In the competitive automotive parts manufacturing industry, accurately identifying and prioritizing failure modes is essential for preventing financial losses, ensuring passenger safety, and maintaining quality standards, since systematic risk management directly affects production efficiency and product reliability. This applied, descriptive-analytical study aimed to identify and prioritize failure modes in the constant-velocity (CV) joint assembly process of an Iranian automotive parts manufacturer by integrating the Fuzzy Best-Worst Method (FBWM) and Fuzzy VIKOR (FVIKOR). A panel of fifteen purposively selected experts, each with at least ten years of relevant experience, took part in a three-round Delphi survey using a five-point Likert scale to screen the candidate risk factors and failure modes; the retained risk factors were then weighted using FBWM, and the screened failure modes were ranked using FVIKOR. The findings showed that severity received the highest relative weight among the retained risk factors, and that failure modes associated with the tensile strength and dimensional and spline tolerances of the CV-joint components received the highest priority for corrective action. The pairwise comparisons of the expert panel showed a high level of consistency, and combining Delphi screening with fuzzy multi-criteria weighting and ranking improved prioritization accuracy compared with the traditional risk priority number approach. The findings provide production managers with a reliable and replicable basis for preventive decision-making and process optimization in automotive parts manufacturing.