What challenges does Artificial Intelligence face in Industry 4.0?

AI is revolutionizing the industry as it was known until very recently. It is undoubtedly one of the most important drivers of the current Industry 4.0 framework, just as is the case with the machine learningThe predictive analysis it provides is one of the most important milestones for boosting industrial business. Thanks to the combination of both technologies, an industrial company is able to detect errors, monitor production quality in real time and optimize the supply chain in detail that was unthinkable until recently. Those companies that have invested in the implementation of these technologies are able to be more competitive with respect to their competitors in terms of both quality and costs. However, it is also true that the adoption and implementation of the Artificial Intelligence The concept is constantly evolving and, although it offers extraordinary results, it is still in its early years of existence. After all, it is a concept that is constantly evolving and, although it offers extraordinary results, it is still in its first years of existence. For this reason, company systems managers and consulting firms specializing in digital transformation in the industry must go hand in hand on a path that promises to be exciting. Let's take a look at some of these challenges.

Need for real-time response

Manufacturing and its predictive Industry 4.0 applications are very sensitive to latency or response time. So much so, that if this latency is greater than desired, anticipating potential problems that may arise is not so simple. An ultra-fast response is required. The issue is that an advanced system cannot afford the luxury of interacting with the cloud, since the time it takes to process data and obtain information is excessive. For this reason, the response has to be in real time, in milliseconds. So there is no other option than local data processing, and in order to be able to address it, it is essential to have the appropriate IT infrastructure. Both control systems and decision making need to be carried out through edge computing. Intelligent production requires that predictive models can be implemented in each of the sensitive terminals of the process and that they respond in real time and in an adjusted manner.

Legacy systems

It is very common to find all kinds of equipment in an industrial company, a fact that makes system interoperability difficult. The market offers such a wide variety of technologies, machines and components that they are often incompatible when it comes to connecting them together, as required by Industry 4.0. Although it is also true that the Industrial Internet of Things (IIoT) does not yet have established frameworks and standards, the main problem to be solved is that of legacy machines. The ecosystem of a current industry must have compatible components that use rules to connect to PLCs, SCADA, MES or ERP, and in that sense, everything points to the OPA AU protocol becoming the most accepted protocol in data modeling and communication for Industry 4.0. In any case, in order to fine-tune such a specialized process as how to carry out the renewal and adaptation of the system, companies need the advice of specialists. Especially when it comes to such critical investments.

Access and use of data

The complex environmental conditions in which a production plant may be located are not always the best allies when it comes to collecting the most reliable data. In addition, manufacturing may take place in a space that is remote from the site where the data is collected and processed. On the other hand, the company may object to storing data in the cloud for security reasons and a local solution may have to be devised. The point is that all of these difficulties may require engineering work. ad hoc that is capable of overcoming all these obstacles. This proper channeling of the different actors that provide the data means that the IT and OT teams have to operate in an aligned manner and under the planning of a specialist in both areas. In this sense, HEXA Ingenieros is a consulting firm specializing in both IT and OT and, therefore, capable of tackling projects of such magnitude with a expertise integral. Big Data Graph

Multidisciplinary approach to Artificial Intelligence

 Industrial projects involving Artificial Intelligence require the formation of multidisciplinary teams with experience in data management, algorithms and machine learning. This forces the companies that undertake them to hire experts in Artificial Intelligence and machine learning, but the relative scarcity of these in the market makes it not as easy a task as with other positions. Still, there are automated machine learning tools that OT experts can leverage to build predictive models without necessarily requiring data scientists on staff. New AutoML platforms automate up to 100 percent of the AI and machine learning development workflow, using an AI engine to automatically discover meaningful patterns and create ML-ready feature tables from relational, operational, temporal, geographic location and text data.

Distrust of Artificial Intelligence

 More than 80 percent of industrial companies suffer from downtime, at enormous cost. Typically, OT experts have relied, and still do in large numbers, on their experience and instinct rather than an algorithm. However, just as the industry eventually gave in to the benefits of relying on robots, it can no longer turn its back on AI and machine learning. After all, these are technologies that are not only more than proven to be efficient, but do not mean that the company's technology experts are no longer as necessary as ever. All that is needed is an update that can be given in the best terms with specific training thanks to comprehensive consulting firms such as HEXA Ingenieros.
Share this