Selection Of The Hotel Suppliers Under High-Level Uncertainty

Abstract

The objective of this paper is to develop a model for supplier selection under high-level uncertainty in the decision-making environment. The pandemic seriously affected the economic well-being of the hospitality industry, decreased travel and tourists` numbers, undermined hospitality service delivery systems and their financial stability. Business structures and relationships, developed in the industry during several decades of stability, have been destroyed fully or partially, and service quality is deteriorating. One of the consequences of the pandemic is supply chains disruptions, caused by the inabilities of suppliers to provide services according to customer requirements. Given that, it is necessary to solve the problem of supplier selection for the tourism sector taking into consideration specifics of the pandemic and post-pandemic conditions. During pandemic and post-pandemic recovery, internal and external environments of the business tasks are characterized by the high-level of uncertainty, insufficiency, and subjectivity of the available information. Supply chain management task is a classic example of such tasks, and it is necessary to develop an approach that can operate with uncertainties and subjectivity of various nature, inherent to this decision-making problem. In such circumstances, traditional probabilistic or fuzzy methods may not always be relevant for formalizing uncertainties, and the use of perception-based dual-natured (fuzzy & probabilistic) Z-numbers may be more appropriate. Z-number-based multi-criteria decision-making (MCDM) method ZVIKOR was used to select alternatives (suppliers) for the hotels. The criteria for the supplier selection were determined by Delphi analysis. The supplier selection task is solved on the example of hotels in Turkey and Azerbaijan. Results of the research illustrate the applicability of the approach for solving MCDM problems in the tourism sector under conditions of highlevel uncertainty.