AI in Logistics and Transportation Industry: Key Benefits

AI in Logistics and Transportation Industry: Key Benefits - 1
Paul Francis

Table of content

    In the age of technology, businesses deploy new solutions to streamline their operations, save time, and offer the best available service to their clients. And logistics plays a crucial role in it. According to the World Trade Organization, international commerce volumes have grown 45 times since the early days of the General Agreement on Tariffs and Trade. Handling these cargo flows is getting more challenging. This article will discuss how companies resolve issues using AI logistics.

    The Role of Artificial Intelligence in Logistics

    AI in Logistics and Transportation Industry: Key Benefits - 2


    According to the report by Market Research Guru, in 2022, the worldwide market for using AI in logistics and transportation was worth about $5.17 billion. Experts predict this market will grow by around 19.43% annually until 2028. By then, it could be worth about $15.01 billion. 

    How is AI improving logistics? One way is with digital twin technology, which creates virtual copies of real things like trucks or warehouses. These copies help companies quickly spot problems along the supply chain and react faster. 

    Companies also use AI for logistics in warehouses and transport to automate tasks like forecasting, inventory management, and planning routes. They even apply AI to control robots and forklifts. These things matter for the surging e-commerce market, which is projected to grow from $4.45 trillion in 2024 to $7 trillion in 2029.

    And in places like Africa, where getting supplies to remote areas can be tough, drones controlled by AI deliver important medical supplies to places that are hard to reach. No wonder, the worldwide market for cargo drones is expected to grow by more than $17 billion by 2030.

    How Is AI Impacting the Logistics & Transportation Industry?


    As we already know, AI in logistics and transportation assists businesses by making warehouses more efficient, optimizing routes, and conducting predictive analysis. Let’s examine in detail how AI simplifies the customer experience.

    Predictive Analysis


    We all need to learn from our own mistakes. This is especially true when talking about logistics. Gaps in the supply chain may affect the outcomes. However, pinpointing every bottleneck can become particularly challenging, especially as operations expand to a larger scale. You need more people and more time for comprehensive analysis. 

    And in this case, AI models are the ones to assist businesses. AI can conduct data analysis across various levels and time intervals. And that’s not just collecting data. AI models carefully review big data volumes and make their conclusions and predictions. At this checkpoint, the company is approaching a bottleneck that could potentially transform into difficulties in the next six months. 

    Moreover, employing machine learning and similar methodologies for processing vast datasets guarantees very low error rates, enabling more efficient human resource utilization. Implementing predictive analysis enables strategic planning of shipments through optimized routes, streamlining the entire process. Furthermore, informed decisions regarding different transportation modes can be made, optimizing them for superior outcomes.

    Big Data


    Whoever owns the information, owns everything. This is especially true in the era of technology and data-based decisions. Every day around 379 million terabytes of data is generated. So the data is big indeed. And processing such big volumes and benefiting from them can be challenging. In logistics it should be sorted by:

    • Size. This is what we face most often. Big Data is about petabytes to exabytes of material.

    • Type. Contemporary data sources include text, spreadsheets, images, audio, PDF files, and other formats. And all of that should be structured properly for tracking the trends.

    • Processing speed. Big Data is continuously generated. The ability to process data at higher speeds enhances its potential significance.

    • Variability. Big Data exhibits temporal instability, with its value fluctuating over time.

    AI in Logistics and Transportation Industry: Key Benefits - 3

    This entails gathering, processing, and scrutinizing vast quantities of data produced by diverse origins, such as sensors, GPS devices, RFID tags, customer engagements, and other sources. Using artificial intelligence in logistics for processing Big Data may help businesses in:

    • Route optimization
    • Road accident number reduction
    • Avoiding out-of-stock at warehouses
    • Scheduling vehicles’ technical maintenance to avoid truck shortage

    Goods like drugs and vaccines that require refrigerators can be transported properly with AI analysis. Processing lots of parameters like temperature, humidity, and vibration AI may offer the best solution.

    Computer Vision

    Computer vision systems, capable of interpreting digital images and videos, are referred to as such. These systems possess the ability to perceive and comprehend their surroundings akin to humans. Advancements in artificial intelligence, visual system technology, and computing power have facilitated this achievement.

