Machine learning refers to a set of techniques that surround the study and practice of algorithms, which have the ability to learn from data. They are able to create programs from general behavior pattern recognition. In other words, machines can learn without being previously programmed for something specifically.
The learning process of the machine is similar to data mining. Both systems use 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, according to software parameters. Machine learning algorithms are classified between supervised and unsupervised. Supervised algorithms can apply what they have learned in the past and with the new data they can use it, for what we call training data. Unsupervised algorithms can draw conclusions from data sets without a priori knowledge.
This is not something new. Machine learning has much to do with the original idea of artificial intelligence; in fact, it is a type of AI.
New information technologies and telecommunications have marked a before and after in companies, in some sectors more than in others. Logistics is one of those sectors that have been impacted with great force. The ability to use and analyze massive amounts of continuously generated data has led to many improvements, for example, in continuous processes and optimization of routes.
As said earlier, we are not talking about something new. The novelty is that now large amount of data can be collected by companies in order to create the basic raw material that is used for machine learning. Sometimes companies have the conflict of what to do with them, and data by itself is not useful. When we talk about massive amounts of data it becomes essential to have proper administration and analysis, so we can turn them into a useful tool. Given this reality, we have two choices: we can simply store them, which represent a loss of valuable information and opportunities for the company, or we can use them to learn and grow.
Thanks to the advance and development of new information technology, 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 huge amounts, to grow steadily and rapidly. Flexible algorithms and the ability to adapt them independently, result in a myriad of solutions, ranging from software, to online recommendations. For example, as we talked a few months ago, the development of self-driven vehicles, without a driver.
Machine learning applications in a logistics company
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 include machine learning. These are just some examples: search engines, filtering emails, facial recognition, medical diagnostics, etc.
But, what kind of applications in machine learning should we have for logistics companies? 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, in order 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, to determine, 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, which profitable services should adapt to the needs of customers, etc.
– The famous cars and trucks without driver …
Applying machine learning in a logistics company is not easy. It requires in addition a professional programmer with a profile specialized in probability and statistics. However, it is an option to consider, especially when it comes to problem-solving nature of complex algorithms. That is very helpful when we need to find precise solutions in the shortest time possible.
The key of machine learning is the ability to adapt and build a decision tree based on known data. Their applications are so broad and the creative capacity of each one of these applications are intended to detect patterns in data or to answer certain questions in a predictive way, saving us time in the study of data and the definition of casuistry which could take weeks, months or even years.
Finally, an important aspect to be put on the table is the kind of applications we are talking about will answer: What will happened? But not, why? This fact is transcendental and clashes frontally with our empirical training. We will be able to detect that something will happen, but if we want to know why it will happen, we need further analysis. The reality is that we often just want to know what will happen, because then we can act accordingly and remedy it. For example, thanks to these tools we are able to predict an earthquake. Then, we can prepare ourselves, displacing people, etc., and this actions should be enough. We don´t need to know that this is because of a tectonic plate moved or that there was a tsunami in such a place that caused it.
So there is no doubt that we are only at the beginning of a revolution, where the use of big data and machine learning tools, will be in all areas of our life, especially in logistics. Regarding this, the recommendation is to think as our grandparents and save everything, especially data. Because the unstructured data as we have today: Excel sheets, PowerPoint presentations, emails, documents of all kinds, etc.; are the raw material that is needed by machine learning in order to work and help us to manage our business better.
