Supply chain flexibility assessment by multivariate regression and neural networks
This paper compares two vastly different methods of analysis - multiple regression and neural networks, in supply chain flexibility assessment. Data of manufacturing firms evaluating their prominent suppliers were analysed by multiple regression and simulated using three-layer multilayer perceptron...
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| Other Authors: | |
| Format: | Conference Paper |
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Springer-Verlag
2010
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| Online Access: | http://hdl.handle.net/20.500.11937/44399 |
| _version_ | 1848756989586309120 |
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| author | Jeeva, Ananda Guo, W. |
| author2 | Zhigang Zeng |
| author_facet | Zhigang Zeng Jeeva, Ananda Guo, W. |
| author_sort | Jeeva, Ananda |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper compares two vastly different methods of analysis - multiple regression and neural networks, in supply chain flexibility assessment. Data of manufacturing firms evaluating their prominent suppliers were analysed by multiple regression and simulated using three-layer multilayer perceptron (MLP) neural networks. Our study shows that NN can accurately determine a supplier?s flexibility capability within an error of 1% The incorporation of these two methods can lead to better understanding and dynamic prediction of supply chain flexibility for buyers. |
| first_indexed | 2025-11-14T09:20:58Z |
| format | Conference Paper |
| id | curtin-20.500.11937-44399 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:20:58Z |
| publishDate | 2010 |
| publisher | Springer-Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-443992023-01-18T08:46:45Z Supply chain flexibility assessment by multivariate regression and neural networks Jeeva, Ananda Guo, W. Zhigang Zeng Jun Wang supply chain multivariate regression flexibility Neural network This paper compares two vastly different methods of analysis - multiple regression and neural networks, in supply chain flexibility assessment. Data of manufacturing firms evaluating their prominent suppliers were analysed by multiple regression and simulated using three-layer multilayer perceptron (MLP) neural networks. Our study shows that NN can accurately determine a supplier?s flexibility capability within an error of 1% The incorporation of these two methods can lead to better understanding and dynamic prediction of supply chain flexibility for buyers. 2010 Conference Paper http://hdl.handle.net/20.500.11937/44399 10.1007/978-3-642-12990-2 Springer-Verlag fulltext |
| spellingShingle | supply chain multivariate regression flexibility Neural network Jeeva, Ananda Guo, W. Supply chain flexibility assessment by multivariate regression and neural networks |
| title | Supply chain flexibility assessment by multivariate regression and neural networks |
| title_full | Supply chain flexibility assessment by multivariate regression and neural networks |
| title_fullStr | Supply chain flexibility assessment by multivariate regression and neural networks |
| title_full_unstemmed | Supply chain flexibility assessment by multivariate regression and neural networks |
| title_short | Supply chain flexibility assessment by multivariate regression and neural networks |
| title_sort | supply chain flexibility assessment by multivariate regression and neural networks |
| topic | supply chain multivariate regression flexibility Neural network |
| url | http://hdl.handle.net/20.500.11937/44399 |