    AI in Logistics and Transportation Industry: Key Benefits - 4

    No surprise, the computer vision market is projected to grow from $25.8 billion in 2024 to $46.96 billion in 2030. The share of computer vision in transportation reached nearly 10% among other industries in 2022. 

    How can logistics benefit from it? DHL – one of the global express delivery leaders – gives five main points:

    1. Occupational Safety. Computer vision makes workplaces safer by spotting potential dangers in places like warehouses and depots. It helps lower risks and prevents accidents by ensuring employees wear the right protective gear and follow safety rules.
    2. Health Protection. Besides keeping workplaces safe, computer vision can also recognize when someone is sitting or moving incorrectly or showing signs of being tired. It can then alert them to take a break.
    3. HR Planning and Operations. Computer vision looks at how work is done in a building and determines the best paths for employees. It can also spot when there aren’t enough workers or when things could be done more efficiently. Plus, it helps keep unauthorized people out, adding to security.
    4. Facility and Device Management. Computer vision keeps an eye on buildings and sites all the time, letting maintenance teams know if something needs fixing right away. It helps plan how to use buildings and machines effectively and makes sure there are always enough supplies on hand. It can also make managing fleets of vehicles easier.
    5. Shipment Management. Computer vision helps with tasks like packing loads efficiently, checking product quality, and scanning barcodes. It makes many tasks in shipping and receiving faster, simpler, and more accurate.

    In retail AI-powered computer vision solutions may update brand packaging for efficient inventory management. In this way, they keep businesses aware of the actual up-to-date stock.

    Autonomous Vehicles

    Do we need driving skills? Autonomous vehicles say, not necessarily. Step by step they gain their market share on the roads. In 2019 there were 31 million vehicles with different levels of automation in use. Let us discover what can be automated:

    • Level 0: No driving automation. No machine assistance to drivers. Automating the vehicle at this stage is not a task.

    • Level 1: Driver assistance. At this stage, a car is fully driven by a human. The person keeping the steering wheel holds full responsibility for safety. However, some auxiliary tools like cruise control and lane-keeping assistance are available.

    • Level 2: Partial automation. In this setup, the vehicle manages both steering and acceleration/deceleration. However, it doesn’t achieve full self-driving capability since a human driver remains in the driver’s seat, ready to take over control whenever necessary. Examples of Level 2 systems include the Tesla Autopilot and Cadillac (General Motors) Super Cruise.

    • Level 3: Conditional driving automation. Here the car starts doing the most of driving job. However, the driver should always be prepared to take over control if the situation requires their intervention.

    • Level 4: High driving automation. Humans are no longer required to intervene; they can work, watch movies, or even sleep while the vehicle drives autonomously. The vehicle must safely come to a stop, such as in a parking lot. However, Level 4 autonomy still relies on specific conditions like defined routes, highway driving, or navigating parking garages.

    • Level 5: Full driving automation. Human attention is unnecessary as the “dynamic driving task” is completely removed. These cars will lack steering wheels and acceleration/braking pedals. They won’t be restricted by geofencing and will have the capability to navigate freely, performing tasks comparable to those of skilled human drivers.

    According to Statista, by 2025, nearly 60 percent of newly sold cars worldwide are projected to possess Level 2 autonomy. Moving forward to 2030, Level 2 autonomous vehicles are anticipated to remain prevalent in the market, with Level 3 and Level 4 autonomous vehicles constituting roughly eight percent of new car sales.

    Maybe in the future vehicles won’t need drivers to get to technical maintenance stations. As AI in logistics progresses numerous sensors and specialized software will analyze the car’s condition and make an informed decision about the need to repair something.



    The global logistics robotics market size was valued at $8.78 billion in 2023 and is projected to surpass $39.55 billion by 2033. Big online shopping companies in Asia Pacific are spending a lot of money on robots and machines to make their warehouses work better. They want to pack orders faster and deal with increasing online orders. Factories in the area are also starting to use robots more to make things quicker, better, and safer.

    Logistics robots are mainly used in warehouses and storage places to move goods around. They work on set paths, carrying items for shipping and storing all day and night. These robots help cut down on logistics costs and make the supply chain smoother.

