customer experience. Consequently, companies that prioritize customer service through AI solutions gain a competitive edge, attracting and retaining customers, and expanding their market presence, thereby contributing to the growth of the AI in Transportation and Logistics Market.

Market Segmentation

By Component

By Component, the global Artificial Intelligence in Transportation and Logistics Market is divided into software, Hardware, and services.

The software segment holds the dominant position in the AI in transportation and logistics market, commanding a market share exceeding 48.72%. Meanwhile, the hardware segment is poised for rapid growth soon, fueled by the surging adoption of AI-enabled devices and sensors. The expansion of the software component is attributed to the rising need for AI-driven software solutions in areas like fleet management, supply chain optimization, and predictive maintenance. Concurrently, the hardware segment is experiencing robust growth with 24.65% CAGR, owing to the increasing utilization of AI-enabled devices and sensors, including cameras, LiDAR, and radar, which play a vital role in data collection for AI algorithms. Additionally, the services segment is on the rise due to escalating demand for AI-powered consulting, implementation services, as well as training and support services.

By Application

By Application, the global Artificial Intelligence in Transportation and Logistics Market is divided into Route optimization and scheduling, Predictive maintenance, Autonomous vehicles, Warehouse automation, inventory management.

In the landscape of the Artificial Intelligence (AI) in Transportation and Logistics Market, route optimization and scheduling stands at the forefront, boasting a significant market share of 26.7% in 2023.  The ascendancy of route optimization and scheduling is propelled by the imperatives of supply chain optimization and cost reduction. AI emerges as a pivotal tool, adept at scrutinizing real-time traffic data, weather conditions, and vehicle attributes to finesse routes and timetables. This refinement not only slashes delivery times but also curtails fuel consumption, delivering tangible efficiencies. Predictive maintenance, a swiftly growing contender with 20.34% CAGR, due to evolving strategy of companies to employ AI in foreseeing equipment malfunctions.

Such foresight enables companies to preempt costly downtime, enhancing the overall operational efficiency.

While autonomous vehicles remain in their formative stages, they carry the potential to transform the transportation landscape fundamentally. AI spearheads their development, empowering them to navigate roadways, make decisions regarding halting and commencing, and interact seamlessly with other vehicles and pedestrians. Warehouse automation is also experiencing rapid proliferation as businesses seek AI's prowess to automate labor-intensive tasks like picking, packing, and shipping. This automation enhances efficiency and trims labor costs. Inventory management, a more mature AI application, continues to thrive as companies harness AI to refine inventory levels, mitigating the risks of stockouts and overstocks while optimizing their operations.

By Region

By region, the global Artificial Intelligence in Transportation and Logistics Market is divided into North America, Europe, APAC and Others. Others is further divided into Middle East, Africa and South America.

North America takes the lead, commanding a substantial market share of 33.5% in 2023. North America's preeminence in this field is rooted in its rich legacy of innovation within the transportation and logistics sector. The region boasts a multitude of world-renowned transportation and logistics enterprises, cultivating a fertile ground for the development of AI solutions that cater to the industry's specific needs. North American companies have been early adopters of AI in transportation and logistics. They have invested in AI-based solutions to optimize supply chains, improve route planning, enhance customer service, and reduce operational costs.

Concurrently, the Asia-Pacific region stands out as the most rapidly expanding market for AI in Transportation and Logistics. It is forecasted to experience a robust compound annual growth rate (CAGR) of 22.2% from 2023 to 2030. This remarkable growth can be attributed to a constellation of factors, including the region's sizable population, burgeoning economy, and robust governmental endorsement of AI advancement. APAC is experiencing significant urbanization, resulting in increased demand for advanced transportation and logistics solutions. AI can help manage the challenges of burgeoning urban centers, such as traffic congestion and last-mile delivery logistics.

Competitive Landscape

The global Artificial Intelligence in Transportation and Logistics Market is consolidated with the presence of few major players contributing to the market revenue. This dominance of these major players is driven by their technological expertise, extensive resources, and established brand recognition. These companies typically offered comprehensive and diversified solutions to end use industries.

  • Google AI

Google AI stands as a research branch within Google, wholly committed to the advancement of cutting-edge artificial intelligence (AI) technologies. Established in 2016 and helmed by the accomplished Jeff Dean, Google AI has left an indelible mark on the AI landscape through its substantial contributions. These contributions encompass the creation of novel algorithms spanning machine learning, natural language processing, and computer vision. Beyond theoretical development, the division actively channels AI expertise into tangible, real-world solutions, applying its prowess to diverse domains, including healthcare, transportation, and education.

