Structure Characteristics Analysis of Diesel Sales in Complex Network Method
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With the rising profits of oil companies in the refined oil sector, the optimization of the refined oil supply chain network has received more and more attention. In most supply chains (SCs), transaction relationships between suppliers and customers are commonly considered to be an extrapolation from a linear perspective. However, this traditional linear concept of an SC is egotistic and oversimplified and does not sufficiently reflect the complex and structure of supplier–customer relationships in current economic and industrial situations. But key global knowledge can be obtained from complex network characteristics analysis of the net form sales system. For two-level network like refine oil supply network, this paper proposed an integrated framework to explore its characteristics. Through various analyses of this complex network, including visual, network scale, network agglomeration, network community and geographic information analyses, we could found the characteristics of regular network node relations and regional location characteristics, as well as a strongly correlation between correlation coefficient thresholds and the network interdependence, and also moderated the correlation between SN efficiency and SN resilience. In order to testify this supply network analysis method, we conducted a real-world SN analyses based on a Chinese province diesel supply network and describe an advanced investigation of SN theory. This method enrich the SN theory, which can benefit SN management, community economics and industrial resilience. Also the basic understanding of the diesel sales network system obtained in this paper provides guidance for further research on this network structure, which can also provide a reference for regional sales supervision and resource distribution.
KeywordsDiesel Sales Complex network Supply network Structure characteristics
This research is supported by grants from the Natural Science Foundation of China (Grant No. 71173199). The authors would like to express their gratitude to An Haizhong who provided valuable suggestions.
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