Business partner selection considering supply-chain centralities and causalities
Business partner selection considering supply-chain centralities and causalities
复制标题
考虑供应链中心性和因果关系选择业务合作伙伴
DOI:
10.1080/16258312.2020.1824531
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发表时间:
2020
期刊:
影响因子:
--
通讯作者:
Sakata Ichiro
中科院分区:
文献类型:
--
作者:
Sasaki Hajime;Sakata Ichiro
While network centricity-based supplier recommendation models can make predictions with high accuracy, they do not sufficiently encompass supply chain causality, and their interpretability is controversial. We propose adding conditional probabilities from the Bayesian network to a supplier predictive model to improve interpretability while maintaining the performance of the social network approach. We construct a supplier forecasting model for 327,012 corporate transactions in the Northeast region of Japan using corporate attributes, network centrality, and conditional probabilities as features and discuss both performance and interpretability. Random forest, support vector machine, and logistic regression were applied as classifiers, and the outputs were compared. The proposed model exceeded an F1 score of 80%, and we found that conditional probabilities have resulted in the highest significance. By incorporating causal features, we were able to construct an accurate and interpretable model. Our findings have implications for companies’ choice of suppliers and for local governments’ consideration of regional industrial policy from a macro perspective.
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影响因子:
3.3
作者:
Nita Yodo;Pingfeng Wang
通讯作者:
Nita Yodo;Pingfeng Wang
DOI:
10.1504/ijbpscm.2010.036167
发表时间:
2010
期刊:
Int. J. Bus. Perform. Supply Chain Model.
影响因子:
--
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发表时间:
2015
期刊:
Supply Chain Forum: An International Journal
影响因子:
--
作者:
S. Sharma;Saurabh Sharma
通讯作者:
Saurabh Sharma
DOI:
10.31387/oscm0170107
发表时间:
2014
期刊:
2005 International Conference on Machine Learning and Cybernetics
影响因子:
--
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通讯作者:
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DOI:
10.1109/icmlc.2005.1527571
发表时间:
2005
期刊:
2005 International Conference on Machine Learning and Cybernetics
影响因子:
--
作者:
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通讯作者:
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