Enhancing Efficiency Assessment of Egyptian Pharmaceutical Companies through the Usage of Two-Stage Uncertainty Data Envelopment Analysis Algorithm

Abstract

Evaluating the performance of pharmaceutical manufacturing companies is crucial for their success and competitiveness in the rapidly evolving Egyptian market. However, traditional data envelopment analysis (DEA) approaches often overlook the inherent uncertainty in the data, which can significantly impact efficiency assessments. To address this issue, this paper proposes a novel integrated DEA model to enhance the efficiency assessment of Egyptian pharmaceutical manufacturing companies. This two-stage framework effectively handles imprecise and ambiguous data, accommodating various forms of uncertainty, including fuzzy, stochastic, and neutrosophic data, alongside deterministic data. The results demonstrate that the proposed model outperforms traditional DEA models in capturing data uncertainty and providing more accurate efficiency evaluations.