Modeling the enablers of humanitarian supply chain management: a hybrid group decision-making approach

Purpose This paper intends to explore and appraise the humanitarian supply chain management enablers (HSCMEs) for efficient and effective humanitarian operations. This research aims to analyze the interaction of enablers for humanitarian supply chain management (HSCM) using a proposed hybrid framework consists of fuzzy Delphi (FD), interpretive structural modeling (ISM)-matriced impacts croises multiplication appliquee a un classement (MICMAC) and revised Simos approach. Design/methodology/approach This research is deliberate to identify 28 HSCMEs through a literature review and experts' opinions; out of which 20 HSCMEs are selected using FD. ISM is applied to know contextual relationship among the selected HSCMEs for developing a hierarchical model. The MICMAC analysis classifies the HSCMEs based on driving power and dependence power to validate the developed hierarchical ISM structure. The revised Simos technique is used to prioritize the HSCMEs to access its relative significance in humanitarian operations. Findings The finding of the analysis suggests that government policy and leadership support obtained the highest priority, having high driving power and low dependence power is significantly strategic and emerged as the leading driver for the HSCM implementation. Research limitations/implications ISM model presents an insight into interrelationship among HSCMEs, but this cannot quantify the impact of each HSCMEs. Practical implications Disaster relief aid agencies and stakeholders may focus on the enablers having high driving power and higher weight in designing and executing an effective and efficient humanitarian supply chain and to improve their activities and strategies of HSCM. Social implications This research helps humanitarian logisticians and humanitarian organizations to make better decisions to improve their operational performance in pre and postdisaster phases. Originality/value This paper explores the application of proposed hybrid framework to analyze the HSCMEs that can be considered as the original contribution.