Water treatment chemical supply chain disruption risk assessment and mitigation strategy ranking using BWM–VIKOR

Chemical supply disruptions can compromise compliance with water treatment regulations and service continuity. This study proposes an integrated multi-criteria decision-making framework that combines the Best–Worst Method (BWM) and VIKOR to prioritize mitigation strategies for water treatment chemical supply chain disruptions. A case application at Shanghai’s Yangshupu Water Plant demonstrates the approach. BWM-based weighting shows that compliance/public health (0.26) and service continuity (0.18) dominate decision priorities (44% combined), followed by recoverability/flexibility (0.16) and supply vulnerability (0.14). Using these weights, VIKOR ranks mitigation alternatives and identifies a compromise set. Under baseline conditions (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:v=0.5$$\end{document}), dual sourcing and supplier prequalification achieve the best compromise performance (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Q=0.083$$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:S=0.563$$\end{document}), while safety stock and reorder redesign minimize worst-case regret \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(R=0.140)$$\end{document}; the acceptable advantage condition is not satisfied (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.135<0.167$$\end{document}), leading to a compromise set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\left\{A1,A2\right\}$$\end{document}. Sensitivity and scenario tests confirm that the shortlist is robust, with safety stock becoming top-ranked under prolonged logistics disruption and QA/QC strengthening rising under quality failures. The proposed framework provides transparent, defensible support for utility resilience planning.