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April 26, 2024

Machine Learning in Logistics: 10 Use Cases of AI and ML


Artur Haponik

CEO & Co-Founder

Reading time:

14 minutes

Artificial Intelligence and machine learning are conquering more and more industries and spheres of our lives, and logistics is not an exception. AI and machine learning in logistics can be a great help when it comes to the supply chain sphere. Using them, it is possible to optimize the processes, avoid mistakes which humans can make or miss, predict future opportunities and challenges.

Therefore, make the business itself more successful and profitable. Here are some more details regarding the benefits of implementing AI in the supply chain. So keep reading to discover how AI and Machine Learning algorithms can help your business to develop.

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Machine Learning in Logistics: Supply Chain Planning

Supply chain planning, or SCP, is among the most important activities included in SCM (supply chain management) strategy. Therefore, it is crucial to have reliable tools for developing efficient plans. If you implement machine learning, your supply chain decision-making processes can be optimized significantly. Analyzing huge data sets and applying intelligent algorithms, you and your team will balance demand and supply, and at the same time optimize the delivery processes.

Another great thing, apart from better supply decisions, is the fact that human intervention is minimal. You won’t have to analyze those loads of data on your own. Artificial Intelligence (AI) algorithms will do everything for you and save you from making mistakes. You will only have to set up the parameters.

“Machine learning is a game changer for supply chain automation.” –

Interested in machine learning?
Read our: Machine Learning. What it is and why it is essential to business?

Warehouse management using machine learning

Proper warehouse and inventory-based management is a must for efficient supply chain planning. Why? Well, because both over-and under-stocking can turn into a real challenge for your business and destroy even the most efficient SCP strategy.

Machine learning and its forecasting feature can solve the problem and completely change your warehouse management for the better. And, again, artificial intelligence can analyze a big data set much faster than you will even be able to do, and easily avoid all the mistakes which humans can make.

“Machine learning’s most significant contributions will be in providing supply chain operators with more significant insights into how supply chain performance can be improved, anticipating anomalies in logistics costs and performance before they occur.” – McKinsey

Track and warehouse analysis

Computer Vision (CV) is a field of study which is responsible for developing diverse techniques that help computers to see and understand images and videos. And this is exactly that tool which can provide you with warehouse automation and solve a number of tasks. For instance, computer vision systems can automate the barcode reading process and, therefore, accelerate and simplify it.

They can also monitor the warehouse perimeter and track the employees, analyze the data, and prevent thefts and violation of the safety rules. And thanks to facial recognition technology, a computer vision system is also able to identify who is entering and leaving the warehouse territory.

AI in logistics for demand prediction

To improve your supply chain efficiency[1], you can use artificial intelligence and machine learning in order to predict the demand or improve the demand forecasting. In case you have already tried to do something like that. On the basis of the past experience, you will get a detailed analysis of all the factors which may have an influence on the demand. Using this knowledge, you will be able to make the right business decision.

And just like in many other cases, artificial intelligence and machine learning are much more effective than traditional methods of demand prediction. The thing is that those methods which you may be used to consider fewer demand-influencing factors. Thus, such predictions can’t be as reliable as those ones made with the help of advanced technology.

Logistics route optimization

To reduce the costs of shipping and make the shipping process faster, you can use artificial intelligence to decide on the best routes. This is especially important in case you are a large e-commerce company with a lot of customers. They are always happy to get their orders as soon as possible, without any delays. And artificial intelligence (AI) is what you need to analyze the existing routing, do the track route optimization. Therefore, you will be able to reach better results and bigger profits.

Predicting peak hours using AI in logistics centers

Artificial intelligence and machine learning can monitor and predict traffic and other factors that can somehow influence your shipping time. Peak hours in logistics centers are also an important factor, so we recommend you use the technology to predict and, therefore, avoid them. As a result, you will spend less time in the centers and make your customers happier.

Supplier selection and supplier relationship management

Selecting a reliable supplier and maintaining a proper relationship with them can be extremely challenging. If you make the wrong choice, your business can suffer, and the same can happen in case you make a mistake when managing your cooperation. In the worst case, your business can even fail.

But if you apply machine learning to the data sets based on your supplier relationship management actions (for instance, audits and credit scoring). You will get pretty reliable predictions for every interaction with your potential or already existing supplier. This trick will help you to avoid many mistakes and build a mutually beneficial collaboration.

Supplier Relationship Management, warehouse

Foreign language data cleansing and building data robustness

Natural Language Processing, or NLP, if short, is an element of machine learning and artificial intelligence (AI) domains which is a great solution when you have to decipher loads of foreign language data. In some cases, language barriers can be very hard to deal with.

