Supplier assessment plays a pivotal role in various sectors and is a key component of supply chain management (SCM); however, it combines complexities and challenges associated with uncertainty and dynamic requirements. Several studies have applied various methods to assess suppliers; however, their calculations are complex. Therefore, this work introduces a new hybrid method, depending on the full consistency method (FUCOM) with triangular fuzzy numbers (TrFNs) to consistently prioritize supplier assessment criteria, using the distance from the ideal assessment criterion to assess various suppliers, as well as to handle uncertainty. The proposed method is applied to a practical case study, and the results show that it prioritizes the assessment criteria, reflecting stakeholder preferences. Moreover, it enables decision makers to construct a set of decision matrices and take into account various viewpoints, and it uses simple, detailed steps with TOPSIS, thus avoiding confusion. Additionally, the results indicate that the use of the FUCOM-TrFNs provides consistent weights and a robust tool for imprecise problems, with fewer comparisons and less confusion during calculations. Ultimately, the findings provide valuable insights for assessing and selecting suitable suppliers with a more applicable method.

The growth in data volume and complexity of decision-making create the need for new data analytics methods, such as machine learning (ML) and deep learning