Training neural networks for logistics

The implementation of devices developed with artificial intelligencebecomes a necessity in the processes of the supply chain, since the activities developed by the latter continue to be very manual. This aspect of companies has been directly impacted by thetechnological revolution, which presents solutions that contribute to the optimization of time and resources.

This way, they can generate an added value in the operations of industries in sectors such as retail, transportation, packaging, and Freight Forwards.

These technologies contribute to the operation of the companies because investing in them, it is possible to identify benefits in terms of productivity increase, decrease in the investment of resources in labor and increase of profitability due to the agility in the processes that can be related to the supply chain.

Let’s start by defining what artificial intelligence is. This is a branch of technology that is recognized as that action executed by the human being to train a neuronal network by means of algorithms, with the objective that a machine finds a determined pattern and has a specific behavior within a system.

It is valid to take into account that, among the most common uses of this technique within a company are functions such as object recognition, that is, placing a box or bag under the device developed with Artificial Intelligenceand that this manages to identify the characteristics of the piece, its nature. It is also applied in quality systems to find the imperfections in products. Additionally, it is frequently used in people counting.

The previous implementations are some illustrative examples that serve to recognize the importance of this science in the business environment.

3 applications of artificial intelligence in logistics

Here we will mention in detail applications that are projected as opportunities for the industrial sector, mainly in terms of optimization of the logistics chain.

1. Automation of picking and packing in warehouses

Picking and packing in retail, logistics and packaging companies are processes that take up a great deal of time. Some companies, in order to streamline and increase their productivity, have adopted a practice called cross docking, which allows for faster pick and delivery of goods, given the elimination of the storage process, within the supply chain.

Additionally, to improve picking and packing, artificial intelligence can be implemented from the machine learning branch, to train the neural networks of a device, and that this is who performs the actions in an automated manner. For example, to train specific machines so that they have the characteristic of recognizing packages or objects and can pack them in the most optimal way inside a box. This is done without the need to involve people, so there is a decrease in the resources invested in labor and time spent on the activity.

2. Route planning

Within logistics there are procedures that are fundamental, such as the transport of goods, which must be carried out by one person, whose performance depends directly on various variables such as traffic, weather, distances, among others.

Currently, it is possible to implement technologies that contribute to the decrease in collection and delivery times, allowing this task to be executed more efficiently. For example, the tools to which information is entered such as the origin and destination of the journey. And this makes an analysis and taking into account various variables such as vehicle traffic, distances, number of packages to be collected and truck capacity, tells the supervisor from the office which is the route that the driver should take to perform the activity most optimally.

3. Optimization of storage space

It is a factor that directly influences the effectiveness of the supply chain, because if there is no clear order, classified products and adequate storage space, the entry and dispatch of goods can take longer than necessary, which not only affects the productivity of operators, but aspects such as delivery times to final customers. which eventually turns into lost money.

In order to achieve greater agility, some companies have begun to implement automated storage systems such as WMS, which provides valuable information regarding the location of goods and packages to achieve greater use of space.

However, there are also opportunities, such as the development of systems that focus on the correct selection of packaging for each product or merchandise, which is achieved based on specific data such as weight and measurements. Data that must be accurate to achieve greater efficiency.

For the challenge of achieving greater accuracy in the information obtained from objects, both regular and irregular shapes, there is already an effective solution that provides logistical processes with agility. CubiQ, a device that cubes goods in 1 sec and provides comprehensive assistance for taking measurements through the use of intelligence and artificial vision technologies.

CubiQ, customized solutions

It is essential to bear in mind that, for the implementation of machine learning projects, it is necessary for companies to be able to adapt to the digital environment, which requires a great effort on the part of companies to digitize the information they have, the formats, templates and the information they receive.

When carrying out a project like the one mentioned above, the company can already start to make deep analyses of the relevant data it has and with the use of big data, determine the use that will be given to it in the automation of the processes.

To conclude, at present, companies are looking for the perfect ally in technology to achieve their objectives aimed at effectiveness, productivity and profitability. And taking into account the situation faced worldwide, where many companies are being affected by the closure of their production and declining sales. It is the perfect time to invest in technology and process automation, in order to reduce costs.

Do you want to learn more about the opportunities and challenges posed by artificial intelligence and machine learning in the logistics sector?