Analyzing readiness factors for organizational disaster management in upstream automotive supply chains based on neutrosophic DEMATEL model
Year: 2025
Type: article
Abstract: Disaster management in the complex supply chain management of the automotive industry is critical. Factors that improve readiness to mitigate operational, financial, and reputational disruptions in this vulnerable sector is an important area of research. The automotive industry’s reliance on a global network of suppliers for parts, materials, and components heightens its exposure to such risks. This study thus presents a framework designed to identify and analyze key readiness factors that contribute to effective supply chain disaster management (SCDM), with the goal of strengthening the resilience of upstream automotive supply chains. Through a case study of Thai car manufacturing, this research identifies 12 crucial readiness factors derived from a comprehensive literature review. It is underpinned by Contingency Resource-Based View (CRBV) theory which supports efficient resource allocation and informed decision making for disaster management. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method alongside the Single-Valued Neutrosophic Set (SVNS) approach are employed. Findings highlight three essential components for improving SCDM: supplier relationship management, collaborative communication networks, and contingency planning. These findings offer insights for managers and decision makers seeking to enhance upstream SCDM in the face of potential disruptions. The proposed framework serves as a strategic tool for improving disaster preparedness, providing a flexible model that can be applied not only to the automotive sector, but also to other industries facing similar challenges.
Source: Results in Engineering
Authors: Detcharat Sumrit, Orawan Jongprasittiphol
Institution: Mahidol University
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,940
OpenAlex ID: W4416413544
