Intelligent industrial design assessment for product development using the picture fuzzy CURLI MCDM approach

Evaluation of design options under various conflicting criteria and uncertain information is a vital task in the product development through industrial design assessment. This paper proposes a smart decision-making model for evaluating industrial design based on the picture fuzzy collaborative unbiased rank list integration (PF-CURLI) multi-criteria decision making (MCDM) model. The suggested approach is efficient for dealing with uncertainty because it uses membership, non-membership, and neutral levels in assessing experts, yielding strong, consistent decision-making results. There is a hypothetical case study of a decision-making process involving several decision-makers, 15 design options, and 7 criteria to determine whether the proposed framework is relevant. The model assesses alternatives based on functional performance, aesthetic quality, ergonomic design, sustainability, innovation, and production cost, and offers a logical ranking of industrial design choices. Sensitivity and benchmarking analyses illustrate the stability, strength, and efficiency of the proposed approach compared to what is currently in use. The results indicate that the proposed framework is an efficient and reliable decision-support tool for industrial design evaluation in complex product development environments.