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Publication

Structured Information Processing as Enabler of Versatile, Flexible Manufacturing Concepts

Simon Komesker; Wolfgang Kern; Achim Wagner; Thomas Bauernhansl; Martin Ruskowski
In: Philipp Weißgraeber; Frieder Heieck; Clemens Ackermann (Hrsg.). Advances in Automotive Production Technology – Theory and Application. Stuttgart Conference on Automotive Production (SCAP-2020), November 9-10, Stuttgart/Virtual, Germany, Pages 108-116, ARENA2036, ISBN 978-3-662-62962-8, Springer, Berlin, Heidelberg, 2021.

Abstract

Automotive production systems face the challenge to produce models and brands with different drive concepts and individually configured equipment variants in a highly efficient way. Studies on modular assembly systems in automotive industry have demonstrated potential for productivity gains through the implementation of an alternative, value-add-oriented process organization. The rigid concatenation of mechanical production processes is the limiting factor; firstly, for an efficient implementation of product individualization and secondly, for a highly available robust production which can optimize the overall factory production flow. Rising degrees of freedom in material flow control associated with more flexi-ble production flow increases the complexity of the overall production system. Decision support for humans by planning systems with integrated control logic is thus a decisive factor for mastering complexity. Currently, the overall perfor-mance of modular manufacturing processes is not sufficiently supported by the IT-architecture on factory level. The individually operating subsystems are not capable of supporting reactive manufacturing control. As a basis for reactive manufacturing control, the information requirements towards modular manufacturing processes across different domains are defined in this paper. Furthermore, a cross-system information and communication matrix is proposed that structures information processing between individually operating subsystems. The application of broker-technology could subsequently enable holistic information-based control on factory level to support human experience-based decision making.