Enhanced Best–Worst Method for Prioritizing Interdependent Risks

In the modern landscape, prioritizing risks is crucial for directing resources toward critical threats and ensuring organizational resilience and continuity. This study introduces an enhanced Best–Worst Method (BWM) that integrates risk interdependencies, employing clustering for efficient data handling and accurate prioritization. A case study in an online store confirmed the model’s validity, yielding significantly lower error rates and heightened consistency compared to the original BWM. Notable findings include identifying high-impact risks, such as failure to deliver on time and incorrect production capacity calculations. The enhanced BWM consistently outperformed Analytic Hierarchy Process (AHP) in managing interdependencies, underscoring its robustness and reliability. Furthermore, the rank reversal test highlighted stable prioritizations across scenarios, validating the model’s resilience under varying conditions. This approach not only offers practical management insights for high-priority risks but also emphasizes strategic planning for enhanced organizational resilience, making it highly applicable in complex decision-making contexts.