Purpose: This study presents a systematic literature review that provides a broad and holistic view of how machine learning can be used and integrated to enhance decision-making in various areas of the supply chain, highlighting its combination with other techniques and models.Design/methodology/approach: An exhaustive literature review used three sets of keywords in the Scopus and Web of Science (WoS) databases. Through a rigorous filtering process, 70 articles were selected from an initial total of 410, focusing on those that specifically addressed the intersection of machine learning and decision-making in supply chain management.Findings: Machine learning has proven to be an essential tool in the supply chain, with applications in inventory management, logistics, and transportation, among others. Its integration with other techniques has led to significant advances in decision-making, improving efficiency in complex environments. Combining machine learning methods with traditional techniques has been particularly effective, and integration with emerging technologies has opened up new application possibilities.Originality/value: Unlike previous studies that focused on specific areas, this study offers a broad perspective on the application of machine learning in the supply chain. Additionally, combining machine learning techniques with other models is highlighted, representing added value for the scientific community and suggesting new avenues for future research.

Additive Manufacturing is a novel manufacturing method in which the part is produced layer by layer from a 3D CAD model. In this work, we present the mechanical characterization of Fusion Deposition Modeling (FDM). Composite parts made by a nylon matrix with two kinds of fiber reinforcements: carbon fiber or fiberglass. From the obtained microstructure, we perform a division of the composite part in regions, and individual stiffness matrices are encountered by either using a linear elastic isotropic model, for the case of solid matrix filling, or an orthotropic linear elastic model based on micromechanical results. Then, a volume average stiffness method is employed to perform the characterization of the whole part. The theoretical results are compared with the experimental data, showing good agreement for both cases. This research allows the prediction of the structural behavior of additive manufacturing 2composite parts.