Artificial Intelligence applied to logistics management

liza89 · Post by **liza89** » Sat Feb 01, 2025 8:56 am

As already mentioned, Artificial Intelligence (AI) aims to recreate the human capacity for reasoning in order to analyze data and, from there, make decisions/provide precise and, of course, intelligent actions – which drastically reduces the possibility of errors.

For the logistics and shipping areas, it is already possible to find companies that use AI applied to robotics, with the use of intelligent machines that promote transportation, storage and even physical inventory management.

Cloud (web) platforms like ours at Rizer , where you armenia telegram screening can create your own logistics management system ( take a free 7-day trial ), are examples of artificial intelligence applied to logistics management.

In the case of the Rizer platform , you can create customized modules, focusing on tasks, inventories, fleets, write-offs, replacements, expiration dates, SKU location (and so on), without even having programming knowledge – our artificial intelligence solution is intuitive and educational and was created with entrepreneurs in mind who have never developed a logistics management system before .

Another relevant aspect regarding the use of artificial intelligence for logistics management concerns the ability to predict and avoid failures, as well as the time and money savings generated.

Machine Learning applied to logistics management
In computational terms, when a machine learns to do/analyze/respond to something, we say that an algorithm has been created. Eureka!

When we bring this reality to logistics management , it is important that the logistics management system used by the institution offers Machine Learning resources – since having technology as an ally in the decision-making process is fantastic!

As an example of a Machine Learning algorithm that is possible and fully applicable to logistics management systems, we have machine learning about predicting low stock levels, to maintain the availability of SKUs, without losses.

Let's explain it in a simpler way:

Imagine that you are the person responsible for the logistics management of a company that sells video games, and that there are 3 months left until Children's Day;
You probably already know the average seasonal sales for this date and are already working on replenishing stock for this purpose;
This average is achieved through its current logistics management system, which, although famous, is static in terms of predictive analysis based on market demand data (artificial intelligence that integrates platforms that measure online demand for a given product/service);