Design of manufacturing enterprise FEW system based on ML from the perspective of circular economy

Yang Huang, Xinyu Li and Dan Li

International Journal of Critical Infrastructures, 2025, vol. 21, issue 9, 1-20

Abstract: This study designs a financial early warning system for manufacturing enterprises, focusing on machine learning and the circular economy. The random forest model is used as the base model, optimised by the artificial jellyfish algorithm to enhance prediction accuracy. Financial and non-financial indicators are selected through significance testing and feature screening methods. The results show that the optimised model achieves the highest accuracy of 88.42% and AUC of 0.918. Key warning indicators include inventory turnover rate, accounts receivable turnover rate, Herfindahl index, and liquidity ratio. The study highlights the importance of timely warnings for maintaining financial stability in manufacturing enterprises, helping them manage financial crises and supporting sustainable growth. The proposed system provides valuable support for policymakers and industry leaders in managing financial risks and advancing circular economy goals.

Keywords: circular economy; CE; manufacturing enterprises; financial early warning; FEW; random forest; RF; artificial jellyfish algorithm. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=148646 (text/html)
Open Access

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcist:v:21:y:2025:i:9:p:1-20

Access Statistics for this article

More articles in International Journal of Critical Infrastructures from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().