We derived a promising approach to reducing the energy consumption necessary in manufacturing processes from the combination of management methodologies and Industry 4.0 technologies. Based on a literature review and experts’ opinions, this work contributes to the efficient use of energy in batch production processes combining the analysis of the overall equipment effectiveness with the study of variables managed by cyber-physical production systems. Starting from the analysis of loss cause identification, we propose a method that obtains quantitative data about energy losses during the execution of batch processes. The contributions of this research include the acquisition of precise information about energy losses and the improvement of value co-creation practices so that energy consumption can be reduced in manufacturing processes. Decision-makers can use the findings to start a virtuous process aiming at carbon footprint and energy costs reductions while ensuring production goals are met.

Facility management is an evolving discipline that has received attention from both professionals and researchers in recent years. Modern facility management considers various interests related to material resources, and among others, social and environmental interests. An important opportunity for the improvement of this discipline derives from the introduction of Industry 4.0 technologies for the management of material resources. In this paper, we shall study the problem of industrial facility management, an area with important economic implications. Starting from a facility management model for the maintenance of industrial assets, we develop a general approach to maintenance based on the Internet of Things and Cyber-Physical Systems, which allows us to reason about the implementation of an effective Organisational Facility Management Unit. The objective is the continuous improvement of maintenance activities, from which also derives the improvement of the production process performance.

The document presents a low-cost, open-source device designed to facilitate the learning of technologies like artificial intelligence in embedded systems through vibration analysis. It also aims to enhance students’ skills by introducing industrial challenges into the classroom via a scaled-down prototype. This study analyzes the vibrations generated by bearings to classify, using Artificial Intelligence (AI), whether they are defective. The device integrates electronic, mechanical, and software components, leveraging online technologies and platforms like Arduino to support hands-on learning. The document provides detailed instructions on the components used, circuit connections, step-by-step construction, and implementation, allowing replication of the prototype. This device fosters the development of STEM skills, promotes the application of AI and TinyML in real-world contexts, and enriches educational programs by encouraging interdisciplinary learning.

This article aims to explain the validation of a model, which considered as a methodology, has been proposed to improve the quality of the process and the product in the Colombian software industry, which has been developed by MSMEs. Some of the methodological, procedural and evaluative aspects that have been developed and applied to validate the proposal, as well as the main results of the process, are presented in this paper. Such validation consists on the development of a group of software applications in certain sectors: education, services, industry, and trade; in which the development and management guidelines given by the model could be applied. After fi nishing them, its quality is compared to other analogous products in its functioning, which have been developed using another methodology or without applying any of them, so that the eff ectiveness or ineff ectiveness of the new model could be compared and concluded.

Introducción: La ingeniería de software, como disciplina, se representa en una serie de subdisciplinas y de buenas prácticas, Una de ellas, el aseguramiento de la calidad del software y, dentro de esta, la calidad de los requisitos. Esta investigación explora y describe la situación en el Eje Cafetero (Colombia), la contrasta con la literatura internacional y propone líneas de acción para mejorar el proceso de desarrollo del software desde el aseguramiento de la calidad de los requisitos. Objetivo: Caracterizar las prácticas de la Industria del software local y su relación con el aseguramiento de la calidad, particularmente en la fase de requisitos. Metodología: La metodología usada en la investigación fue principalmente de tipo descriptiva y exploratorio, en la cual se usó un muestreo no probabilístico por conveniencia en la que participaron 23 empresas y se recolecto la información a través de encuesta. Resultados: Datos de entrada para la formulación de un modelo para el aseguramiento de la calidad de los requisitos en la industria local del software. Conclusiones: La mayoría de los proyectos que se emprenden en la industria local están en manos de organizaciones unipersonales o MiPYMES; organizaciones estas que en su mayoría evitan seguir estándares o metodologías aceptadas y reconocidas mundialmente, llevando entonces la definición de requisitos, para el caso que ocupa este artículo, a una mínima expresión y aumentando así las estadísticas de proyectos fracasados.

The technologies of the fourth industrial revolution have impacted and benefited all industrial processes in different sectors; this allows the optimization of resources in organizations and turn, an improvement in their productivity. The adoption and implementation of 4.0 technologies have grown rapidly in some countries; in others, it remains a challenge. This article presents a literature review on the use of 4.0 technologies in industries, aimed at identifying the most used technologies, the impacted sectors, and the benefited productivity indicators. This study was carried out using the Core of Science platform and the PRISMA methodology with the information provided from the Scopus and Web of Science databases. The findings indicate that the most used technologies are the Internet of Things, cloud computing, and big data. On the other hand, manufacturing is the activity of the industrial sector with the most influence on these technologies, followed by the automotive industry. Finally, there is evidence of deficient coverage of the use of 4.0 technologies in Latin American countries and, more significantly, European countries.