  • Microsoft Azure

Microsoft Azure stands as a comprehensive cloud computing platform, offering an extensive suite of services that encompass computing, storage, networking, and advanced analytics. Within the realm of public cloud services, Azure is a prominent player, holding its place alongside Amazon Web Services (AWS) and Google Cloud Platform (GCP). Debuting in 2010, Azure has witnessed remarkable expansion, evolving into one of the globe's foremost cloud platforms. Businesses spanning the full spectrum of size and scale, from nimble startups to large enterprises, rely on Azure to power a diverse array of workloads. These workloads encompass a broad spectrum, including web applications, mobile apps, and data analytics applications. Azure's flexibility and capabilities make it a go-to choice for a myriad of cloud-based solutions.

  • Amazon Web Service

Amazon Web Services (AWS) is a dynamic and all-encompassing cloud computing platform presented by Amazon, offering a comprehensive blend of infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and packaged-software-as-a-service (SaaS) solutions. Within AWS's extensive portfolio, you'll find a diverse array of services spanning compute, storage, networking, analytics, databases, machine learning, and artificial intelligence. This platform's hallmark is its extraordinary adaptability and scalability, capable of accommodating a broad spectrum of workloads, ranging from straightforward web applications to intricate enterprise-level systems. Renowned as one of the world's preeminent cloud platforms, AWS boasts a global reach, serving over 1 million active customers across more than 190 countries. Businesses of all shapes and sizes, from burgeoning startups to established enterprises, leverage AWS to underpin an array of operations, including web applications, mobile app development, and data analytics applications.

  • IBM Watson
  • Intel AI
  • NVIDIA AI
  • Siemens
  • Bosch
  • Uber
  • FesEx
  • UPS
  • Deutsche Post
  • CNH Industrial
  • JCB
  • Komatsu
  • Volvo Group
  • Scania AB
  • Daimler AG
  • Continental AG
  • Robert Bosch GmbH

Recent Developments

  • On January 10, 2023, Google AI and Waymo unveiled their collaborative effort to advance the field of autonomous trucking. Their partnership aims to harness Google AI's machine learning technology for road navigation and Waymo's autonomous driving technology for decision-making, encompassing functions such as starting and stopping, as well as interactions with other vehicles and pedestrians.
  • On March 8, 2023, Amazon introduced its innovative AI-powered tool named Amazon Logistics Route Optimizer. This tool leverages real-time traffic and weather data, utilizing machine learning to optimize delivery routes. This optimization not only reduces delivery times but also cuts down on fuel consumption, enhancing Amazon's logistical efficiency.
  • On May 11, 2023, FedEx joined forces with IBM to pioneer AI-powered solutions in the realm of supply chain management. By employing IBM's Watson AI platform, FedEx will analyze data derived from its extensive network of warehouses, trucks, and airplanes. The goal is to optimize supply chain operations, minimize costs, and enhance customer service.
  • On July 12, 2023, UPS introduced the UPS Delivery Assistant, a groundbreaking AI-powered tool. This system utilizes machine learning to scrutinize data originating from UPS's fleet of trucks and drivers, with the aim of identifying potential safety concerns and improving driver behavior.
  • On September 14, 2023, Deutsche Post teamed up with Microsoft to create AI-powered logistics solutions. These solutions will harness the power of the Microsoft Azure cloud computing platform, analyzing data from Deutsche Post's extensive network of post offices.

Artificial Intelligence in Transportation and Logistics Market Scope

Report Components Details
Base Year

2022

Forecast Period

2023 – 2030

Quantitative Units

Revenue in US $ 

Drivers
  • Rising demand for efficient and cost-effective transportation
  • Increasing focus on safety and compliance
  • Evolving consumer expectations
  • Investment in AI research and development
Challenges
  • High cost of AI systems
  • Data privacy and security concerns
  • Lack of expertise
Trends
  • New business models
  • Improved customer service
  • Increased productivity
  • Reduced environmental impact
Segments Covered

By Components (Software, Hardware, Services), By Application (Route optimization and scheduling, Predictive maintenance, Autonomous vehicles, Warehouse automation, inventory management)

Countries Covered

U.S. and Canada in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the APAC, Others include Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA), Brazil, Argentina, Mexico, and Rest of South America as part of South America

