Machine Learning Applied to Logistics

According to IBM, “ Machine Learning is a branch of Artificial Intelligence and Computer Science that focuses on using data and algorithms to mimic the way humans learn, gradually improving their accuracy. ” . By the way, it was an IBM employee, Arthur Samuel, who coined the term Machine Learning.in 1959, thanks to research on the movements of the game of checkers. The goal is to allow machines to create parameters to solve unplanned issues. That is, in this new computing format, the algorithms are not static. They manage to improve and adapt automatically, without having a programmer all the time correcting bugs and flaws. Of course, this is only possible with repetition and insertion of updated data. The machine then performs tests and, through trial and error, is able to identify what can be improved. In other words, when making a mistake, the system will know that it must avoid that action, repeating only the correct answers.

The most common computer language within this technology is Python, created in 1989. But others are also used, such as Java, JavaScript, R (preferred by IT scientists) and C/C++. In addition to language, Machine Learning needs the support of other Artificial Intelligence tools (such as ERP software, Enterprise Resource Planning) and also of Big Data, since to learn the machine uses algorithms, data, preferably in abundance. The greater the volume of information, the faster and more efficient this process is.

In Logistics, this technology has a number of advantages. The site Maplink Global lists some of them:

• “ Less dependence on manual work — Despite the need to feed Machine Learning software with data and parameters, logistics becomes more independent of human presence. After all, the systems, after being put into practice, go on a journey of continuous improvement.
• Decrease in the incidence of failures — No matter how qualified and trained employees are, they are more prone to errors than machines. With machine learning in logistics, therefore, the tendency is for the failure rate to drop considerably.
• Fast detection of bottlenecks and immediate correction — Logistics is one of the most costly sectors, with high transport costs, taxes, maintenance and, mainly, expenses related to losses and failures. Machine Learning in logistics facilitates and speeds up monitoring, data capture and fault detection.
• Increase in the level of logistical efficiency — With the increase in automation, reduction in rework and in the time of each process, we managed, with machine learning, to generate much more efficiency for logistics.
• Employees can focus energies on more strategic functions — Many functions can be performed more efficiently and productively by machines and software. Thus allowing employees to focus efforts and time on more important actions, which machines cannot perform.
• Increased end customer satisfaction — If we managed, with this technology, to reduce deadlines, guarantee ideal product conditions, stock and efficient transport, naturally, we would improve end customer satisfaction.
• Capturing Valuable Data for Future Planning — We’re in the age of data. With machine learning, we can filter the results and build much more efficient logistics planning .”

And how can machine learning be used in logistics? Below are some examples:

• Supply Chain Visibility — Due to the high degree of intelligence, information and evolution of machine learning in logistics, we get valuable details of every step. This allows managers to have greater visibility of the supply chain.
• Demand Forecasting — Forecasting demand is extremely complex. But with Machine Learning, we were able to use statistics and detect sales patterns to anticipate fluctuations in demand. That is, production is able to predict declines and rises and allows the readjustment of orders, production and purchases automatically.
• Inventory management — The machine captures up-to-date data and information, without the characteristic immobilization of human management. With repetition, new patterns are identified, and the stock becomes much more efficient, with no shortage or surplus of products, as both are bad.
• Warehouse automation — Machine Learning allows machines to be able to reproduce human actions over time. This, in the future, will facilitate the automation of warehouses, which can be controlled by voice commands, for example.
• Route creation — With self-learning, the software can create increasingly optimized routes, considering elements such as distance, state of conservation of the pavement, traffic volume and robberies.
• Maintenance of fleets, equipment and tools — The IoT (Internet of Things) set, AI (Artificial Intelligence) and Machine Learning collaborate in identifying items that need maintenance, before they, in fact, become unusable. This is possible by inserting the maintenance history, daily use, brands, expiry date, etc.
• Creating a relationship of trust with customers — thanks to all this technology, customers can track their order in real time and know where their order is. In addition, the degree of accuracy increases considerably, leaving any consumer satisfied.

At Águia Sistemas, we can help your company in everything that involves Intralogistics. This includes Machine Learning. Keep in touch with us.