Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing

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dc.contributor.authorChadha, Saurabh
dc.date.accessioned2025-02-19T09:22:25Z
dc.date.available2025-02-19T09:22:25Z
dc.date.issued2024-09
dc.description.abstractThis 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.identifier.urihttps://www.emerald.com/insight/content/doi/10.1108/jm2-07-2024-0210/full/html
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/17897
dc.language.isoenen_US
dc.publisherEmeralden_US
dc.subjectManagementen_US
dc.subjectWorking capital managementen_US
dc.subjectData envelopment analysis (DEA)en_US
dc.subjectNeural networksen_US
dc.subjectManufacturingen_US
dc.subjectSensitivity analysisen_US
dc.titleModelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturingen_US
dc.typeArticleen_US

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