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International Journal of Frontiers in Sociology, 2023, 5(7); doi: 10.25236/IJFS.2023.050706.

Big Data Evaluation Technology in the Implementation Strategy of Green Supply Chain Management for Manufacturing Enterprises


Xiaoxia Han

Corresponding Author:
Xiaoxia Han

Department of Management and Information, Shandong Transport Vacational College, Weifang, Shandong, 261206, China


Implementing green supply chain, also known as Environmentally Conscious Supply Chain (ECSC) management in enterprises, can not only improve their environmental performance, but also improve their economic performance. The two can achieve a “win-win situation”. Driven by big data, the value behavior of manufacturing enterprises is developing towards digitization and networking, which would provide them with new business models and new economic growth points. In the big data environment, fund management has shifted from ex post control to every process in the process. Big data technology can optimize various business processes such as enterprise research and development and production, green supply chain, and marketing, and innovate business management models to achieve the goal of reducing costs and increasing efficiency. Based on the analysis of transaction costs and the theory of inter organizational cost management, this article regarded a cooperative enterprise in the supply chain as an organization, using big data technology to reduce costs and improve enterprise efficiency. The variance in the big data environment was 0.898, while the traditional green procurement practice variance was 0.955. For the operation mode of manufacturing industry, this article provided a new practical solution for ECSC management of manufacturing enterprises.


Green Supply Chain Management, Big Data, Manufacturing Enterprises, Green Procurement

Cite This Paper

Xiaoxia Han. Big Data Evaluation Technology in the Implementation Strategy of Green Supply Chain Management for Manufacturing Enterprises. International Journal of Frontiers in Sociology (2023), Vol. 5, Issue 7: 32-38. https://doi.org/10.25236/IJFS.2023.050706.


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