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Complexity reduction in engineer-to-order industry through real-time capable production planning and control

  • Production Management
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Abstract

The engineer-to-order industry is under constant pressure to optimise production and handle complexity in the delivery of the right components at the right time. In many cases, e.g. in the building industry, they have to install their components at the construction site. Synchronisation between fabrication and on-site installation is difficult to realise with traditional planning techniques and instruments. The purpose of this study is to outline the potential of real-time-capable production planning and control in engineer-to-order companies as a successful approach to minimise time-dependent combinatorial complexity in the value chain. This research is based on axiomatic design theory in order to explain and confirm the hypothesis of complexity reduction through a near real-time feedback request at the installation site. We have demonstrated this through the information axiom of axiomatic design which states that complexity can be reduced to a minimum through a digitally automated continuous (re-)planning in order to avoid the system range shifting outside of the design range. Thus, the research team has described the first results of an industrial case study to develop a digital software tool to overcome this limitation. Our research contributes to complexity management in engineer-to-order manufacturing companies and further provides future direction towards digitalisation.

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Authors and Affiliations

  1. Faculty of Science and Technology, Free University of Bozen-Bolzano, Universitätsplatz 5, 39100, Bolzano, Italy

    Erwin Rauch, Patrick Dallasega & Dominik T. Matt

  2. Fraunhofer Italia Research s.c.a.r.l., Innovation Engineering Center (IEC), via Macello 57, Bolzano, 39100, Italy

    Dominik T. Matt

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  1. Erwin Rauch
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  2. Patrick Dallasega
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Correspondence to Erwin Rauch.

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Rauch, E., Dallasega, P. & Matt, D.T. Complexity reduction in engineer-to-order industry through real-time capable production planning and control. Prod. Eng. Res. Devel. 12, 341–352 (2018). https://doi.org/10.1007/s11740-018-0809-0

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