| dc.contributor.author |
Chadha, Saurabh |
|
| dc.date.accessioned |
2025-02-19T09:22:25Z |
|
| dc.date.available |
2025-02-19T09:22:25Z |
|
| dc.date.issued |
2024-09 |
|
| dc.identifier.uri |
https://www.emerald.com/insight/content/doi/10.1108/jm2-07-2024-0210/full/html |
|
| dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/17897 |
|
| dc.description.abstract |
This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN). |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
Emerald |
en_US |
| dc.subject |
Management |
en_US |
| dc.subject |
Working capital management |
en_US |
| dc.subject |
Data envelopment analysis (DEA) |
en_US |
| dc.subject |
Neural networks |
en_US |
| dc.subject |
Manufacturing |
en_US |
| dc.subject |
Sensitivity analysis |
en_US |
| dc.title |
Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing |
en_US |
| dc.type |
Article |
en_US |