    Some examples of logistics robots include robotic arms that organize items, AGVs that transport goods outdoors like in farming, and mobile robots in stores that keep track of inventory and act like small warehouses. There are also logistics robots used in hospitals and labs to deliver medicine and samples.

    Although logistics robots can have different jobs, they’re usually mobile robots meant to automate moving goods. The more they’re up and running, the more money they make, no matter what they’re doing. Companies are starting to realize how useful logistics robots can be, setting the stage for their market to grow quickly. And we can talk endlessly about the benefits of AI in logistics.

    AI Use Cases in the Logistics Industry

    AI in Logistics and Transportation Industry: Key Benefits - 5

    We had a quick overview of AI-driven technologies and now let us check out some AI logistics use cases from famous brands. These companies fully benefit from AI solutions as they help them stay competitive and outperform other market players.

    Automated Warehouses

    No doubt, Amazon is one of the best examples of AI in logistics. Everybody knows about their network of automated fulfillment centers that use the latest technologies to process orders faster. Presently, a significant portion of Amazon’s vast inventory—ranging from necessities to miscellaneous items—is managed by the company’s robots. 

    These robots, exceed 750,000 in number and operate across over 300 Amazon fulfillment centers globally. In October 2023, Amazon announced the launch of new robots – Sequoia and Digit – to speed up operations. 

    Sequoia enables Amazon to swiftly identify and stock inventory arriving at its fulfillment centers, achieving a speed enhancement of up to 75% compared to current methods. Moreover, when orders are placed, Sequoia streamlines the order processing time within a fulfillment center by as much as 25%.

    Digit possesses the capability to navigate, grip, and manage items within warehouse spaces and corners in innovative manners. Its dimensions and structure are tailored for buildings designed for human occupancy. Amazon perceives a significant potential in scaling a mobile manipulator solution like Digit, envisioning its collaborative integration with employees to enhance operational efficiency.

    Optimizing Shipping Process


    When talking about shipping it’s worth mentioning Maersk – one of the global leading container lines. It’s one of the brightest AI in logistics examples. Currently, Maersk utilizes an AI solution for determining and proposing the most efficient route, along with another system designed to optimize operations within terminals. 

    At present, the group employs artificial intelligence for approximately 15-20 percent of its logistics activities. However, within the next five to seven years, this percentage could surge to 70-80 percent.

    Moreover, Maersk’s success is significantly attributed to its strategic investments in AI and collaborative partnerships. By teaming up with AI startups and technology firms, Maersk has spearheaded the development of groundbreaking solutions for contract negotiations, warehouse management, and supply chain visualization. These alliances have empowered Maersk to leverage AI capabilities and drive enhanced efficiencies across its operations.

    Inventory Management 


    Here Walmart, one of the most well-known retailers, would be a great example. Their AI-powered inventory management system uses past data and predictive tools to smartly position holiday goods in warehouses, stores, and distribution centers. Their upgraded supply chain, with automated centers and efficient delivery methods, ensures items reach customers quickly. The system connects 4,700 stores, fulfillment centers, and distribution centers.

    When Walmart makes AI and machine learning systems for holidays, they start by looking at lots of data and business rules to create different machine learning models. These models help us predict what might happen during the holidays. They use information from past sales and online activity to teach the models.

    They also think about things like future weather, big economic trends, and who lives in different areas to guess how much stuff people might want and if there could be any problems delivering it. By putting all this info together, systems can find and fix any mistakes or problems. So when it’s time for people to start shopping, their AI and machine learning work has already made sure everything runs smoothly.

    Workforce Management

    There are plenty of examples of using AI for human capital management. But the solution applied by UPS is something really out-of-the-box. “Today, we’re leveraging artificial intelligence through our Languages Across Logistics (LAL) platform to break down language barriers,” the company said in its statement.

    The multilanguage bot provides an employee with instructions and if the newcomer misses something the virtual assistant reminds him about that. With a multinational team of more than 500,000 people across the globe, the platform allows a worker to communicate easily with his supervisor who may speak a different language.

    Demand Forecasting


    Another delivery giant, FedEx, is also embracing AI for making informed business decisions. Having integrated AI-powered delivery time estimations, FedEx has experienced a notable transformation into a data-centric enterprise. Utilizing its extensive data reservoir, the company is optimizing operations and elevating customer service.

