The impact of supply chain management on a company’s operation and decision based on the multidimensional data analysis of upstream and downstream industry market states
This study provides data analysis support for the entire enterprise procurement management process, thereby improving the management effectiveness of supply chain operations. It analyzes upstream and downstream industry market status data in the supply chain and various primary data in enterprise ma...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-06-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1369.pdf |
| _version_ | 1797803330552987648 |
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| author | Jingwen Ding |
| author_facet | Jingwen Ding |
| author_sort | Jingwen Ding |
| collection | DOAJ |
| description | This study provides data analysis support for the entire enterprise procurement management process, thereby improving the management effectiveness of supply chain operations. It analyzes upstream and downstream industry market status data in the supply chain and various primary data in enterprise management activities. By utilizing the Delphi method to screen and verify multimode market status data indicators, which significantly impact upstream and downstream industries in multiple rounds, 28 types of market status data were selected for analysis. This analysis aimed to investigate the effect of supply chain management on operational decisions within the company. The data reduction method based on adaptive statistics was the most effective in revealing the market status and promoting efficient operation decision-making based on supply chain management. This study also suggests a brand-new technique for measuring supply chain performance based on the Levenberg-Marquardt Back Propagation (LMBP) algorithm, offering a more impartial manner of doing so. The performance evaluation results showed a maximum error level of less than 0.4% when paired with empirical analysis. The proposed optimization model provides strategic guidance for optimizing supply chain management and improving overall performance. |
| first_indexed | 2024-03-13T05:19:14Z |
| format | Article |
| id | doaj.art-7b0ac492bbf94dfa8e8e54d4e6d6c3d3 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-13T05:19:14Z |
| publishDate | 2023-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-7b0ac492bbf94dfa8e8e54d4e6d6c3d32023-06-15T15:05:06ZengPeerJ Inc.PeerJ Computer Science2376-59922023-06-019e136910.7717/peerj-cs.1369The impact of supply chain management on a company’s operation and decision based on the multidimensional data analysis of upstream and downstream industry market statesJingwen Ding0Faculty of Business, Monash University, Melbourne, Victoria, AustraliaThis study provides data analysis support for the entire enterprise procurement management process, thereby improving the management effectiveness of supply chain operations. It analyzes upstream and downstream industry market status data in the supply chain and various primary data in enterprise management activities. By utilizing the Delphi method to screen and verify multimode market status data indicators, which significantly impact upstream and downstream industries in multiple rounds, 28 types of market status data were selected for analysis. This analysis aimed to investigate the effect of supply chain management on operational decisions within the company. The data reduction method based on adaptive statistics was the most effective in revealing the market status and promoting efficient operation decision-making based on supply chain management. This study also suggests a brand-new technique for measuring supply chain performance based on the Levenberg-Marquardt Back Propagation (LMBP) algorithm, offering a more impartial manner of doing so. The performance evaluation results showed a maximum error level of less than 0.4% when paired with empirical analysis. The proposed optimization model provides strategic guidance for optimizing supply chain management and improving overall performance.https://peerj.com/articles/cs-1369.pdfSupply chain managementMarket status of upstream and downstream industriesMultimode joint data analysisLMBP feedback neural networkCompany operation decision |
| spellingShingle | Jingwen Ding The impact of supply chain management on a company’s operation and decision based on the multidimensional data analysis of upstream and downstream industry market states PeerJ Computer Science Supply chain management Market status of upstream and downstream industries Multimode joint data analysis LMBP feedback neural network Company operation decision |
| title | The impact of supply chain management on a company’s operation and decision based on the multidimensional data analysis of upstream and downstream industry market states |
| title_full | The impact of supply chain management on a company’s operation and decision based on the multidimensional data analysis of upstream and downstream industry market states |
| title_fullStr | The impact of supply chain management on a company’s operation and decision based on the multidimensional data analysis of upstream and downstream industry market states |
| title_full_unstemmed | The impact of supply chain management on a company’s operation and decision based on the multidimensional data analysis of upstream and downstream industry market states |
| title_short | The impact of supply chain management on a company’s operation and decision based on the multidimensional data analysis of upstream and downstream industry market states |
| title_sort | impact of supply chain management on a company s operation and decision based on the multidimensional data analysis of upstream and downstream industry market states |
| topic | Supply chain management Market status of upstream and downstream industries Multimode joint data analysis LMBP feedback neural network Company operation decision |
| url | https://peerj.com/articles/cs-1369.pdf |
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