Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2023 International Conference on Management Research and Economic Development
Series Vol. 23 , 13 September 2023
Open
Access | Article
Supply Chain Management Optimization Based on Bigdata Analysis
* Author to whom correspondence should be addressed.
Abstract
Contemporarily, in the context of big data, enterprises need to continuously optimize and innovate the supply chain, change the traditional supply chain management process pattern as well as optimization the whole supply processes. These actions are able to strengthen the actual effect level of supply chain process operation and achieve more benefits for the enterprise. Under the background of big data, more information management technologies are widely used, and supply chain management needs to clarify the deficiencies in its own operation, and reengineer and adjust the deficiencies. This paper will systematically discusses the implementation of the state-of-art bigdata analysis techniques in SCM. According to the analysis and evaluations of the measures, the usage of advanced big data technology will help increase the cultivation and establishment of management talent resources, so as to optimize the supply chain management process. Overall, these results shed light on guiding further exploration of supply chain management optimization.
Keywords
bigdata, supply chain, smart logistics
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2023 International Conference on Management Research and Economic Development
- ISBN (Print)
- 978-1-915371-89-8
- ISBN (Online)
- 978-1-915371-90-4
- Published Date
- 13 September 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/23/20230359
- Copyright
- 13 September 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited