Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009, Revised Papers, Part 2

Research Article

Fuzzy Entropy Method for Quantifying Supply Chain Networks Complexity

Download
386 downloads
  • @INPROCEEDINGS{10.1007/978-3-642-02469-6_47,
        author={Jihui Zhang and Junqin Xu},
        title={Fuzzy Entropy Method for Quantifying Supply Chain Networks Complexity},
        proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009, Revised Papers, Part 2},
        proceedings_a={COMPLEX PART 2},
        year={2012},
        month={5},
        keywords={supply network complexity complex network network analysis entropy fuzzy variable},
        doi={10.1007/978-3-642-02469-6_47}
    }
    
  • Jihui Zhang
    Junqin Xu
    Year: 2012
    Fuzzy Entropy Method for Quantifying Supply Chain Networks Complexity
    COMPLEX PART 2
    Springer
    DOI: 10.1007/978-3-642-02469-6_47
Jihui Zhang1,*, Junqin Xu1,*
  • 1: Qingdao University
*Contact email: zhangjihui@qdu.edu.cn, xujunqin@qdu.edu.cn

Abstract

Supply chain is a special kind of complex network. Its complexity and uncertainty makes it very difficult to control and manage. Supply chains are faced with a rising complexity of products, structures, and processes. Because of the strong link between a supply chain’s complexity and its efficiency the supply chain complexity management becomes a major challenge of today’s business management. The aim of this paper is to quantify the complexity and organization level of an industrial network working towards the development of a ‘Supply Chain Network Analysis’ (SCNA). By measuring flows of goods and interaction costs between different sectors of activity within the supply chain borders, a network of flows is built and successively investigated by network analysis. The result of this study shows that our approach can provide an interesting conceptual perspective in which the modern supply network can be framed, and that network analysis can handle these issues in practice.