Business partner selection considering supply-chain centralities and causalities

Business partner selection considering supply-chain centralities and causalities
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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:
Supply Chain Forum: An International Journal
影响因子:
--
通讯作者:
Sakata Ichiro
Sakata Ichiro
中科院分区:
--
文献类型:
--
作者:
Sasaki Hajime;Sakata Ichiro

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虽然基于网络中心的供应商推荐模型可以进行高精度预测,但它们并不能充分涵盖供应链因果关系,并且其可解释性存在争议。我们建议将贝叶斯网络的条件概率添加到供应商预测模型中,以提高可解释性,同时保持社交网络方法的性能。我们使用企业属性、网络中心性和条件概率作为特征,为日本东北地区的 327,012 笔企业交易构建了供应商预测模型,并讨论了性能和可解释性。应用随机森林、支持向量机和逻辑回归作为分类器,并对输出进行比较。所提出的模型超过了 80% 的 F1 分数,我们发现条件概率产生了最高的显着性。通过结合因果特征,我们能够构建一个准确且可解释的模型。我们的研究结果对于企业选择供应商以及地方政府从宏观角度考虑区域产业政策具有重要意义。
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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