Purpose:

To demonstrate the relevance of risk management (RM) - particularly in the risk identification phase - within organizations, highlighting its usefulness in the context of the supply chain (SC) of the automotive industry (AI).

Methodological design:

Through a literature review of 44 articles on RM in the SC of AI, 19 empirical publications were selected, published between the years 2018-2022, and retrieved from the databases: Google Scholar and Dimensions. The publications are situated in a global context and belong to Scimago classifications ranging from Q1 to Q4.

Results:

A total of 17 distinct risk categories were identified, with demand-driven risks and acquisition risks being particularly prominent. Notably, the most commonly identified risk types encompass issues concerning subpar part quality and inadequate communication with suppliers. The main contribution of this work is a typology of risks.

Research limitations:

The subjectivity that may have been present when the researchers evaluated the risks and the lack of formal risk management frames are potential constraints of this study.

Findings:

The primary findings indicate that RM serves as a managerial instrument which improves the performance of AI by providing criteria to identify risk factors within a global economy. These factors are considered not only as threats but also as opportunities.

        · abstract in Spanish     · text in Spanish     · Spanish ( pdf )