Improved efficiency assessment in network DEA through interval data analysis: An empirical study in agriculture

Abstract

Conventional Network Data Envelopment Analysis (NDEA) models often make an assumption of data precision, which frequently does not align with the realities of many real-world scenarios. When dealing with ambiguous data, whether it involves input, output, or intermediate products represented as bounded or ordinal data, the accurate assessment of efficiency scores poses a significant challenge. This study addresses the crucial issue of handling interval data within NDEA structures by introducing an innovative methodology that integrates both optimistic and pessimistic strategies. Our proposed methodology goes beyond the mere determination of upper and lower bounds for efficiency scores; it also incorporates target-setting and improvement approaches. Through the calculation of interval efficiency for each decision-making unit (DMU), our approach offers a comprehensive framework for efficiency classification. To underscore the effectiveness of this methodology, the study presents empirical evidence through a case study in the agriculture industry. The results not only showcase the advantages of our proposed methodology but also emphasize its potential for practical application in diverse and complex real-world contexts.