What does machine learning contribute to logistics?

Machine learning, or automatic learning, has arrived to provide an answer to a large extent to one of the greatest challenges of modern logistics: information management. The vast number of processes involved in the logistics process generates a massive amount of data that must be analyzed and utilized correctly. Otherwise, we will not only be wasting an important asset, but the proliferation of data will end up generating conflicts and becoming a problem.

Machine learning is a new technology that, when applied to logistics, helps to conclude delicate and complex tasks such as demand forecasting and supply chain planning. It achieves this with a high degree of automation and autonomy, detecting trends and key variables among all generated data on its own.

What is machine learning, and how can it be applied in logistics?

Machine learning is the branch of artificial intelligence dedicated to the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. This technology is currently in an effervescent development process, and its application in the logistics sector is already becoming an increasingly present reality.

Data itself is useless and needs to be managed, organized, and analyzed to become an asset and a functional tool. Here is where the machine learning algorithm comes into play in logistics. It helps to make better decisions, predict future results and needs, reduce overall costs, and improve delivery and shipping systems.

Applications and examples of machine learning in logistics

One of the main advantages of applying machine learning in logistics is the opportunity to lower costs while keeping customer expectations intact. In the supply chain, machine learning helps reduce both operational and inventory costs and response times.

In transportation logistics, machine learning models create the most optimized routes to reach the destination faster. Soon, this aspect will leap forward with the popularization of autonomous vehicles.

On the other hand, machine learning analyzes data to discover the best way to optimize inventory. It gives access to inventory in real-time and in advance to facilitate picking, storage, and planning of future deliveries by avoiding errors during loading and delivery.

The world of logistics and supply chains is complex and unforeseen events often occur. Machine learning simplifies planning and schedules different tasks more accurately and efficiently, avoiding unwanted interruptions.

Another strength is that it can predict production demands in advance. Computer systems based on machine learning search for patterns and connections among all the data they receive and store to detect any possible excess or deficit in demand.

At a pure cybersecurity level, machine learning can detect cyber-attacks or any type of intrusion in the data communication network.

In short, in a context where there is increasing pressure to deliver goods both nationally and internationally in a faster, more efficient, and cost-effective manner, machine learning is called upon to play an increasingly important role in the logistics of companies.