Time Lag Effect of Supply Chain Management on Firm Performance

Xiao, Lu; Zhu, Quan; Xu, Xiaolin; Wäger, Christoph

doi:10.1007/s11518-021-5489-1

J. Syst. Sci. Syst. Eng.

2021

DOI: 10.1007/s11518-021-5489-1

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Time Lag Effect of Supply Chain Management on Firm Performance

Lu Xiao

Quan Zhu

Xiaolin Xu

et al.

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Cited by 4 publications

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“…( 6) BPNN: this model is established through sample data acquisition and repeatedly trained via the self-learning characteristics to correct the weight and reduce the model error. Then, the connection weight of each layer of the network is analyzed, and a conclusion is drawn [15,16]. Ricardo Saavedra et al believed that SCM could systematically coordinate the relationship between departments within the enterprise and related enterprises and improve the work efficiency of departments and economic benefits of enterprises [17].…”

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Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

2022

Wireless Communications and Mobile Computing

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The present work is aimed at solving the investment risks in the supply chain management (SCM) process of enterprises. Therefore, the Back Propagation Neural Network (BPNN) algorithm, logistic regression analysis, and other related theories are used for the risk prediction analysis of supply chain samples. Firstly, 40 pieces of supply chain training data are collected as research samples. Secondly, the examples are trained using the BPNN algorithm. Meanwhile, a logistic regression model is constructed based on Principal Component Analysis (PCA). Finally, the two models conduct risk prediction on the test samples. The results indicate that the BPNN model can effectively predict various risks in the SCM process. It achieves an excellent evaluation effect of single risks, consistent with the actual results. Still, there are some deviations between the prediction results and the actual results of mixed risks. When the significance P value is more than 0.5, the sample is predicted to be of high risk. When it is less than 0.5, the sample is predicted to be of low risk. The prediction accuracy of the logistic regression model is as high as 92.8%, demonstrating brilliant applicability and popularization in the investment risk prediction of the supply chain. The BPNN algorithm and logistic regression model can precisely predict the investment risk in SCM and provide a reference for the improved SCM and the sustainable and stable development of enterprises in the supply chain.

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confidence: 99%

Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

2022

Wireless Communications and Mobile Computing

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Changes in supply chain relationships and the enterprise internationalization process

Mao,

Yang

2024

Research in International Business and Finance

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Determinants of SOE’s performance: A systematic literature review

Mauludina

Azis

Sukmadilaga

et al. 2023

Cogent Business & Management

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Time Lag Effect of Supply Chain Management on Firm Performance

Cited by 4 publications

References 89 publications

Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

Changes in supply chain relationships and the enterprise internationalization process

Determinants of SOE’s performance: A systematic literature review

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