The requirements for manufacturing systems that can manufacture individualized products are demanding. In addition to a large throughput while simultaneously supporting unique manufacturing processes for each product, high product quality, compactness, energy efficiency, expandability, and cyber resilience are essential. This paper examines two demonstrators that utilize an industrial PC-based control with incorporated Machine Learning (ML) and a flexible product transport system at the centre of the corresponding manufacturing system. The use of a multi-core processor demonstrates efficient exploitation of synergies, potentially extending the manufacturing system’s lifecycle. Both demonstrators incorporate neural network models to control the flow of individualized products. The inference time for image classification is less than 500 µs with a classification accuracy of 100% while the inference time for transport system is less than 250 µs.