In supply chain management (SCM), the selection of suppliers plays a vital role in an efficient production process. Over the last few years, to form a trade-off between the quantitative and qualitative criteria, the selection of suppliers in SCM is considered very conclusive. The decisions generally demand different criteria to balance between every possible inconsistent parameters involving subjectivity and uncertainty in the process. The evaluation information mostly depends on the experience and knowledge of experts that are unsure and indistinct. This study introduces a novel decision-making method by integrating rough approximations with fuzzy numbers and preference ranking organization method for enrichment evaluation (PROMETHEE) to deal with subjective and objective vagueness in the assessment of decision makers. To minimize the dependency on experts’ judgements, entropy weights are computed from the original data set. The preference index is then computed using entropy weights and deviations among alternatives. The alternatives are ranked using the intersection of both positive flow and negative flow. To show the significance and importance of fuzzy rough PROMETHEE method, a case study of supplier selection in industrial manufacturing is discussed in detail. The developed method is effectively used to rank the supplier alternatives under given criteria. The results are then compared with different rough numbers and fuzzy numbers based on MCDM methods. The fuzzy rough PROMETHEE method can be efficiently used for the selection of the best suppliers to reduce losses and maximize the production process.

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Failure modes and effects analysis (FMEA) is a systematic framework that is broadly used to recognize and eliminate viable failures within objects, services, structures, designs, or organizations. In the general FMEA process, failure modes are assessed and ranked by the risk priority numbers which are attained through the product of crisp ratings of the risk elements such as detection, occurrence, and severity of each failure mode. But the traditional FMEA technique fails to deal with the uncertainty, present in complex systems. To enhance the effectiveness of conventional approaches, this paper presents a novel FMEA technique based on rough numbers and preference ranking organization method for enrichment evaluations (PROMETHEE) in the multi-criteria decision-making problems. Rough numbers are applied to handle ambiguous information in terms of rough intervals instead of single numerical ratings. In the proposed approach, an entropy weight technique is applied to determine the weights of risk factors. This presented approach uses the usual criteria preference function to show the preferences of failure modes. The risk priorities of the recognized failure modes are acquired by the rough PROMETHEE method. To illustrate its potential applications, the proposed method is applied to examine the risks of the general anesthesia process in the medical field. In the end, a comparison with the existing techniques is also described to show the efficiency of the proposed model.