By: Borja Arrizabalaga
The machine learning (machine learning) refers to a set of techniques that revolves around the study and practice of algorithms that have the ability to learn from the data. It is able to create programs from general behavior pattern recognition. Put another way, is that machines can learn without having to program them specifically.
The learning process of the machine is similar to data mining (data mining). Both systems use the data to look for patterns. However, instead of extracting data for human understanding – such as applications of data mining – machine learning uses this data to detect patterns and modifies them, automatically, parameters software accordingly. Machine learning algorithms are classified between supervised and unsupervised. Supervised algorithms can apply what they have learned in the past with the new data and use what we call training data. Unsupervised algorithms can draw conclusions from data sets without a priori knowledge.
This is not anything new, machine learning has much to do with the original idea of artificial intelligence, in fact, is a type of AI (artificial intelligence).
The new information technologies and telecommunications have marked a before and after in companies, although in some sectors than in others. Logistics is one of those sectors that are impacted with great force. The ability to use and analyze massive amounts of data generated continuously has led to many improvements, for example, in continuous processes and the optimization of routes.
Although, as said earlier, not talking about anything new, novelty is the large amount of data that companies are now able to collect the data are the basic raw material used machine learning. Sometimes companies take the conflict of what to do with them, and that data alone are not useful. When talking about massive amounts becomes essential to proper administration and analysis of them to turn them into a useful tool. Given this reality, we have two choices: we can simply store them, representing a loss of valuable information and opportunities for the company. Or we can use them to learn and grow.
Thanks to the advancement and development of new information technology, the machine learning today, little or nothing has to do with machine learning solutions that we know from the past. Today, we can apply and use algorithms or data volumes in amounts ranging grow steadily and rapidly. It is flexible algorithms and the ability to adapt independently, resulting in a myriad of solutions ranging from software to online recommendations, for example, as we talked a few months ago, the development of vehicles drive autonomously, without driver.
Machine learning applications in logistics company
The applications are almost endless; in fact, we can adapt machine learning to as many situations as we have data. There are many regular activities in our lives and daily routines that are driven machine learning. These are just some examples: search engines, filtering emails, facial recognition, medical diagnostics, etc.
But what may have applications in machine learning logistics company? These are some of the applications in the management of the supply chain:
- Facial recognition, voice or objects applicable, especially in stores.
- Predictions and forecasts. Very useful in the phase of transport, for example, to obtain data on traffic or weather conditions; or even to avoid errors in technological equipment.
Optimization methods to create faster and more effective, assessing, for example, what is the best time to execute a particular task.
- Analysis of consumer behavior and productivity. It is possible, through machine learning to detect potential customers, predict which employees can be more productive and profitable services adapted to the needs of customers, etc.
- The famous cars and trucks without driver …
Applying machine learning the logistics company is not easy, requires, in addition to a professional programmer, also a profile specializing in probability and statistics. However, it is an option to consider, especially for problem-solving nature of complex algorithms that are helpful to find precise solutions in the shortest time possible.
The key machine learning is its ability to adapt and build a decision tree based on known data. Ai