Applying the Heuristic to the Risk Assessment within the Automotive Industry Supply Chain

Supply chains are complex networks that receive assiduous attention in the literature. Like any complex network, a supply chain is subject to a wide variety of risks that can result in significant economic losses and negative impacts in terms of image and prestige for companies. In circumstances of aggressive competition among companies, effective management of supply chain risks (SCRs) is crucial, and is currently a very active field of research. Failure Mode, Effects and Criticality Analysis (FMECA) has been recently extended to SCR identification and prioritization, aiming at reducing potential losses caused by lack of risk control. This article has a twofold objective. First, SCR assessment is investigated, and a comprehensive list of specific risks related to the automotive industry is compiled to extend the set of most commonly considered risks. Second, an alternative way of calculating the Risk Priority Number (RPN) is proposed within the FMECA framework by means of an integrated Multi-Criteria Decision-Making (MCDM) approach. We give a new calculation procedure by making use of the Analytic Hierarchy Process (AHP) to derive factors weights, and then the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) to evaluate the new factor of “dependence” among the risks. The developed joint analysis constitutes a risk analysis support tool for criticality in systems engineering. The approach also deals with uncertainty and vagueness associated with input data through the use of fuzzy numbers. The results obtained from a relevant case study in the automotive industry showcase the effectiveness of this approach, which brings important value to those companies: When planning interventions of prevention/mitigation, primary importance should be given to (1) supply chain disruptions due to natural disasters; (2) manufacturing facilities, human resources, policies and breakdown processes; and (3) inefficient transport.

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“…Marasova et al [44] Supply, operational, demand, security, policy, competitive, resource, macro risks and other risks.…”

Section: Authors Risks Classificationmentioning

confidence: 99%

Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA

et al. 2020

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Conceptual model for breaking ripple effect and cycles within supply chain resilience

Marcucci

Mazzuto

Ciarapica

et al. 2022

Supply Chain Forum: An International Journal

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A multicriteria decision-making model for risk management in an integrated management system

Cagnin,

Oliveira,

Cauchick-Miguel

et al. 2024

TQM

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PurposeThis paper proposes a multicriteria model for risk management to identify and assess risks associated with an integrated management system (IMS). The main benefit of the proposed model is its systemic and logical visualization, which may facilitate the understanding of this proposal’s practical application.Design/methodology/approachThe research design consists of four stages: (1) conduct a literature review to establish risk management models in IMS; (2) collect data concerning risk management models in IMS from a large multinational automotive company; (3) propose a multicriteria model to define and assess risks as well as prioritize mitigation actions and (4) apply the proposed multicriteria model to the data collected in case-based research to evaluate the practical viability of the model to contribute to methods traditionally used.FindingsThe results showed that the proposed risk management model contributes to more reliable decision-making in an IMS. The application of the proposed model identified 85 risks in the total processes of the IMS, 31 of which were classified as high risk; thus, priority actions to be taken were defined. The risk classification and prioritization facilitated the implementation of measures to mitigate or eliminate risks, as pointed out by the company managers.Research limitations/implicationsOne of the limitations is the fact that specific knowledge is required to maintain and update the multicriteria decision-making tool used in this study. Another one implies the approach to managing risks under the different ISO standards and sector-specific requirements, since this may require updates and customization of the proposed risk management model.Practical implicationsThe implementation of IMS in contemporary business environments can be supported by a robust risk management approach. In addition, it provides the leadership with a holistic view of multiple aspects related to a company and fosters continuous improvement.Social implicationsThe social implications of this study are assessed indirectly. This study contributes to the improvement of company management models.Originality/valueTraditionally, the methods used for risk management in IMS are usually applied independently of techniques such as failure mode and effect analysis. The model developed in this work enables to manage risks continuously to achieve a systemic view of organizational issues and greater transparency of the processes.

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Applying the Heuristic to the Risk Assessment within the Automotive Industry Supply Chain

Cited by 3 publications

References 5 publications

Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA

Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA

Conceptual model for breaking ripple effect and cycles within supply chain resilience

A multicriteria decision-making model for risk management in an integrated management system

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