As a result misunderstanding can lead to serious problems and inefficient business decisions. But with the help of NLP, you and your team will avoid the potential troubles and streamline those compliance and auditing actions which were unavailable before due to the language issues.

Workforce planning

Workforce planning is a must for any modern organization. It involves such processes as recruiting, retention, employee development, redeployment, performance management, and some others. Machine Learning and artificial intelligence (AI) solutions can significantly simplify them and make your workforce planning strategy more efficient.

Thanks to this, you will get a happier team. This is important, as employees who like their organization and the way of work are more productive. And with such a team, your business will become more successful.

Workforce Planning, machine learning in logistics

Autonomous vehicles – AI in logistics and shipping

Logistics and shipping are pretty obvious things when talking about the supply. In most cases, goods have to be somehow delivered to the customer, contractor or whoever else. And here we have a lot of restrictions! For instance, drivers are allowed to drive non-stop only for a certain amount of time. After this, they have to take a break or be replaced by another driver. But having at least two drivers on every delivery truck can be expensive, while a need to wait until one driver will rejuvenate can significantly prolong the shipping time.

Both these issues can have a catastrophic impact on your business, especially in the modern world of severe competition. However, autonomous vehicles can solve these problems, and save your budget and time. The only thing here is that such vehicles are not advanced enough at the moment. So you will have to wait a bit. But as soon as they enter the market, don’t miss this opportunity. Autonomous vehicles are a great choice when it comes to shipping.

“Self-driving vehicles, automatically choosing the most efficient route… Artificial Intelligence will dramatically improve logistics.” – Dave Waters

Drones used in the delivery

Delivery drones are one of the latest solutions that enable logistics companies to deliver products to the most difficult places. Companies often struggle to get their packages delivered to places where a ground transfer is not safe or reliable, and in some cases even impossible.

Drones have revolutionized delivery logistics, especially for companies such as pharmaceuticals, where products often have a short shelf life. Problematic transport often results in the waste of products or the need to invest in specialized warehouses, which are a significant expense.

Drones used in the delivery

The latest AI use cases

AI in Logistics: Marketing and sales departments

AI solutions play an important role in improving marketing processes in logistics companies. One of the many examples is email marketing. Thanks to artificial intelligence, this usually time-consuming task can now be automated. Now, marketers can focus on more creative tasks while AI-based software takes care of repetitive tasks.

Chatbots – AI solution in logistics that automates customer service

Every organization operating in logistics knows how much work it takes to offer high-quality customer service. Customers often expect companies to answer their questions and solve their problems immediately.

The delivery processes are complex and not fully predictable, so problems arise from time to time – that you can be sure of. How can you optimize customer service so it’s effective and cost-efficient?

The answer is chatbots. Chatbots for customer service can take over the most common call center tasks such as tracking shipments, delivery requests, reordering, and answering frequently asked questions.

In addition, chatbots can analyze customer experiences and draw conclusions on how to improve them. Companies can now better understand their customers’ needs and respond to them immediately.


Dynamic pricing with AI in logistics

In short, dynamic pricing is advanced real-time pricing that responds to changes in supply and demand, as well as to changes in competitors’ prices and the prices of dependent products. As a result, companies can offer optimal prices for their products that will attract more customers.

How does dynamic pricing software work? It uses machine learning algorithms to analyze the customer’s historical data in real-time. This way, it can react faster to changes in demand and adjust product prices.

Damage detection

Nothing disappoints your customers like opening a new purchase only to find out it’s broken. This experience often leads to bad reviews and customer churn.

To prevent such situations, implement computer vision solutions to identify damages. This advanced technology will enable you to determine the damage depth, find out what type of damage it is, and make changes to prevent or at least reduce further damage.

Damage detection, fragile

Automation of back-office tasks

Nowadays, companies can automate many back-office tasks thanks to hyperautomation.

Hyperautomation can be achieved using a variety of technologies such as artificial intelligence, process mining, robotic process automation, and more.

Here is a list of some back-office tasks that you can automate for your business:

  • Generating reports. Logistics companies can now automatically generate basic reports that are necessary to update managers and make sure every employee is aligned.
  • Tracking and scheduling. Machine learning in logistics helps you organize cargo pipelines, create transport schedules, assign employees to different tasks and implement package tracking in the warehouse.
  • E-mail processing. Process automation robots can analyze the content of automatically generated reports and send appropriate e-mails to stakeholders.

back-office tasks, reports

Wrapping up – AI in logistics

Companies face tremendous pressure to implement AI and machine learning to improve operational efficiency and streamline business decisions with futuristic systems. Advanced technologies can also help logistics businesses to provide the ability to optimize production, logistics, warehousing and final delivery at various levels.

