The concept of granulized Z-numbers improves information utilization and manages the degree of uncertainty in decision-making. In this paper, a novel scoring method, namely, ordered weighted averaging-based expected granulized Z-number, a new aggregation operator, named, granulized Z-number-based ordered weighted averaging operator, and a novel fuzzy risk assessment scheme is proposed. The proposed scoring method is used to order the granulized Z-numbers and takes care of the possibilistic as well as probabilistic information contained in the granulized Z-numbers. The maximum entropy principle-based nonlinear optimization model is formulated to capture the aforesaid probabilistic information during the scoring process. Based on the proposed scoring and ordering method, the granulized Z-number-based ordered weighted averaging operator is developed, which judiciously integrates the benefits of the granulized Z-numbers and the ordered weighted averaging operator to provide an improved aggregation of the decision-making information collected from multiple sources (experts). The required properties are also proved. Finally, the novel fuzzy risk assessment scheme is developed using the granulized Z-number-based ordered weighted averaging operator, the average linkage-based ordered weighted averaged similarity measure between two granulized Z-numbers, and the basic operations of logical gates of a fault tree. This scheme provides the system-level failure probability in an easy-to-understand form with reliability. A case study is also presented to demonstrate the usability and feasibility of the proposed models and schemes.