Supplier Selection in Highly Uncertain Environments: The Fuzzy Stratified Best-Worst Method

Abstract

The literature suggests the application of multi-criteria decision-making (MCDM) methods for supplier selection. MCDM methods commonly require decision makers to assign weightings of importance to the decision-making criteria based on which the available suppliers are ranked. Such rankings, however, may not sufficiently support the supplier selection process. This is because the selected supplier will be used for a relatively long period of time. The decision criteria, therefore, need to have weightings that take into consideration uncertainty that is related to the future in which the supplier will still be used (especially in the current challenging world after COVID-19). This research gap is important because the current literature may guide authorities to select a supplier which may suit their current situation but may not be the best option in the near future. This may result in the need to change their supplier in the near future or tolerate the consequences of a wrong selection. To address this gap, this paper suggests the application of an MCDM technique, namely the stratified MCDM (SMCDM) in combination with fuzzy logic and the best-worst method (BWM). Therefore, while this paper addresses the supplier selection problem in an uncertain environment, it also proposes a novel decision-making framework to support decision making in uncertain environments.