Dynamic Fuzzy Approach for Assessing Manufacturing Agility and ESG Performance Using Time-Series Data

High-frequency monitoring of manufacturing agility and Environmental, Social, and Governance (ESG) responsiveness is increasingly required in data-rich operations, yet many practical indices remain low-frequency, weakly decomposable, or difficult to interpret in weekly control settings. This study presents a single-enterprise methodological demonstration of a weekly fuzzy monitoring model with dual benchmarks for explainable operational control. The empirical panel covers 156 weeks from 2023 to 2025 across three plants, five product families, and 27 KPIs grouped into Operational Agility, Sustainable Responsiveness, and Socio-Market Adaptability. KPI and benchmark weights were elicited through a two-round Delphi process with 24 experts. The model combines fixed target-centered B1 compliance thresholds with percentile-calibrated B2 thresholds for direction-adjusted week-to-week adaptation. In the calibrated specification, the overall index mean is 0.618 with a range of 0.489 to 0.741, while the mean B1 and B2 values are 0.619 and 0.617. Matched-level validation at the plant–product–week level (N = 2340) shows a positive association with EBIT (Pearson r = 0.222, 95% CI [0.200, 0.245]), and time-safe calibration checks preserve the substantive interpretation of the index. The results support the model as an explainable, human-in-the-loop instrument for within-case weekly monitoring and diagnosis rather than as a broadly validated predictive model.