A robust-stochastic data envelopment analysis model for supplier performance evaluation of the telecommunication industry under uncertainty

Abstract

The primary activities of any organization rely on the procurement of the required goods and services at the shortest time and highest quality possible. On this basis, the problem of supplier evaluation, ranking, and selection is considered critically important. Data envelopment analysis is a well-known and successful approach in this field. In this study, we propose a robust-stochastic data envelopment analysis model to measure the efficiency of decision-making units under uncertainty. We measure efficiency through a standard and an inverted model in terms of resilience and agility. In order to demonstrate the practical potential of the proposed model, we apply the model to a case study of the Iranian telecom industry with 90 decision-making units. Numerical results reveal that human resources and cash assets are the most important input criteria. Also, the output indicators, including adaptability, reliability, visibility, and coordination, have high importance in measuring the efficiency of decision-making units. It should be noted that employing the robust-stochastic optimization approach leads to controlling the fluctuations of uncertain parameters and maintaining a desirable optimal level of efficiency for decision-making units under different scenarios. The results suggest that the model is sufficiently valid and reliable for evaluating the performance of suppliers in the telecom industry, may be employed under uncertain conditions, and can incorporate decision-makers' varying preferences. The managerial insights derived from this research indicate that, in the short term, uncertainty throughout the evaluation process of suppliers often leads to reduced efficiency among the decision-making units. However, operating under uncertainty is associated with several advantages in the long term, such as increased decision-making consistency and improved vital ability to cope with uncertainty.