In recent years, efforts have been made towards the integration of smart digital technologies into traditional industrial processes, allowing for the collection of vast amounts of data across all aspects of the manufacturing cycle, in what has been referred to as the “Fourth Industrial Revolution”, or “Industry 4.0”. Machine learning tools, particularly neural networks, have been playing an increasingly relevant role within this context, thanks to their ability to handle complex multidimensional data, uncover hidden relationships extrapolating patterns, predict the future evolution of the process and thus aid decision-making, ultimately improving efficiency. In this work, we aim to showcase how a next-generation programmable logic controller like the Finder Opta - which integrates traditional PLC features with a powerful processor, several connectivity options and access to the Arduino language and ecosystem - enables breakthrough applications within the “Industry 4.0” framework, even supporting small neural networks to handle on-device classification or regression tasks.