A hybrid approach using Z-number DEA model and Artificial Neural Network for Resilient supplier Selection

Today's business environment has created a high level of uncertainty and disturbed procedures in supply chains. Suppliers have been often identified as the main source risks creating massive levels disruptions That is why resilient supplier selection can greatly reduce purchase costs time delays create stability practices, thereby increasing competitiveness customer satisfaction. Pharmaceutical companies play an important key role health society, these are frequently exposed to this disorder. Hence, paper tries propose new integrated approach based on traditional (delivery, quality, price, technology level) criteria for pharmaceutical using Z-number data envelopment analysis (Z-DEA) model artificial neural network (ANN). In proposed approach, expert opinions provided Z-numbers due inherent ambiguity evaluation process. This first study that evaluates industry resilience factors by presenting methodological structure under environment. Here, fuzzy mathematical used. A real case utilized indicate applicability industry. Finally, suppliers ranked best selected regarding reliable α. To features capabilities performance presented three parts. First, obtained results compared with DEA (FDEA) method form validation verification. Second, sensitivity executed show effects different ranking results, price index most criteria. Third, predictive ANN able detect efficiency or inefficiency 83% accuracy.