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

Q1 Economics, Econometrics and Finance

Journal of Open Innovation: Technology, Market, and Complexity Pub Date : 2025-01-31 DOI:10.1016/j.joitmc.2025.100489

Ishansh Gupta , Seyed Taha Raeisi , Sergio Correa , Hendro Wicaksono

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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.

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汽车供应链风险因素评估：扩展PESTLE框架的混合模糊AHP-TOPSIS方法

本研究的目的是评估影响汽车供应链关键绩效指标（kpi）的外生风险因素（ERFs），旨在增强应对全球中断的弹性。主要的研究问题集中在识别和确定对运营绩效构成最大威胁的erf的优先级。采用模糊层次分析法（FAHP）和模糊理想解相似偏好排序法（FTOPSIS）相结合的混合决策框架。验证是通过18位供应链专业人士的不同角色和合计318年的经验来确保的。该研究确定了34个erf，包括半导体短缺、流行病和信息基础设施中断，并评估了它们对关键绩效指标（kpi）的影响，如缺少零件、积压、特殊运输和错误交付。通过在运输和材料维度上扩展传统的PESTLE框架，本研究提供了可操作的策略，以减轻风险并加强动荡环境下的供应链弹性。

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