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Chin. Phys. B, 2017, Vol. 26(11): 110505    DOI: 10.1088/1674-1056/26/11/110505
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Empirical topological investigation of practical supply chains based on complex networks

Hao Liao(廖好)1, Jing Shen(沈婧)1, Xing-Tong Wu(吴兴桐)1, Bo-Kui Chen(陈博奎)2, Mingyang Zhou(周明洋)1
1. National Engineering Laboratory for Big Data Computing Systems, Guangdong Province Key Laboratory of Popular High Performance Computers, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
2. Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417, Singapore
Abstract  

The industrial supply chain networks basically capture the circulation of social resource, dominating the stability and efficiency of the industrial system. In this paper, we provide an empirical study of the topology of smartphone supply chain network. The supply chain network is constructed using open online data. Our experimental results show that the smartphone supply chain network has small-world feature with scale-free degree distribution, in which a few high degree nodes play a key role in the function and can effectively reduce the communication cost. We also detect the community structure to find the basic functional unit. It shows that information communication between nodes is crucial to improve the resource utilization. We should pay attention to the global resource configuration for such electronic production management.

Keywords:  China supply chain networks      complex networks      data science      network science  
Received:  14 June 2017      Revised:  19 July 2017      Published:  05 November 2017
PACS:  05.65.+b (Self-organized systems)  
  05.45.-a (Nonlinear dynamics and chaos)  
  05.10.Gg (Stochastic analysis methods)  
Fund: 

Project supported by the National Natural Science Foundation of China (Grant Nos. 11547040 and 61703281), Guangdong Province Natural Science Foundation, China (Grant Nos. 2016A030310051 and 2015KONCX143), Shenzhen Fundamental Research Foundation, China (Grant Nos. JCYJ20150625101524056 and JCYJ20160520162743717), SZU Student Innovation Fund, China, the PhD Start-up Fund of Natural Science Foundation of Guangdong Province, China (Grant No. 2017A030310374), the Young Teachers Start-up Fund of Natural Science Foundation of Shenzhen University, China, the Natural Science Foundation of SZU, China (Grant No. 2016-24), and the Singapore Ministry of Education Academic Research Fund Tier 2 (Grant No. MOE 2013-T2-2-033).

Corresponding Authors:  Mingyang Zhou     E-mail:  zmy@szu.edu.cn

Cite this article: 

Hao Liao(廖好), Jing Shen(沈婧), Xing-Tong Wu(吴兴桐), Bo-Kui Chen(陈博奎), Mingyang Zhou(周明洋) Empirical topological investigation of practical supply chains based on complex networks 2017 Chin. Phys. B 26 110505

[1] Te Velde D W, Ahmed M M, Alemu G, Bategeka L, Calí M, Castel-Branco C, Chansa F, Dasgupta S, Foresti M, Hangi M and Ingombe L 2010 London:Overseas Development Institute p. 5
[2] Song D W and Lee P T 2009 Maritime Logistics in the Global Supply Chain 83 84
[3] Panayides PM 2006 Maritime Economics& Logistics 8 3
[4] Burgess R 1998 The International Journal of Logistics Management 9 15
[5] Ng A K, Lee P T, Fu X and Sutiwartnarueput K 2014 with selected papers from IAME 2012, IFSPA 2013 and the 2013 Conference on Challenge and Response of Ports in a Globalized Economy 209 211
[6] Lam J S and Bai X 2016 Transportation Research Part E:Logistics and Transportation Review 16 27
[7] Wang T and Cullinane K 2015 InPort Management(London:Palgrave Macmillan) pp. 253-272
[8] Hu S, Li X, Fang Q and Yang Z 2008 International Journal of Information Technology& Decision Making 7 627
[9] Wu L, Chuang CH and Hsu CH 2014 International Journal of Production Economics 112 132
[10] Shore B and Venkatachalam AR 2003 International Journal of Physical Distribution& Logistics Management 33 804
[11] Hu S, Li X, Fang Q and Yang Z 2008 International Journal of Information Technology& Decision Making 7 627
[12] Stadtler H 2015 InSupply Chain Management and Advanced Planning(Berlin Heidelberg:Springer) pp. 3-28
[13] Lazzarini S, Chaddad F and Cook M 2001 Journal on Chain and Net-work Science 1 7
[14] Surana A, Kumara S, Greaves M and Raghavan U N 2005 International Journal of Production Research 43 4235
[15] Hearnshaw E J and Wilson M M 2013 International Journal of Operations& Production Management 33 442
[16] Barabá A L and Albert R 1999 Science 286 509
[17] Ngai E W, Moon K K, Riggins F J and Candace Y Y 2008 International Journal of Production Economics 112 510
[18] Pike R, Dorward S, Griesemer R and Quinlan S 2005 Scientific Programming 13 277
[19] Qiu T, Chen G and Zhang Z K 2017 Chin. Phys. Lett 34 068902
[20] http://120.25.219.225:8765/
[21] Newman M E 2003 SIAM Review 45 167
[22] Lewis T G 2011 Network Science(Hoboken:John Wiley& Sons)
[23] Watts D J and Strogatz S H 1998 Nature 398 440
[24] Mariani M S, Medo M and Zhang Y C 2015 Scientific Reports 5
[25] Zhou M, He X, Fu Z and Zhuo Z 2016 Physica A 442 458
[26] Fortunato S 2010 Physics Reports 486 75
[27] Zhang L, Cao J and Li J 2015 Mathematical Problems in Engineering
[28] Lu J A, Xu M M and Zhou J 2016 Acta Phys. Sin. 65 028902(in Chinese)
[29] Cheng J J, Xiong F and Wang X M 2016 Chin. Phys. B 25 108904
[30] Panayides P M 2006 Maritime Economics& Logistics 8 3
[31] Ahn Y Y, Bagrow J P and Lehmann S 2009 Nature 466 761
[32] Zhang D M,Jiang X Q,Fang P J and He H M 2015 Chin. Phys. Lett. 32 088901
[33] Palla G, Derényi I, Farkas I and Vicsek T 2005 Nature 435 814
[34] Pothen A, Simon H D and Liou K P 1990 SIAM Journal on Matrix Analysis and Applications 11 430
[35] Capocci A, Servedio V D, Caldarelli G and Colaiori F 2005 Physica A 352 669
[36] Wang C, Sun J C and Han F 2016 Chin. Phys. B 25 12
[37] Zhou T, Kuscsik Z, Liu J G, Medo M, Wakeling J R and Zhang Y C 2010 Proc. Natl. Acad. Sci. USA 107 4511
[38] Zhou M Y, Zhuo Z, Cai S M and Fu Z 2014 Chaos 24 033128
[39] Mao R, Zhang P H, Li X L, Liu X and Lu M H 2016 International Journal of Machine Learning and Cybernetics 7 311
[40] Qiu T, Chen G and Zhang Z K 2017 Chin. Phys. Lett. 34 068902
[41] Liao H, Mariani M S, Medo M, Zhang Y C and Zhou M Y 2017 Physics Reports 68 1
[42] Mao R, Cai T T, Li R H, Yu J X and Li J X 2017 World Wide Web 20 621
[43] Liao H, Zeng A, Zhou M Y, Mao R and Wang B H 2017 Communications in Nonlinear Science and Numerical Simulation 251 115
[44] Zheng A, Luo S and Xia H 2016 International Journal of Modern Physics C 27 1650002
[45] Wang T, He X S, Zhou M Y and Fu Z Q 2017 Scientific Reports 7
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