Data enrichment software and artificial intelligence for industry 4.0

To understand the role of data enrichment and artificial intelligence in the manufacturing context, we must start from the centrality of the data in an Industry 4.0 project. As in any other context, in fact, even in manufacturing, digital transformation does not mean “adding” technologies to a consolidated plant, but rather a much wider and deeper change, albeit enabled by the technology itself. Industry 4.0, smart manufacturing, data enrichment and AI can exist without robotics but not without the centrality of the data, which is the lowest common denominator of a successful model.

A smart manufacturing structure is an infinite source of data: a good part are those acquired by IIoT sensors and industrial machinery and processed with a Cloud or edge model according to requirements, but in reality the sources are many and include MES systems (Manufacturing Execution System), the company ERP, all the Supply Chain Management systems, as well as WMS for warehouse management and so on. Without forgetting, then, the data that can be acquired directly from the final product, from the company’s customer care (chatbots are an inexhaustible source…), even from the Internet through crawlers, to understand the trends and habits of end users.

This description would be enough in order to understand the amount of data that gravitates around a Smart Manufacturing project and, above all, how fundamental a data enrichment project is which transforms its complexity into valuable information for production planning purposes, logistics or business objectives. The development, enrichment and synthesis of data, typical data science activities, are fundamental to fully exploit the advantages of a 4.0 model, which as anticipated is not limited to adding sensors and robots to the production lines but relies on the ‘informative treasure’ present in the available data, which come from within the company but also from outside. Moreover, if you are able to coordinate the data coming from the production plant with those of sales and external ones, you can reach that concept of personalized production which is another of the great objectives of the 4.0 model.

The role of Artificial Intelligence

When the company decides to adopt a data-driven approach, at that moment we start talking about Artificial Intelligence. In the manufacturing context, which, as anticipated, is a veritable mine of data, the technologies that fall within the definition of Artificial Intelligence find wide application possibilities. Furthermore, since the theme is constantly evolving, it can be assumed that the current use cases will gradually be extended with new interesting implementations.

On a practical level, Artificial Intelligence is the basis of predictive maintenance of machinery, a typical textbook case when it comes to the data-driven approach to manufacturing. Here, the Machine Learning algorithms can highlight subtle anomalies in the machines and provide a maintenance operation before the fault occurs. All this leads to two benefits for the company: in the meantime one avoids breakdowns, with potential machine downtime, and one is no longer restricted to the criteria of scheduled maintenance, which causes production slowdowns even when there would be no need for them.

Although this is a typical example, predictive maintenance is only one of the manifestations of AI in the production context: one can think, for example, of using it as a decision-making support for the management of automatic systems, whether they be for production or storage, or again for quality control that uses Computer Vision technologies to detect imperfections that are completely invisible to the eye.

Another trend of the moment is the Digital Twin: the ‘digital twin’ is a virtual reproduction of an object or a real system, managed by a software that faithfully replicates the dynamics in front of external inputs: this allows companies to simulate the behavior of a device or a product in front of particular test conditions without having to use real objects, with all the benefits that this entails in terms of costs and, sometimes, safety. The “virtualization” of the factory will allow new automation models that will increasingly lead to the creation of a digital “brain” (the company brain) that can orchestrate business and production processes harmoniously, creating a continuum between two mountains (OT and IT) that until today have lived independently. Furthermore, the creation of a digital twin will allow the introduction of the concept of cyber range, a virtual model of the factory aimed at simulating cyber attacks and implementing fundamental awareness processes to better manage the human factor during security processes.

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