Please use this identifier to cite or link to this item:
http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/17897Full metadata record
| DC Field | Value | Language |
|---|---|---|
| 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 |
| Appears in Collections: | Department of Management | |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.