Purpose: The goal of XPRESS is to establish a breakthrough for the factory of the future with a new flexible production concept based on the generic idea of “specialized intelligent process units” (“Manufactrons”) integrated in cross-sectoral learning networks for a customized production. XPRESS meets the challenge to integrate intelligence and flexibility at the “highest” level of the production control system as well as at the “lowest” level of the singular machine.

Design/methodology/approach: Architecture of a manufactronic networked factory is presented, making it possible to generate particular manufactrons for the specific tasks, based on the automatic analysis of its required features.

Findings: The manufactronic factory concept meets the challenge to integrate intelligence and flexibility at the “highest” level of the production control system as well as at the “lowest” level of the singular machine. The quality assurance system provided a 100% inline quality monitoring, destructive costs reduced 30%-49%, the ramp-up time for the set-up of production lines decreased up to 50% and the changeover time decreased up to 80%.

Research limitations/implications: Specific features of the designed manufactronic architecture, namely the transport manufactrons, have been tested as separate mechanisms which can be merged into the final comprehensive at a later stage.

Practical implications: This concept is demonstrated in the automotive and aeronautics industries, but can be easily transferred to nearly all production processes. Using the manufactronic approach, industrial players will be able to anticipate and to respond to rapidly changing consumer needs, producing high-quality products in adequate quantities while reducing costs.

Originality/value: Assembly units composed of manufactrons can flexibly perform varying types of complex tasks, whereas today this is limited to a few pre-defined tasks. Additionally, radical innovations of the manufactronic networked factory include the knowledge and responsibility segregation and trans-sectoral process learning in specialist knowledge networks.