    Below are key components of FedEx’s data-driven strategy:

    • Data-optimized logistics. FedEx harnesses its wealth of data to refine logistics operations, boosting efficiency and precision in package management and delivery.

    • Digital-centric approach. Embracing a digital-first mentality, the company employs advanced technologies such as machine learning to scrutinize data and generate precise forecasts for volume and demand.

    • Machine learning predictions. Leveraging machine learning algorithms, FedEx enhances accuracy in volume forecasts within its Ground unit, facilitating improved resource management and strategic planning.

    • Proactive carbon footprint assessment. Through its FedEx Sustainability Insights platform, FedEx offers shippers predictive carbon emissions data, empowering customers to make informed decisions and reduce environmental impact.

    • AI-driven customer support. In customer service operations, FedEx utilizes AI tools like chatbots and virtual assistants to deliver swift and accurate responses to customer queries.

    Predictive Maintenance

    Another delivery giant DHL uses AI-driven solutions for efficient fleet management. DHL has a big team of around 600,000 workers who use about 300 planes and over 100,000 trucks. Coordinating all these vehicles effectively was getting tricky because of the different types and ways of use.

    Each type of vehicle works differently. DHL needed to figure out which vehicle was best for each job. To do this, they require data and smart tools to help managers decide which vehicle to use where so they can do their job right.

    And their new solution created by Samsara resulted in a 26% reduction in accidents.

    How To Integrate an AI Solution in Your Ecosystem


    Deploying AI models can be tricky and requires meticulous planning and expertise. So, how can a company benefit from AI in logistics? Some of them create their own AI and ML teams, others hire specialized external vendors. Let us have a closer look at some case studies.

    As logistics is crucial for e-commerce, this project is an example of the company’s digital transformation. Uvik’s client needed a modern platform able to analyze customers’ behavioral patterns and forecast sales. The brief included machine-learning algorithms tailored for this specific data analysis and making predictions.

    Uvik’s experts created a customized solution deploying such technical stack as Python 3 and AWS Cloud Services for backend processes and data handling, as well as machine learning frameworks like SKLearn and SageMaker. 

    The results were impressive. The client got a new platform that allowed him to scale and offer its customers exactly what they needed. It also had a positive impact on its inventory by avoiding out-of-stock emergencies thanks to predictive analysis.

    AI in Logistics and Transportation Industry: Key Benefits - 6


    AI in transportation and logistics offers plenty of benefits, ranging from streamlining operations and facilitating predictive analysis to eliminating language barriers in international companies and beyond. Companies equipped with AI-powered tools gain a competitive edge over their peers, enabling them to navigate the rapidly evolving business landscape with agility and efficiency.

    As we see artificial intelligence logistics transportation aren’t just buzzwords. In today’s fast-paced world, businesses have limited time for transformation and must seize opportunities swiftly. To ensure your business stays ahead of the curve, Uvik’s team of AI and ML experts stands ready to assist in elevating your operations to new heights.


    What are the key benefits of incorporating AI in logistics operations?

    The incorporation of AI in logistics operations brings numerous benefits such as enhanced efficiency, improved decision-making, reduced costs, and increased productivity.

    How is artificial intelligence improving efficiency in transportation and logistics?

    Artificial intelligence is improving efficiency in transportation and logistics by optimizing route planning, predictive maintenance, demand forecasting, and real-time tracking, leading to faster delivery times and reduced operational costs.

    Can you provide examples of AI use cases in the field of logistics?

    Examples of AI use cases in logistics include predictive analytics for demand forecasting, autonomous vehicles for transportation, robotic automation in warehouses, route optimization algorithms, and chatbots for customer service.

    What do you envision as the future trajectory of AI integration in logistics?

    The future trajectory of AI integration in logistics is expected to involve further advancements in autonomous vehicles, drone delivery systems, predictive analytics, machine learning algorithms, and the widespread adoption of AI-powered tools for end-to-end supply chain optimization.

    How useful was this post?

    Average rating 0 / 5. Vote count: 0

    No votes so far! Be the first to rate this post.

    AI in Logistics and Transportation Industry: Key Benefits - 7

    Need to augment

    your IT team with

    top talents?

    Uvik can help!