Software-based Identification of Adaptation Needs in Global Production Networks

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Schuh, G.; Gützlaff, A.; Schlosser, T.X.; Rodemann, N.; Haak, N.: Software-based Identification of Adaptation Needs in Global Production Networks. In: Herberger, D.; Hübner, M. (Eds.): Proceedings of the Conference on Production Systems and Logistics: CPSL 2022. Hannover : publish-Ing., 2022, S. 622-631. DOI: https://doi.org/10.15488/12169

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Sum total of downloads: 122




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Abstract: 
Internal and external influencing factors force companies to adapt their production networks to changing conditions, which entails a high level of complexity. To be competitive in the future, manufacturing companies have to minimize the required adaptation time between the occurrence of a change and the implementation of an adaptation. While some approaches deal with modelling and evaluating network configuration, there is a lack in identifying the need for adaptation. In practice, the creation of scenarios is often based on the experience and knowledge of the network designer. This paper presents an approach to systematically link perceived key figure changes to possible adaptation alternatives in network configuration. For this purpose, the relevant objects for network adaptations are first defined and adaptation alternatives are systematically described. Subsequently, these are combined with a set of key figures to derive suitable adaptation alternatives depending on their development. The approach is further implemented in a software-based prototype that enables the automated generation of adaptation alternatives in response to perceived changes and provides the user with a listing of possible alternatives prioritized by their utility. The validation with company data demonstrates that by earlier and automated identification of possible configuration adaptations, the adaptation time to changes can be reduced and the generated scenarios are less dependent on the individual experience of the user.
License of this version: CC BY 3.0 DE
Document Type: BookPart
Publishing status: publishedVersion
Issue Date: 2022
Appears in Collections:Proceedings CPSL 2022
Proceedings CPSL 2022

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 75 61.48%
2 image of flag of United States United States 14 11.48%
3 image of flag of Russian Federation Russian Federation 6 4.92%
4 image of flag of Switzerland Switzerland 5 4.10%
5 image of flag of China China 4 3.28%
6 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 3 2.46%
7 image of flag of India India 3 2.46%
8 image of flag of No geo information available No geo information available 2 1.64%
9 image of flag of Czech Republic Czech Republic 2 1.64%
10 image of flag of Vietnam Vietnam 1 0.82%
    other countries 7 5.74%

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