{"id":23122,"date":"2025-01-23T11:13:45","date_gmt":"2025-01-23T10:13:45","guid":{"rendered":"https:\/\/www.regestaitalia.eu\/artificial-intelligence-in-manufacturing-revolution-or-mere-evolution\/"},"modified":"2025-06-03T15:35:48","modified_gmt":"2025-06-03T13:35:48","slug":"artificial-intelligence-in-manufacturing-revolution-or-mere-evolution","status":"publish","type":"post","link":"https:\/\/www.regestaitalia.eu\/en\/artificial-intelligence-in-manufacturing-revolution-or-mere-evolution\/","title":{"rendered":"Artificial intelligence in manufacturing: revolution or mere evolution?"},"content":{"rendered":"

Artificial intelligence is the most widely discussed<\/strong> and central topic<\/strong> in the field of innovation and digital transformation in recent years, so pervasive that it is impossible not to mention it even in sectors where its use is already established<\/strong>. It is, therefore, a matter of better understanding a phenomenon that has jumped into the headlines recently, but whose origins go back further. Artificial intelligence in manufacturing has, in fact, reached a mature level of adoption<\/strong>: why, then, is it being talked about again?<\/p>\n<\/div>

Artificial intelligence in manufacturing: let’s get some clarity<\/h2>\n

The misunderstanding stems from the not entirely appropriate use of terminology. Today, in fact, there is a tendency to use the term \u201cArtificial Intelligence\u201d to refer to<\/strong> a more specific field of application, namely Generative Artificial Intelligence or Gen AI.<\/strong> <\/p>\n

Actually, \u201cArtificial Intelligence\u201d<\/strong> should be an umbrella term that also includes other application areas such as machine learning, deep learning, machine vision, and so on.<\/strong><\/p>\n<\/div><\/div><\/div>

<\/span><\/div><\/div><\/div><\/div>

From this perspective, the marriage of AI and manufacturing goes back at least to the definition of Industry 4.0\u00b9 in the early 2010s, which identified this very combination as one of the founding principles. In this context, we refer primarily to uses related to data analysis and process optimization.<\/strong> The use of artificial intelligence in business processes in industry is, therefore, well-established and in many cases transparent to end users and operators. <\/p>\n

There is, of course, no shortage of innovations and improvements, but the peak phase of innovation has given way to proven<\/strong>, established applications<\/strong> in which a pragmatic approach prevails. We recall the main ones below. <\/p>\n

On the other hand, Gen AI is also gaining ground in industry and manufacturing.<\/strong> Again, a limitation of commonly used definitions must be considered: the best-known conversational models do not represent the full potential of generative models. In this sense, innovative and particularly interesting uses of generative AI<\/strong> are emerging .<\/strong> <\/p>\n

Artificial intelligence in manufacturing: established application technologies<\/h2>\n

As we have mentioned, the use of artificial intelligence in manufacturing and industry already has numerous universally available application fields. We recall here the main ones. <\/p>\n

Collaborative robotics<\/h3>\n

Cobots support operators in performing complex or burdensome tasks. They are machines that, thanks to artificial intelligence, can learn, adapt and work safely alongside humans, improving efficiency and safety. <\/p>\n

Predictive maintenance<\/h3>\n

Advanced data analysis enables the prediction of failures and anomalies and reduces the cost of planned maintenance. Predictive maintainance makes better use of resources allocated to maintenance and improves overall plant safety. <\/p>\n

Digital Twin<\/h3>\n

Digital twins virtually replicate machinery and plants to perform simulations and optimizations without physical intervention. AI enables in-depth analytics that empower every aspect of business management, from predictive maintenance to operations management. <\/p>\n

Quality control<\/h3>\n

Tools such as AI-based computer vision detect defects and anomalies with greater accuracy than the human eye. This makes it possible to prevent production errors, increase product quality and support operators. <\/p>\n

Demand forecasting<\/h3>\n

Machine learning algorithms analyze historical data to predict market trends. This capability improves production planning and optimizes the supply chain through the elimination of bottlenecks and inefficiencies. <\/p>\n

Logistics and inventory<\/h3>\n

AI optimizes both procurement aspects by being able to predict specific needs and manage inventories dynamically. In this way, companies can avoid disruptions in production or problems with stocks or supplies. <\/p>\n

The potential of Gen AI in manufacturing<\/h2>\n

In contrast, generative artificial intelligence is now beginning to make its way into the manufacturing context, with applications that can have considerable impacts, at different points in the supply chain. An interesting insight from McKinsey\u00b2 summarizes them comprehensively. We reproduce here an excerpt of those that are particularly significant, beyond such well-known topics as analytics<\/strong>, where the potential is already widely known and expressed, document management, and enterprise knowledge organization<\/strong>, also fields widely discussed elsewhere. <\/p>\n