Market Players Covered

Google AI, Microsoft Azure, Amazon Web Service,  IBM Watson, Intel AI, NVIDIA AI, Siemens, Bosch, Uber, FesEx, UPS, Deutsche Post, CNH Industrial, JCB, Komatsu, Volvo Group, Scania AB, Daimler AG, Continental AG, Robert Bosch GmbH

Table of Contents

1 INTRODUCTION OF GLOBAL ARTIFICIAL INTELLIGENCE IN TRANSPORTATION AND LOGISTICS MARKET

1.1 Overview of the Market

1.2 Scope of Report

1.3 Assumptions

 

2 EXECUTIVE SUMMARY

 

3 RESEARCH METHODOLOGY

3.1 Data Mining

3.2 Validation

3.3 Primary Interviews

3.4 List of Data Sources

 

4 GLOBAL ARTIFICIAL INTELLIGENCE IN TRANSPORTATION AND LOGISTICS MARKET OUTLOOK

4.1 Overview

4.2 Market Dynamics

4.2.1 Drivers

4.2.2 Restraints

4.2.3 Opportunities

4.3 Porters Five Force Model

4.3.1. Bargaining Power of Suppliers

4.3.2. Threat of New Entrants

4.3.3. Threat of Substitutes

4.3.4. Competitive Rivalry

4.3.5. Bargaining Power among Buyers

4.4 Value Chain Analysis

5 GLOBAL ARTIFICIAL INTELLIGENCE IN TRANSPORTATION AND LOGISTICS MARKET, BY COMPONENTS

5.1 Overview

5.2 Software

5.3 Hardware

5.4 Services

6 GLOBAL ARTIFICIAL INTELLIGENCE IN TRANSPORTATION AND LOGISTICS MARKET, BY APPLICATION

6.1 Overview

6.2 Route optimization and scheduling

6.3 Predictive maintenance

6.4 Autonomous vehicles

6.5 Warehouse automation

6.6 Inventory management

7 GLOBAL ARTIFICIAL INTELLIGENCE IN TRANSPORTATION AND LOGISTICS MARKET, By REGION

7.1 North America

7.1.1 U.S.

7.1.2 Canada

7.2 Europe

7.2.1 Germany

7.2.3 U.K.

7.2.4 France

7.2.5 Rest of Europe

7.3 Asia Pacific

7.3.1 China

7.3.2 Japan

7.3.3 India

7.3.4 South Korea

7.3.5 Singapore

7.3.6 Malaysia

7.3.7 Australia

7.3.8 Thailand

7.3.9 Indonesia

7.3.10 Philippines

7.3.11 Rest of Asia Pacific

7.4 Others

7.4.1 Saudi Arabia

7.4.2 U.A.E.

7.4.3 South Africa

7.4.4 Egypt

7.4.5 Israel

7.4.6 Rest of Middle East and Africa (MEA)

7.4.7 Brazil

7.4.8 Argentina

7.4.9 Mexico

7.4.10 Rest of South America

8 COMPANY PROFILES

8.1 Google AI

8.1.1. Company Overview

8.1.2. Key Executives

8.1.3. Operating Business Segments

8.1.4. Product Portfolio

8.1.5. Financial Performance (As per availability)

8.1.6 Key News

 

8.2 Microsoft Azure

8.2.1. Company Overview

8.2.2. Key Executives

8.2.3. Operating Business Segments

8.2.4. Product Portfolio

8.2.5. Financial Performance (As per availability)

8.2.6. Key News

 

8.3 Amazon Web Services

8.3.1. Company Overview

8.3.2. Key Executives

8.3.3. Operating Business Segments

8.3.4. Product Portfolio

8.3.5. Financial Performance (As per availability)

8.3.6. Key News

 

8.4  IBM Watson

8.4.1. Company Overview

8.4.2. Key Executives

8.4.3. Operating Business Segments

8.4.4. Product Portfolio

8.4.5. Financial Performance (As per availability)

8.4.6. Key News

 

8.5 Intel AI

8.5.1. Company Overview

8.5.2. Key Executives

8.5.3. Operating Business Segments

8.5.4. Product Portfolio

8.5.5. Financial Performance (As per availability)

8.5.6. Key News

 

8.6 NVIDIA AI

8.6.1. Company Overview

8.6.2. Key Executives

8.6.3. Operating Business Segments

8.6.4. Product Portfolio

8.6.5. Financial Performance (As per availability)

8.6.6. Key News

 

8.7 Siemens

8.7.1. Company Overview

8.7.2. Key Executives

8.7.3. Operating Business Segments

8.7.4. Product Portfolio

8.7.5. Financial Performance (As per availability)

8.7.6. Key News

 

