Analyzing readiness factors for organizational disaster management in upstream automotive supply chains based on neutrosophic DEMATEL model

Abstract

This study develops an integrated framework to identify and evaluate key readiness factors (RFs) that support effective supply chain disaster management (SCDM) and strengthen resilience across upstream automotive supply networks. Using a case study of Thai automobile manufacturing, twelve critical RFs are identified through a comprehensive literature review and interpreted using the contingency resource-based view (CRBV), which emphasizes adaptive resource allocation and informed decision-making during crises. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) combined with single-valued neutrosophic sets (SVNS) is applied to examine complex causal relationships under uncertainty, while prominence values determine the relative importance of each factor. The top-ranked RFs include supplier relationship management, collaborative communication networks, contingency planning, emergency preparedness plans, risk assessment and monitoring, supply chain visibility, scenario planning, crisis leadership, cross-functional training, and supply chain redundancy. Relation analysis identifies five causal factors with positive influence values: crisis leadership, cross-functional training, collaborative communication networks, supplier relationship management, and risk assessment and monitoring. Seven RFs show negative influence values and are classified as effect factors: contingency planning, supply chain visibility, scenario planning, emergency preparedness plans, supplier evaluation, information technology integration, and supply chain redundancy, indicating their dependence on improvements in the causal factors. The findings support strategic resource prioritization, stronger disaster preparedness, and improved coordination across supply chain partners. The findings provide managerial guidance on strategic resource prioritization, capability development, and enhanced coordination across supply chain partners. The framework offers a flexible, data-driven decision-support tool that can be adapted to industries facing similar disruption risks.