Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89181
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Title: An interaction investigation of the contributing factors of the bullwhip effect using a bi-level social network analysis approach
Authors: Zhou, H 
Yip, WS 
Ren, J 
To, S 
Issue Date: 2020
Source: IEEE access, 2020, v. 8, p. 208737-208752
Abstract: The bullwhip effect refers to the phenomenon of the increase in demand variability from supply chain downstream members to upstream members. This effect is critically important for the different sectors involved in a supply chain and is one of the main causes of inefficiency, material waste, and low sustainability in supply chain management. In order to help minimize the bullwhip effect and achieve sustainable development in a supply chain, this study identifies the influential factors of the bullwhip effect in a supply chain and the interactive relationships among them. A bi-level bullwhip effect analysis model was established to evaluate the causes of the bullwhip effect at different levels by using a social network analysis approach. Furthermore, to help achieve efficient and sustainable development, the implications of mitigating the impacts of the bullwhip effect in a supply chain were explored based on the results of the inter-relationships analysis among the contributing factors of the bullwhip effect.
Keywords: Bullwhip effect
Fluctuations
Graph theory
Graph theory
Information distortion
Measurement
Social network analysis
Social networking (Online)
Supply chain management
Supply chains
Sustainable development
Sustainable development
Uncertainty
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE access 
EISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.3038680
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
The following publication Zhou, H., Yip, W. S., Ren, J., & To, S. (2020). An interaction investigation of the contributing factors of the bullwhip effect using a bi-level social network analysis approach. IEEE Access, 8, 208737-208752 is available at https://dx.doi.org/10.1109/ACCESS.2020.3038680
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