An inverse data envelopment analysis model to consider ratio data and preferences of decision-makers.

This article focuses on developing a novel inverse Data Envelopment Analysis (DEA) model tailored for working capital management (WCM) with ratio data under the variable returns-to-scale (VRS) assumption. The proposed inverse DEA–VRS ratio model addresses limitations of standard inverse DEA models when outputs include ratio variables, such as EBIT/Total Assets, and can handle negative data through translation invariance. Applied to 212 Indian textile companies using financial data from 2019–2020, the model calculates working capital efficiency (WCE) and determines minimum input targets for increased outputs without compromising efficiency. Results indicate that the ratio inverse DEA model provides more realistic and smaller input targets compared to standard inverse DEA, making it a closer approximation of the production possibility set for ratio data. The study highlights managerial implications for resource allocation and suggests potential extensions to super-efficiency and slack-considering DEA models.