Determining supply chain flexibility using statistics and neural networks: a comparative study
The purpose of this paper is to examine the application of neural networks as a flexibility andperformance measure in supplier-manufacturer activities. The dimensions of information exchange,supplier integration, product delivery, logistics, and organisational structure are used as determinantsfacto...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
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Conference Publishing Services
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/9696 |
| _version_ | 1848746024114323456 |
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| author | Jeeva, Ananda Guo, W. |
| author2 | Yang Xiang |
| author_facet | Yang Xiang Jeeva, Ananda Guo, W. |
| author_sort | Jeeva, Ananda |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The purpose of this paper is to examine the application of neural networks as a flexibility andperformance measure in supplier-manufacturer activities. The dimensions of information exchange,supplier integration, product delivery, logistics, and organisational structure are used as determinantsfactors affecting this supply chain flexibility. The data set was collected from more than 200 Australianmanufacturing firms evaluating their suppliers. Our study shows that neural networks can accuratelydetermine a supplier's flexibility with an error within 1%, which is more accurate than the conventional multivariate regression can. |
| first_indexed | 2025-11-14T06:26:40Z |
| format | Conference Paper |
| id | curtin-20.500.11937-9696 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:26:40Z |
| publishDate | 2009 |
| publisher | Conference Publishing Services |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-96962022-12-09T05:23:40Z Determining supply chain flexibility using statistics and neural networks: a comparative study Jeeva, Ananda Guo, W. Yang Xiang Javier Lopez Haining Wang neural network supply chain multivariate regression flexibility The purpose of this paper is to examine the application of neural networks as a flexibility andperformance measure in supplier-manufacturer activities. The dimensions of information exchange,supplier integration, product delivery, logistics, and organisational structure are used as determinantsfactors affecting this supply chain flexibility. The data set was collected from more than 200 Australianmanufacturing firms evaluating their suppliers. Our study shows that neural networks can accuratelydetermine a supplier's flexibility with an error within 1%, which is more accurate than the conventional multivariate regression can. 2009 Conference Paper http://hdl.handle.net/20.500.11937/9696 10.1109/NSS.2009.87 Conference Publishing Services fulltext |
| spellingShingle | neural network supply chain multivariate regression flexibility Jeeva, Ananda Guo, W. Determining supply chain flexibility using statistics and neural networks: a comparative study |
| title | Determining supply chain flexibility using statistics and neural networks: a comparative study |
| title_full | Determining supply chain flexibility using statistics and neural networks: a comparative study |
| title_fullStr | Determining supply chain flexibility using statistics and neural networks: a comparative study |
| title_full_unstemmed | Determining supply chain flexibility using statistics and neural networks: a comparative study |
| title_short | Determining supply chain flexibility using statistics and neural networks: a comparative study |
| title_sort | determining supply chain flexibility using statistics and neural networks: a comparative study |
| topic | neural network supply chain multivariate regression flexibility |
| url | http://hdl.handle.net/20.500.11937/9696 |