Efficient management of production processes in modern manufacturing depends on the timely identification of their most critical phases, as such recognition directly enhances process reliability, productivity, and product quality. To address this need, an objective multi-attribute decision-making (MADM) framework has been developed by integrating the Criteria Importance Through Inter-criteria Correlation (CRITIC) method with Pareto analysis, a well-established approach also referred to as ABC classification. Within this framework, a comprehensive set of evaluation criteria was determined in collaboration with a Process Failure Mode and Effects Analysis (PFMEA) team from a Tier-1 automotive manufacturer. The decision matrix was constructed from data extracted from PFMEA reports that had been subjected to preliminary statistical processing to ensure robustness and comparability. The relative importance of the criteria was then established using the CRITIC method, which objectively derives weights from statistical indicators such as the arithmetic mean, standard deviation, and inter-criteria correlation coefficients. The framework was subsequently applied to the PFMEA report for a rear axle assembly process, encompassing 16 discrete production phases. Pareto analysis was employed to classify the phases according to their criticality, thereby enabling a systematic prioritization of process risks. The resulting classification demonstrated strong consistency with expert evaluations and was confirmed to reflect real-world production conditions accurately. Beyond confirming methodological validity, the findings underscore the advantages of employing a fully objective weighting mechanism combined with a widely recognized prioritization tool, thereby offering a transparent and replicable basis for decision-making in complex manufacturing contexts. This integration not only supports continuous improvement and risk mitigation but also provides a scalable framework applicable to a broad range of industrial processes where critical phase identification is essential.
Remote sensing plays a crucial role in disaster management. Moreover, its effectiveness is severely limited due to operational, technological and environmental challenges. Data acquisition can be disrupted by sensor limitations and by extreme events or natural factors, such as cloud cover. In fact, high-resolution imagery often requires significant processing time, specialized expertise and expensive infrastructure. Therefore, ensuring timely, accurate and accessible remote sensing data at all stages—preparedness, response, recovery and mitigation—is a major challenge. This study explores the application of multi-criteria decision making (MCDM) techniques using bipolar fuzzy numbers (BFNs) to evaluate this. We apply the weighted and ranking MCDM method, i.e., Method Based on the Removal Effects of Criteria (MEREC) and Multi-Attributive Border Approximation Area Comparison (MABAC), respectively, in this paper. The decisions of multiple decision makers (DMs) are considered when collecting this problem related data and BFNs are utilised as mathematical tools to handle uncertainty. In order to address the ambiguity and inconsistency of the system, we finally conclude to conduct the comparative and sensitivity analyses here with the final result.
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