An Integrated Cloud-Based Failure Mode and Effects Analysis Framework for Risk Assessment of Unmanned Aerial Vehicle...

Unmanned aerial vehicles (UAVs) are increasingly considered for urban logistics, including the delivery of critical medical supplies. However, their safe operation in dense urban environments requires systematic risk analysis that can account for both technological uncertainties and human factors. Conventional failure mode and effects analysis (FMEA) offers a structured way to identify and prioritize risks but is limited by its use of deterministic evaluations and the absence of mechanisms to handle uncertainty or disagreement among experts. To address these limitations, this study develops an integrated cloud-based FMEA framework to UAV logistics operations. The method begins with expert assessments expressed through linguistic terms, which are transformed into cloud models to represent uncertainty more faithfully. A consensus-reaching process is then applied to align divergent expert opinions and establish an aggregated evaluation matrix. To determine the relative importance of risk factors, a hybrid weighting scheme is employed that combines subjective judgments through cloud step-wise weight assessment ratio analysis (SWARA) and objective information through cloud criteria importance through intercriteria correlation (CRITIC). These weights are subsequently integrated into a cloud technique for order similarity to ideal solution (TOPSIS) procedure that produces a transparent ranking of failure modes. The framework is applied to a case study of medical supply delivery UAVs, where it identifies the most critical risks and validates the prioritization through sensitivity and comparative analyses. The results demonstrate that the proposed approach provides a robust and practical decision-support tool for enhancing safety in urban UAV logistics.