Applications of artificial intelligence in industry

As we have mentioned in previous posts, Artificial Intelligence (AI) refers to technological developments capable of giving machines ways of acting similar to those developed by human beings. Motor skills can be simulated, an example being robotic arms on assembly lines, or cognitive skills in systems capable of interacting with the environment and solving problems based on the variables interpreted, such as image recognition systems. In other words, Artificial Intelligence perceives the reality it has been programmed for, through data, and analyzes it in order to generate an automatic response based on its interpretations. As you can imagine, its applications in industry are countless and offer significant benefits related to efficiency, productivity, profitability, quality, and process optimization. Let’s take a look at some of them:

Computer vision

A branch of Artificial Intelligence, Machine Learning, and within it the development of convolutional neural networks typical of Deep Learning, allows us to get computers to read images, identify inconsistent elements, and classify them based on learned patterns, in other words, to create computer vision. Let’s look at an example to understand this better.

Thanks to Deep Learning, we can develop surveillance systems for large facilities. In these cases, the machines know the ideal image of the premises and the elements that may be present in them, alerting to anomalous events as soon as they are detected. If a camera points to a large RO-RO port area, the system knows that there may be cars and their natural location. If a cloud of smoke were to appear in the images captured by the monitoring equipment, for example, the system detects it as an intrusion and triggers an alarm signal with a probabilistic identification of the strange element captured.

But there is still another step. The machines are trained and learn by self-enriching their databases as they work with new images, making their contribution increasingly efficient. As we can see, the usefulness of this type of Artificial Intelligence application is enormous and, under the same principles, extends to the maintenance of large infrastructures (roads, rail networks, high-voltage lines, hydrocarbon transport, etc.), to the recognition and classification of elements on conveyor belts, to quality control, or to the monitoring of vast natural areas (logging companies, disaster assessment, land control, etc.), to name just a few examples.

Predictive maintenance

Another application where the benefits of Artificial Intelligence in industry are most evident is predictive maintenance. Industrial machinery is a vital asset for companies. Productivity, and consequently the profitability of organizations, depends on its performance.

Breakdowns and wear are inherent to any device or production line. Their constant operation and the need for them to perform according to established parameters require efficient maintenance policies that reduce downtime and optimize the replacement parts needed, and therefore the investment required.

In this regard, new disruptive technologies, together with the development and falling costs of sensor technology, have contributed to a giant leap forward, moving from preventive maintenance to predictive maintenance. And what does this mean? It means being able to anticipate possible machine failures or performance drops and take corrective action in advance, minimizing the time machines remain out of operation.

By analyzing and interpreting the data extracted from the devices themselves (heat, vibration, speed, hours of activity, etc.), alerts can be obtained regarding deviations from optimal performance in their early stages. This speed in detecting failures makes it possible to implement corrective actions precisely when they are truly needed, eliminate fixed schedules and therefore unnecessary interventions, and avoid major breakdowns that result in much longer downtime.

Demand forecasting

Based on the same principle as the previous system—anticipating what will happen—we find demand forecasting tools. As the name suggests, in this case the future estimates refer to the sales that will be made. Knowing in advance the expected turnover of SKUs simplifies and streamlines operations as complex as warehouse management, taking stock optimization to its highest levels of efficiency.

To obtain these forecasts and act on their results, the company’s internal and external data (sales history, market trends, supplier lead times, turnover rates, etc.) are compiled and processed using predictive algorithms to generate a future demand scenario for our products, tailored to the reality of the company and the sector.

In this way, by knowing immediate and future needs, warehouse management becomes much simpler and more efficient. And this is not just an advertising slogan. These systems make it possible to adjust stock levels, avoiding both stockouts and overstocking, both of which have significant financial impacts—the former due to lost customers, and the latter due to tied-up capital and the risk of product obsolescence.

In addition, in tools like Purchases, our very own assistant, which we know best, it is possible to automate processes to receive direct purchasing suggestions based on present and future needs, current stock, supplier responsiveness, and more, and to set up alerts to increase process efficiency. All this information is also displayed on visual, intuitive dashboards so that all the necessary information can be analyzed at a glance.

Conclusion

As we can see, Artificial Intelligence has become one of the greatest assets available to industry. Its applications are countless—we have only highlighted three of them—but it can be applied across all areas of activity, in any sector, to improve a wide range of tasks. Companies that want to remain competitive need to rely on Artificial Intelligence to optimize their processes. A few years ago, it may have seemed that its implementation was only for large corporations, but nothing could be further from the truth—today, companies of any size can benefit from its enormous potential.