It is important for companies to select the right suppliers to deal with in order to avoid any disruptions. Considerable research has been conducted to find the solution for this problem. Previous attempts were made to account for uncertainties and disruptions in decision-making in order to rank suppliers based on different criteria of interest and select the desired resources as needed. This study addresses this issue by grouping suppliers in groups based on their similarities in terms of decision-making standards (criteria). Crisp Gradual Means Integral used in fuzzy analytic hierarchy process is used in conjunction with the clustering technique. By grouping suppliers into clusters, several advantages can be gained by the decisions' maker firms. For example, a firm can determine the level of similarity between different suppliers when taking into consideration all variables of interest to its decision-makers. When computing this similarity, the decision-makers can substitute any of the suppliers with other supplier from the same group in the event of the occurrence of a negative event that prevents the original selected supplier from fulfilling their obligations. •The crisp gradual means integral is a well know approach used in the fuzzy analytics hierarchy processes.•The proposed methodology adapts the crisp means integral and clustering algorithms.•The resulted outcomes group suppliers into groups to help the decision makers select the best supplier.
Keywords: Clustering algorithms; Crisp Gradual Means Integral in Conjunction with Clustering Algorithms; Fuzzy analytic hierarchy processes; Group technology; Similarity coefficient; Supply chain management.
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