Purpose Humanitarian supply chain management (HSCM) in today’s environment faces the challenges such as information availability, inventory management, collaboration, logistics related issues and preparedness. The purpose of this paper is to evaluate the HSCM performance, considering the consequences in terms of operation, recovery and responsiveness based on the fuzzy estimates of the components presented. Design/methodology/approach In the study, triangulation approach was adapted for collecting data and developing a hierarchical structure for humanitarian supply chain performance assessment. The relationships between HSCM performance and its suddenness and required preparedness are depicted by cause and effect diagrams. The concepts of fuzzy association and fuzzy composition are applied to identify relationships. Findings In the hierarchy presented, the performance in a disaster situation, preparedness and suddenness of the situation and factors that influence the above are modeled. The taxonomy is developed for describing the relationship between factors, their likelihoods and impacts to achieve consistent quantification. Research limitations/implications The study considers case studies from Indian conditions; however, conditions in other countries and their practices for the disaster management may vary to certain extent. Practical implications A methodology presented for evaluating the exposures in considering the consequences in terms of responsiveness, operations, recovery, mitigation and emergency response. The study may help the humanitarian relief practitioners to understand the insights of the disaster situations using the proposed framework. Originality/value A common language for describing the different factors of HSCM is presented, which includes terms for quantifying likelihoods and impacts. The concept of fuzzy association and fuzzy composition has been applied to identify relationships between sources and consequences on HSCM performance. The use of descriptive linguistic variables is ensured through the implementation of fuzzy logic.