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Manufacturing enterprise collaboration network: An empirical research and evolutionarymodel

With the increasingly fierce market competition, manufacturing enterprises have to continuously improve their competitiveness through their collaboration and labor division with each other, i.e. forming manufacturing enterprise collaborative network (MECN) through their collaboration and labor divis...

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Bibliographic Details
Published in:Chinese physics B 2020-08, Vol.29 (8), p.88901
Main Authors: Hu, Ji-Wei, Gao, Song, Yan, Jun-Wei, Lou, Ping, Yin, Yong
Format: Article
Language:English
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Summary:With the increasingly fierce market competition, manufacturing enterprises have to continuously improve their competitiveness through their collaboration and labor division with each other, i.e. forming manufacturing enterprise collaborative network (MECN) through their collaboration and labor division is an effective guarantee for obtaining competitive advantages. To explore the topology and evolutionary process of MECN, in this paper we investigate an empirical MECN from the viewpoint of complex network theory, and construct an evolutionary model to reproduce the topological properties found in the empirical network. Firstly, large-size empirical data related to the automotive industry are collected to construct an MECN. Topological analysis indicates that the MECN is not a scale-free network, but a small-world network with disassortativity. Small-world property indicates that the enterprises can respond quickly to the market, but disassortativity shows the risk spreading is fast and the coordinated operation is difficult. Then, an evolutionary model based on fitness preferential attachment and entropy-TOPSIS is proposed to capture the features of MECN. Besides, the evolutionary model is compared with a degree-based model in which only node degree is taken into consideration. The simulation results show the proposed evolutionary model can reproduce a number of critical topological properties of empirical MECN, while the degree-based model does not, which validates the effectiveness of the proposed evolutionary model.
ISSN:1674-1056
DOI:10.1088/1674-1056/ab96a8