research-article

Risk assessment for supply chain based on Cloud model

Authors: Shuhao Li, Qiang Sun, Shupei LiuAuthors Info & Claims

Journal of Intelligent & Fuzzy Systems, Volume 41, Issue 2

Pages 3523 - 3540

https://doi.org/10.3233/JIFS-210883

Published: 01 January 2021 Publication History

Abstract

In recent years, supply chain risk management has been followed with interest due to the short life cycle of products. How to identify risk indicators can help evaluate risks on supply chains. Commonly adopted methods such as Fuzzy to determine the level of risks have limitations. In this paper, a framework of supply chain risk evaluation is first proposed and risk indicators are identified by theoretical surveys from 35 keywords and empirical analysis from 448 questionnaires. Moreover, both linguistic risk assessment model and Cloud model are used to evaluate risks of supply chain. The Cloud model evaluation results are between general risk and high risk but closer to high risk. In addition, Cloud expected value of risk is 6.54 which is within the high-risk range, and evaluation results are also high risk. It is shown that when the weights are the same, the cloud model can determine the priority of risk indicators, and reflect volatility and randomness comparing with other evaluation methods.

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Cited By

Zhang ZJiang HShao TShao Q(2023)Understanding the selection of intelligent engineering B2B platform in China through the fuzzy DANP and TOPSIS techniquesApplied Soft Computing10.1016/j.asoc.2023.110277141:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110277

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Risk assessment for supply chain based on Cloud model

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cover image Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology Volume 41, Issue 2

2021

1632 pages

ISSN:1064-1246

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© 2021 – IOS Press. All rights reserved.

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IOS Press

Netherlands

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Published: 01 January 2021

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Cited By

Zhang ZJiang HShao TShao Q(2023)Understanding the selection of intelligent engineering B2B platform in China through the fuzzy DANP and TOPSIS techniquesApplied Soft Computing10.1016/j.asoc.2023.110277141:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110277

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