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.

Introducción: los métodos de aprendizaje automático permiten manejar datos estructurados y no estructurados para construir modelos predictivos y apoyar la toma de decisiones. Objetivo: identificar los métodos de aprendizaje automático aplicados para predecir el comportamiento epidemiológico de enfermedades arbovirales utilizando datos de vigilancia epidemiológica. Metodología: se realizó búsqueda en EMBASE y PubMed, análisis bibliométrico y síntesis de la información. Resultados: se seleccionaron 41 documentos, todos publicados en la última década. La palabra clave más frecuente fue dengue. La mayoría de los autores (88,3 %) participó en un artículo de investigación. Se encontraron16 métodos de aprendizaje automático, el más frecuente fue Red Neuronal Artificial seguido de Máquinas de Vectores de Soporte. Conclusiones: en la última década se incrementó la publicación de trabajos que pretenden predecir el comportamiento epidemiológico de arbovirosis por medio de diversos métodos de aprendizaje automático que incorporan series de tiempo de los casos, variables climatológicas, y otras fuentes de información de datos abiertos.

This review presents biocomposite materials as a sustainable strategy for manufacturing, aligned with the principles of the circular economy. A bibliometric and systematic review of 109 studies was conducted, highlighting the mechanical, thermal, and chemical performance of biocomposites, as well as their applications in sectors such as construction, automotive, and packaging. The findings reveal that biocomposites with biodegradable polymers matrices and reinforced with natural fibers such as flax, jute, and hemp can reduce product weight by up to 40%, improving mechanical performance by 25%, and lower carbon diioxide emissions by ≈30% compared to conventional materials. Data analysis identified key production challenges, emerging trends, innovation gaps, and future research opportunities, while also mapping international collaboration networks and thematic evolutions in research. This work concludes that biocomposites are alternatives to fossil-based materials and key enablers of circular economy strategies and sustainable manufacturing practices, offering significant environmental, economic, and regulatory advantages. However, their widespread adoption and large-scale implementation still faces critical challenges, including material compatibility, performance consistency, standardization in process, and end-of-life management. Overcoming these limitations requires interdisciplinary collaboration, advances in biopolymers, and the application of life cycle assessment from the design stage.

The article conducts a systematic literature review on digital transformation and emerging technologies in manufacturing SMEs. It analyzed 961 articles from databases such as Scopus, Emerald, Redalyc, Dialnet, and Ebsco, selecting 60 studies that met the inclusion criteria. The research identified the most used emerging technologies, facilitators, and barriers to digital transformation in SMEs. The main results show that IoT and Cloud Computing are the most referenced technologies, with aspirations to integrate AI into business processes. Challenges include limited financial resources, lack of technical skills, and cultural resistance. The review concludes that successful digital transformation in SMEs can enhance operational efficiency, reduce costs, and improve product quality. However, SMEs adopt these technologies more slowly than large companies due to post-adoption difficulties, such as insufficient resources and training.