8.8 Bosch

8.8.1. Company Overview

8.8.2. Key Executives

8.8.3. Operating Business Segments

8.8.4. Product Portfolio

8.8.5. Financial Performance (As per availability)

8.8.6. Key News

 

8.9 Uber

8.9.1. Company Overview

8.9.2. Key Executives

8.9.3. Operating Business Segments

8.9.4. Product Portfolio

8.9.5. Financial Performance (As per availability)

8.9.6. Key News

 

8.10 FedEx

8.10.1. Company Overview

8.10.2. Key Executives

8.10.3. Operating Business Segments

8.10.4. Product Portfolio

8.10.5. Financial Performance (As per availability)

8.10.6. Key News

 

8.11 UPS

8.11.1. Company Overview

8.11.2. Key Executives

8.11.3. Operating Business Segments

8.11.4. Product Portfolio

8.11.5. Financial Performance (As per availability)

8.11.6. Key News

 

8.12 Deutsche Post

8.12.1. Company Overview

8.12.2. Key Executives

8.12.3. Operating Business Segments

8.12.4. Product Portfolio

8.12.5. Financial Performance (As per availability)

8.12.6. Key News

 

8.13 CNH Industrial

8.13.1. Company Overview

8.13.2. Key Executives

8.13.3. Operating Business Segments

8.13.4. Product Portfolio

8.13.5. Financial Performance (As per availability)

8.13.6. Key News

 

8.14  JCB

8.14.1. Company Overview

8.14.2. Key Executives

8.14.3. Operating Business Segments

8.14.4. Product Portfolio

8.14.5. Financial Performance (As per availability)

8.14.6. Key News

 

8.15 Komatsu

8.15.1. Company Overview

8.15.2. Key Executives

8.15.3. Operating Business Segments

8.15.4. Product Portfolio

8.15.5. Financial Performance (As per availability)

8.15.6. Key News

 

8.16 Volvo Group

8.16.1. Company Overview

8.16.2. Key Executives

8.16.3. Operating Business Segments

8.16.4. Product Portfolio

8.16.5. Financial Performance (As per availability)

8.16.6. Key News

 

8.17 Scania AB

8.17.1. Company Overview

8.17.2. Key Executives

8.17.3. Operating Business Segments

8.17.4. Product Portfolio

8.17.5. Financial Performance (As per availability)

8.17.6. Key News

 

8.18 Daimler AG

8.18.1. Company Overview

8.18.2. Key Executives

8.18.3. Operating Business Segments

8.18.4. Product Portfolio

8.18.5. Financial Performance (As per availability)

8.18.6. Key News

 

8.19 Continental AG

8.19.1. Company Overview

8.19.2. Key Executives

8.19.3. Operating Business Segments

8.19.4. Product Portfolio

8.19.5. Financial Performance (As per availability)

8.19.6. Key News

 

8.20 Robert Bosch GmbH

8.20.1. Company Overview

8.20.2. Key Executives

8.20.3. Operating Business Segments

8.20.4. Product Portfolio

8.20.5. Financial Performance (As per availability)

8.20.6. Key News

Global Artificial Intelligence in Transportation and Logistics Market Segmentation

Artificial Intelligence in Transportation and Logistics by Component: Market Size & Forecast 2023-2030

  • Software
  • Hardware
  • Services

Artificial Intelligence in Transportation and Logistics by Application: Market Size & Forecast 2023-2030

  • Route optimization and scheduling
  • Predictive maintenance
  • Autonomous vehicles
  • Warehouse automation
  • Inventory management

Artificial Intelligence in Transportation and Logistics by Geography: Market Size & Forecast 2023-2030

  • North America (USA, Canada, Mexico)
  • Europe (Germany, UK, France, Russia, Italy, Rest of Europe)
  • Asia-Pacific (China, Japan, South Korea, India, Southeast Asia, Rest of Asia-Pacific)
  • South America (Brazil, Argentina, Columbia, Rest of South America)
  • Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa, Rest of MEA)

Major Players:

  • Google AI
  • Microsoft Azure
  • Amazon Web Service
  • IBM Watson
  • Intel AI
  • NVIDIA AI
  • Siemens
  • Bosch
  • Uber
  • FesEx
  • UPS
  • Deutsche Post
  • CNH Industrial
  • JCB
  • Komatsu
  • Volvo Group
  • Scania AB
  • Daimler AG
  • Continental AG
  • Robert Bosch GmbH

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