Evaluating risk factors in automotive supply chains: A hybrid fuzzy AHP-TOPSIS approach with extended PESTLE framework

Abstract

The purpose of this study is to evaluate Exogenous Risk Factors (ERFs) affecting Key Performance Indicators (KPIs) in automotive supply chains, aiming to enhance resilience against global disruptions. The primary research question focuses on identifying and prioritizing ERFs that pose the greatest threat to operational performance. A hybrid decision-making framework integrating Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is employed. Validation is ensured through insights from 18 supply chain professionals with diverse roles and a combined 318 years of experience. The study identifies 34 ERFs, including semiconductor shortages, pandemics, and information infrastructure disruptions, and evaluates their impact on KPIs such as missing parts, backlogs, special transports, and wrong deliveries. By extending the traditional PESTLE framework with Transportation and Material dimensions, this study provides actionable strategies to mitigate risks and strengthen supply chain resilience in volatile environments.