Enhancing Supply Chain Resilience through Correlation-Aware Risk Prioritization: A Comparative and Statistical Analysis Approach
Keywords:
Supply chain risk management, DEMATEL, Petri Net, Neural networks, Risk prioritization, Automotive industryAbstract
In today’s interconnected automotive supply chains, managing correlated risks is critical to prevent cascading disruptions. This study compares four prioritization methodologies—Risk Priority Number (RPN), Decision-Making Trial and Evaluation Laboratory (DEMATEL), Petri Net simulation, and a feedforward multilayer perceptron (MLP) neural network—applied to 36 expert-identified risks within vehicle component supply chains. Using severity, occurrence, and correlation-based influence data, the methods reveal contrasting prioritization patterns. While RPN assumes risk independence, DEMATEL and Petri Net capture causal propagation, and the MLP model identifies non-linear relationships among risks. Statistical tests (Friedman and post-hoc Wilcoxon) confirm significant differences, showing that correlation-aware approaches alter risk rankings by 15–25%. Applied to Saipa Press’s heavy stamping component supply chain under sanctions and regulatory constraints, the proposed framework improves resource allocation and highlights systemic vulnerabilities. The results advocate hybrid, correlation-integrated approaches for resilient supply chain decision-making in volatile environments.
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Copyright (c) 2026 Zohreh Mousavi (Author); Sadigh Raissi; Kambiz Jalali Farahani (Author)
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