Autor: Fazel Ansari

The engineer-to-order (ETO) production approach is gaining relevance in response to increasing demand for individualized products and small batch sizes. However, ETO inherently reduces the economies of scale typically achieved in series production, as each order requires tailored engineering and production steps. This loss of efficiency can be mitigated through demand-driven and context-aware information provision throughout the product development process. A recommendation system based on semantic artificial intelligence (AI) and machine learning can support this by i) analyzing historical data and prior knowledge, for example drawings or a bill of materials from previous projects, and ii) making automated suggestions, like reusing existing designs or proposing design alternatives, thus compensating for the aforementioned effects.

The increasing skill mismatch in manufacturing workforce is raising demand for training opportunities to cope with advanced manufacturing systems. To maintain production and adjust quickly to technological transformation, innovative work-based learning approaches are emphasized. Intelligent machines are becoming capable of interaction and collaboration with humans. They not only introduce a new type of learnable workforce to manufacturing but may open opportunities to enhance learning of all learners. Symbiotic relationships of humans and machines have wide potential for human-centric manufacturing (aka Industry 5.0). Moreover, connecting smart devices and deploying self-learning solutions is envisioned to increase flexibility of manufacturing, thus changing work division between humans and machines. Where humans and machines collaborate, the term Reciprocal Learning has been coined to describe the process of bidirectional learning. While a definition of Reciprocal Learning exists the ...