Machine Learning in Logistics

Minimization of a human factor and automation of business processes allow diminishing risks and expenses. Machine learning approaches help to develop solutions for resolving logistics and supply chain challenges.

5 min read, Jul 8

Trends concerning AI application and digital transformation are one of the most stable ones for the last decade. Considering changes in the business environment and the fact that artificial intelligence is playing a big role in technical evolutionary processes of various fields it’s clear that this tendency won’t change and further on. Machine learning (ML) is an integral part of AI studying computer statistical models and algorithms that are used for independent and efficient task performance. In other words, a machine is trained with an application of certain techniques to be able to analyze and process data in a certain way as well as make following predictions and decisions without being thoroughly instructed each time.

Machine learning & AI in transport and logistics started the changing process of the fields since the 90s. The scope history showed that operational efficiency can’t be achieved without advanced planning, quality forecasting, continuous tracking and maintenance of task execution.

Machine Learning application

The modern demands for any scope are regular improvements in customer experience that now is a much wider definition than quality and timely services. AI allows enabling personalization of services on shorter terms and regularly make changes depending on client requirements.

Benefits of machine learning for logistics

This direction of AI development is drastically changing all aspects of the supply chain. The technology earned such a crucial role since it was able to address the burning field challenges concerning execution, maintenance, and strategic planning and partly or entirely resolve them.

Machine learning applications for logistics and other scope branches are created to optimize or improve the following issues.

Demand forecasting

The accuracy of future production tasks and needs is vital for the smooth workflow of the whole supply chain. ML allows doing more scrupulous and yet less time-consuming research and analysis considering important factors that can’t be taken into account otherwise.


With machine learning, it’s possible to make any stage of the goods journey more transparent for all process parties. ML solutions can help to prevent fraud and theft during the production, detect damages during delivery to make handling changes as well as track the whole package path to an end customer.

Visibility in logistics


Using machine learning supply chain logistics and other services it's easier to assure and bring to the required quality level on each stage. Data analysis allows detecting weak spots in the workflow and discovering optimal approaches to fix them.


Logistics processes warehouse- and production-related can be managed with digital solutions based on ML. It can help to reduce the influence of factors concerning professional incompetence or internal staff relations as well as raise the security level within facilities.


Management systems implemented with machine learning in shipping and logistics like TMS or WMS allow optimizing various sectors of the supply chain. In combination with logistics automation processes, an enterprise reaches the level to fulfill modern requirements.

Efficiency in logistics


Usage of mentioned-above solutions results in minimizing freight costs and budget reducing without affecting service quality. So extra financial contributions that are required for the digital transformation of the scope and the enterprise, in particular, will pay off not just in additional revenue but also in customer engagement.


Efficient systems and predictive analytics minimize the possibility of situations where a company can’t perform timely and accurately. Such business approach allows earning a reputation of a reliable partner within various scopes.

Machine learning use cases in logistics and supply chain

ML allows taking a custom approach to the challenges of each particular company providing services within the supply chain scope. Besides enterprise resource planning (ERP) machine learning applications for logistics and warehousing can have the form of the following solutions.

Supply Chain Planning. With advanced big data analytics building, a company strategy becomes simpler and more accurate. Such software allows defining key aspects of supply chain based on statistics and current company and environment conditions.

Warehouse Management

Warehouse Management. Such systems can cover various workflow tasks like security assurance and inventory management. They can be implemented within a complicated WMS or separate tools like an anti-theft solution.

Transport Management

Transport Management. These systems can be a module of WMS or even ERP and maintain processes like routing, scheduling, loading, shipping, invoicing and billing. They can be focused on specific aspects like freight management or a certain customer group like 3PL users.

Workforce Management

Workforce Management. Such software solutions or ERP parts are oriented on optimal usage of the company staff and its potential. It includes scheduling, monitoring, and assessment of task execution, workload forecasting, interaction optimization, and real-time tracking and reporting.

Risk Management in Logistics

Weather forecast. These machine learning applications for logistics are usually integrated and help to predict weather-related risks by calculating transport impedance to environment conditions and delays that can be caused by its imperfection. They also refine the estimated time of arrival (ETA).

Machine learning and logistics: the future

ML solutions greatly contribute to AI logistics optimization as well as the whole supply chain. According to MarketsandMarkets, the logistics automation market worth will grow by 57 percent by 2023. Such statistics showing digital transformation is slowly but confidently taking over the field.

Considering that the modern business environment is highly dependent on customer expectations it significantly raises the bar for service providers forcing them to rely more on technology than on people. Possibilities of machine learning allow creating suitable solutions for their workflow optimization and the whole company boost among competitors through time.

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