Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing
- Title
- Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing
- Creator
- Seth H.; Tripathi D.K.; Chadha S.; Tripathi A.
- Description
- Purpose: 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). Design/methodology/approach: A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME. Findings: Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME. Originality/value: The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively. 2024, Emerald Publishing Limited.
- Source
- Journal of Modelling in Management
- Date
- 2024-01-01
- Publisher
- Emerald Publishing
- Subject
- Data envelopment analysis; India; Manufacturing; Neural networks; Sensitivity analysis; Slack-based measure; Working capital management
- Coverage
- Seth H., Indian Institute of Management, Rohtak, India; Tripathi D.K., Department of Management, Birla Institute of Technology and Science Pilani, Pilani Campus, Pilani, India; Chadha S., Department of Management, Birla Institute of Technology and Science Pilani, Pilani Campus, Pilani, India; Tripathi A., Department of Professional Studies, CHRIST University, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 17465664
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Seth H.; Tripathi D.K.; Chadha S.; Tripathi A., “Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/13521.