Modelling sustainability risk in the Brazilian cosmetics industry

Supply chains involve several stakeholders, with different environmental, social, economic, and ethical attributes, and are exposed to various risks along all stages. One of these risks relates to conditions or events related to sustainability that have the potential to generate harmful reactions from stakeholders in the supply chain. Those risks can materialize through stakeholders’ responses, when they hold companies responsible for unfavorable conditions in the supply chain, leading to reputational damage. Understanding the supply chain’s sustainability risk factors can help companies improve supply chain resilience. This article aims to empirically identify the most influential risk factors in the Brazilian cosmetics supply chain and, additionally, analyze the interrelationships between these risks. The methodology combines interpretative structural modeling (ISM) and matrix cross-impact multiplication (MICMAC) analysis, and is grounded in the opinions of cosmetics industry experts. Firstly, the critical causes and consequences are identified, called factors. Secondly, the ISM model is built, representing the interrelationships between factors and their hierarchy. Thirdly, the MICMAC analysis is performed, unfolding the strength of the relationship among the influencing factors. Fourthly, measures are designed to act on and mitigate the factors identified in the previous steps. The results show that the Brazilian cosmetic companies analyzed do not take advantage of the opportunity to take leadership in cost reduction, differentiation, and engagement with their partners. “Financial risks” were identified as the most influential among the set of risks, while “Technology and innovation” and “Legislation and responsibility” were identified as root risk factors. This research identified measures that could be implemented to act on and mitigate the root risk factors, thus contributing to the research relating to sustainability risks in supply chains.This research was funded by FCT – FUNDAÇÃO PARA A CIÊNCIA E TECNOLOGIA, grant number UIDB/00285/2020.