With the rise of the internet and electronic commerce in recent years, the logistics sector has undergone major changes in order to adapt and be able to offer a service capable of meeting the increased demand in areas such as transport or the preparation of orders.

In today post we are going to review some of the technologies that make logistics a more efficient sector.

I4.0 technologies in the logistics sector

In order to offer a higher quality, efficient and optimized service, what is known as Logistics 4.0 has emerged. Which consists of the use in logistics of technologies and methodologies typical of Industry 4.0.



With the increase in the amount of work in both the preparation and the treatment of packages, the need arose to automate the work as much as possible in these processes.

For this reason, robotics has become one of the most useful technologies in the field of logistics that allows automation and increased efficiency.


Use of robotics in logistics 4.0

The use of robots for the automation of packaging and palletizing of orders is common. In this way, a robotic arm can take care of preparing the packaging of various objects, saving resources and improving efficiency.

On the other hand, it is also common in the logistics and package movement sector to use AGVs to transport orders and pallets within a certain area. In this way, this process can also be automated and the traceability of the packages in the work area can be improved, thus creating a more robust system against possible failures.

Big Data

In the logistics sector, a massive amount of data is produced on a daily basis regarding various parts of the process of an order or the transport of goods, such as the number of certain objects that are transported in a day, most followed transport routes, errors made. …

All this data obtained and stored is what is known as Big Data.

Use of Big Data in Logistics 4.0

Through the treatment and analysis of Big Data, in the logistics sector 4.0 information can be collected that allows optimizing and increasing the efficiency of the entire logistics process and facilitating decision-making.

For example, it is possible to analyze the delivery points to determine which are the most optimal routes, the quantity ordered for a specific product to determine future orders, or the analysis of where errors usually occur in the process in order to correct them.


The use of sensors in transport and logistics have also meant great advances that have allowed the optimization of processes and a greater and more precise collection of data (which is very useful as mentioned in previous sections). Thanks to them you can obtain information such as the location of packages in real time, the state they are in or by which hands it has passed throughout the process. In this way it is possible to manage information in practically real time, being very useful for the development of Logistics 4.0.

Use of Sensors in logistics 4.0

One of the most widely used examples of sensors in logistics and transportation processes are RFID tags. These adhesive labels allow, by means of a reader, to offer all kinds of information that can be sent through the network to, for example, modify the status of an order. These sensors are also of great interest to the end user since they are the ones that make it possible to offer information about products or orders in progress, allowing, for example, the traceability of a certain order knowing its location at all times.

Artificial Intelligence

As mentioned in previous sections, reading and collecting data is an element of great importance for Logistics 4.0. In the analysis of this data is where Artificial Intelligence can play a role of great importance.

The collected data can be processed using Machine Learning techniques to draw conclusions from different aspects of the process.

Use of Artificial Intelligence in logistics 4.0

Artificial intelligence is very useful for the evolution of Logistics 4.0 since at the same time learning from these conclusions automatically in order to improve future decisions taken. Data processing using this technique allows us to anticipate certain situations and minimize possible damage, for example, the possibility of traffic jams on certain routes and possible alternatives or the forecast of future orders for a product.

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