Abstract
In view of the limitations of the linear model used in the traditional supply chain solution method, it cannot adapt to the dynamic supply chain network solution method in the multi-source big data environment of the Internet, and maps the dynamic supply chain into a network diagram model, and proposes e-commerce. Supply chain network model, based on this model, presents a semi-instance pattern detection method based on collaborative matrix decomposition, which is used to detect a semi-instantiated collaborative behaviour pattern in the supply chain network. According to the given collaborative behaviour model, the collaborative supply matrix decomposition method is first used to calculate the candidate supply chain of the personalized supply chain, and the degree of intimacy between node entities in the supply chain network is calculated. Using the A* graph search algorithm, a supply chain result candidate chain set is generated based on a personalized supply chain candidate set. According to personalized time, cost and other constraints, the final supply chain solution is tailored. The correctness, efficiency and accuracy of the method were verified by the e-commerce supply chain data set for apparel.
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References
Wang TK, Zhang Q, Chong HY, Wang XY (2017) Integrated supplier selection framework in a resilient construction supply chain: an approach via analytic hierarchy process (AHP) and grey relational analysis (GRA). Sustainability 9(2):289
Vahidi F, Ali Torabi S, Ramezankhani MJ (2018) Sustainable supplier selection and order allocation under operational and disruption risks. J Clean Prod 174:1351–1365
Yazdani M, Chatterjee P, Zavadskas EK, Zolfani SH (2017) Integrated QFD–MCDM framework for green supplier selection. J Clean Prod 142(4):3728–3740
Jain V, Sangaiah AK, Sakhuja S (2018) Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Comput Appl 29(7):555–564
Babbar C, Amin SH (2018) A multi-objective mathematical model integrating environmental concerns for supplier selection and order allocation based on fuzzy QFD in beverages industry. Expert Syst Appl 92:27–38
Rajesh R, Ravi V (2015) Supplier selection in resilient supply chains: a grey relational analysis approach. J Clean Prod 86:343–359
Liu T, Deng Y, Chan F (2018) Evidential supplier selection based on DEMATEL and game theory. Int J Fuzzy Syst 20(4):1321–1333
Yin S, Li BZ, Dong HM, Xing ZY (2017) A new dynamic multicriteria decision-making approach for green supplier selection in construction projects under time sequence. Math Probl Eng. https://doi.org/10.1155/2017/7954784
Kar AK (2015) A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network. J Comput Sci 6:23–33
Koirala P (2012) What is big data analytics and its application in E-commerce? http://blog.venturesity.com/what-is-big-data-analytics-and-its-application-in-e-commerce
Akter S, Wamba SF (2016) Big data analytics in E-commerce: a systematic review and agenda for future research. Electron Mark 26(2):173–194
Leng K, Shi W, Chen J, Lv Z (2015) Design of an I-shaped less-than-truckload cross-dock: a simulation experiment study. Int J Bifurc Chaos 25(14):1152–1165
Hofacker FC, Malthouse EC, Sultan F (2016) Big data and consumer behaviour: imminent opportunities. J Consum Mark 33(2):89–97
Chen CL, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf Sci 275:314–347
Govindan K, Cheng TCE, Mishra N, Shukla N (2018) Big data analytics and application for logistics and supply chain management. Transp Res Part E Logist Transp Rev. https://doi.org/10.1016/j.tre.2018.03.011
Hofmann E (2017) Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect. Int J Prod Res 55(17):5108–5126
Li XS, Tian YJ, Florentin S, Rajan A (2015) An extension collaborative innovation model in the context of big data. Int J Inf Technol Decis Mak 14(1):69–91
Zhong RY, Chen X, Chen C, Huang Q (2015) Big data analytics for physical internet-based intelligent manufacturing shop floors. Int J Prod Res 55(9):2610–2621
Chen Q, Preston DS, Swink M (2016) How the use of big data analytics affects value creation in supply chain management. J Manag Inf Syst 32(4):4–39
Schoenherr T, Speier-Pero C (2015) Data science, predictive analytics, and big data in supply chain management: current state and future potential. J Bus Logist 36(1):120–132
Ivanov D, Sokolov B, Raguinia EAD (2014) Integrated dynamic scheduling of material flows and distributed information services in collaborative cyber-physical supply networks. Int J Syst Sci Oper Logist 1(1):18–26
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The authors acknowledge the National Natural Science Foundation of China (Grant: 71402048) and Hubei society of social sciences (Grant: 2016101).
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Leng, K., Jing, L., Lin, IC. et al. Research on mining collaborative behaviour patterns of dynamic supply chain network from the perspective of big data. Neural Comput & Applic 31 (Suppl 1), 113–121 (2019). https://doi.org/10.1007/s00521-018-3666-z
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DOI: https://doi.org/10.1007/s00521-018-3666-z