AI in logistics improves customer experience. With machine learning-based solutions, your products will be delivered in perfect condition and on time. Additionally, your team will gain extra time and business insights that will help your management team make successful decisions and ensure the growth of your business.

Now you know how useful artificial intelligence (AI) and machine learning applications can be in logistics and supply chain management.

However, if you have any questions, don’t hesitate to get in touch with us. We would be pleased to provide you with an even better understanding of machine learning services in logistics.

See our machine learning consulting services to find out more.

FAQs About AI and Machine Learning in Logistics

How do AI and machine learning contribute to logistics and supply chain management?

AI and machine learning revolutionize logistics and supply chain management by optimizing processes, minimizing errors, predicting future challenges, and enhancing overall efficiency. These technologies analyze vast amounts of data, enabling businesses to make informed decisions and ultimately improve their success and profitability.

What role does machine learning play in supply chain planning?

Machine learning significantly enhances supply chain planning by optimizing decision-making processes. By analyzing large datasets and employing intelligent algorithms, machine learning aids in balancing demand and supply, as well as optimizing delivery processes. It reduces human intervention while improving accuracy and efficiency.

How does machine learning benefit warehouse management?

Machine learning, particularly through forecasting capabilities, improves warehouse management by preventing over- or under-stocking issues. AI algorithms analyze data rapidly, avoiding human errors and ensuring efficient inventory management. This optimization leads to better overall supply chain performance.

What is the significance of computer vision in logistics?

Computer vision technology automates tasks such as barcode reading, warehouse monitoring, and employee tracking, improving warehouse efficiency and safety. It enhances security measures, streamlines processes, and contributes to overall logistics automation.

How can AI aid in demand prediction and forecasting?

AI and machine learning enable accurate demand prediction by analyzing historical data and considering various influencing factors. Compared to traditional methods, AI-driven predictions are more reliable and comprehensive, leading to better-informed business decisions.

What role does AI play in logistics route optimization?

AI optimizes shipping routes to reduce costs and enhance efficiency, especially for large e-commerce companies. By analyzing existing routing data and performing route optimization, AI ensures timely deliveries and increased customer satisfaction.

How can AI predict and mitigate peak hours in logistics centers?

AI technology monitors and predicts factors like traffic patterns to anticipate peak hours in logistics centers. By avoiding peak hours, businesses can streamline operations, reduce waiting times, and improve overall efficiency.

How does machine learning aid in supplier selection and management?

Machine learning analyzes supplier relationship management data, providing insights and predictions for interactions with suppliers. This helps businesses make informed decisions, avoid mistakes, and establish mutually beneficial partnerships.

What is the role of natural language processing (NLP) in logistics?

NLP assists in deciphering foreign language data, overcoming language barriers, and improving communication in logistics operations. By enabling efficient language processing, NLP enhances compliance, auditing, and decision-making processes.

How does AI contribute to workforce planning in logistics?

AI solutions streamline workforce planning processes, including recruitment, retention, and performance management. By optimizing these processes, businesses can improve employee satisfaction and overall organizational success.

How do autonomous vehicles impact shipping logistics?

Autonomous vehicles offer potential solutions to challenges like driver fatigue and shipping delays. While not yet fully advanced, autonomous vehicles hold promise for reducing costs and improving efficiency in shipping logistics.

What are the benefits of using drones in delivery logistics?

Drones enable deliveries to remote or difficult-to-reach areas, enhancing delivery logistics, especially for time-sensitive products like pharmaceuticals. They reduce the need for specialized infrastructure and improve delivery efficiency.

How do AI solutions improve marketing and sales in logistics?

AI automates tasks like email marketing and customer service, freeing up time for marketers to focus on creative endeavors. Additionally, AI-driven insights help businesses better understand customer needs and optimize marketing strategies.

What is dynamic pricing, and how does AI facilitate it in logistics?

Dynamic pricing adjusts product prices in real-time based on changes in demand, competition, and other factors. AI-driven algorithms analyze customer data to offer optimal pricing strategies, attracting more customers and maximizing profits.

How can computer vision aid in damage detection in logistics?

Computer vision technology identifies and analyzes damages to products, enabling businesses to prevent or minimize further damage. This improves product quality and customer satisfaction in logistics operations.

What back-office tasks can be automated using AI in logistics?

AI enables automation of back-office tasks such as report generation, tracking and scheduling, and email processing. This streamlines operations, improves efficiency, and reduces manual workload for logistics companies.

This article is an updated version of the publication from 21 May, 2021.


[1] Brian Myerholtz. Demand Forecasting: The Key to Better Supply-Chain Performance. Nov 04, 2014. URL: Accessed May 27, 2019.


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