Rough Data Envelopment Analysis: An Application to Indian Agriculture

In an uncertain world, nothing is definite. Measuring a person’s effectiveness in such a volatile world is inevitable. For a traditional data envelopment analysis (DEA) approach for precisely evaluating the relative efficacy of homogenous decision-making units (DMUs), precise input and output quantity data are required. However, it’s possible that precise knowledge of the data won’t be available in many real-world applications. This kind of situation can be handled using rough set theory. In order to quantify uncertainty, this work attempts to build a rough DEA model by merging traditional DEA with rough set theory with optimistic and pessimistic confidence levels of rough variables. In the proposed method, a unified production frontier is created for all DMUs using the same set of restrictions, allowing one to precisely evaluates each DMU’s efficiency in the presence of rough data. Additionally, a ranking system based on the approaches put out is offered. The paper examines the effectiveness of the Indian fertiliser supply chain for over a decade in the face of uncertain circumstances. The results of the suggested models are contrasted with those of the current DEA models to show how policymakers might improve the performance of the Indian fertiliser industry’ supply chains. (An earlier version of this study was published in Arya, A., & Hatami-Marbini, A. (2023). A new efficiency evaluation approach with rough data: An application to Indian fertilizer. Journal of Industrial and Management Optimization, 19(7), 5183–5208).