Aplicação de aprendizado de máquina na otimização da cadeia de suprimentos da indústria 4.0 : uma revisão sistemática da literatura

Abstract

This undergraduate thesis conducts a systematic literature review focused on the application of machine learning to optimize supply chain management in Industry 4.0. The primary objective is to identify key trends, challenges, and opportunities related to the implementation of machine learning techniques in this context. The systematic review involved a critical analysis of 74 selected documents after extensive research in scientific databases. The results highlight the feasibility and effectiveness of machine learning at various stages of the supply chain, ranging from demand forecasting to transport route optimization. Essential applications include improving demand forecasting, optimizing production planning, efficient inventory management, enhancing transport routes, and detecting irregularities and fraud. However, the study acknowledges the existence of significant challenges, such as ensuring data quality and the complex integration of systems. With its valuable findings, this work contributes to identifying strategic opportunities and discussing obstacles to be overcome in the successful implementation of machine learning techniques in supply chain management, providing relevant guidance for the industry in the era of Industry 4.0. Keywords: machine learning; supply chain management; industry 4.0.