Overall equipment effectiveness (OEE) is a key performance indicator used to measure equipment productivity. The purpose of this study is to review and analyze the evolution of OEE, present modifications made over the original model and identify future development areas. This paper presents a systematic literature review; a structured and transparent study is performed by establishing procedures and criteria that must be followed for selecting relevant evidences and addressing research questions effectively. In a general search, 862 articles were obtained; after eliminating duplicates and applying certain inclusion and exclusion criteria, 186 articles were used for this review. This research presents three principal results: (1) The academic interest in this topic has increased over the last five years and the keywords have evolved from being related to maintenance and production, to being related to lean manufacturing and optimization; (2) A list of authors who have developed models based on OEE has been created; and (3) OEE is an emerging topic in areas such as logistics and services. To the best of our knowledge, no comparable review has been published recently. This research serves as a basis for future relevant studies.

The research trend in additive manufacturing (AM) has evolved over the past 30 years, from patents, advances in the design, and layer-by-layer materials, to technologies. However, this evolution is faced with some barriers, such as the implementation of additive manufacturing (AM) in operations, its productivity limitations, and economic and social sustainability. These barriers need to be overcome in order to realize the full potential of AM. The objective of this study is to analyze the bibliometric data on these barriers through a systematic review in two study areas: business model innovation and sustainability in AM from Industry 4.0 perspective. Using the most common keywords in these two study areas, we performed a search on the Web of Science (WoS) and Scopus databases and filtered the results using some inclusion and exclusion criteria. A bibliometric analysis was performed for authorship productivity, journals, the most common keywords, and the identified research clusters in the study areas. For the bibliometric analysis, the BIBEXCEL software was used to extract the relevant information, and Bibliometrix was used to determine the research trend over the past few years. Finally, a literature review was performed to identify future trends in the study areas. The analysis showed evidence of the relationship between the study areas from a bibliometric perspective and areas related to AM as an enabler for Industry 4.0.

This work investigates Industry 4.0 technologies by developing a new key performance indicator that can determine the energy consumption of machine tools for a more sustainable supply chain. To achieve this, we integrated the machine tool indicator into a cyber–physical system for easy and real-time capturing of data. We also developed software that can turn these data into relevant information (using Python): Using this software, we were able to view machine tool activities and energy consumption in real time, which allowed us to determine the activities with greater energy burdens. As such, we were able to improve the application of Industry 4.0 in machine tools by allowing informed real-time decisions that can reduce energy consumption. In this research, a new Key Performance Indicator (KPI) was been developed and calculated in real time. This KPI can be monitored, can measure the sustainability of machining processes in a green supply chain (GSC) using Nakajima’s six big losses from the perspective of energy consumption, and is able to detect what the biggest energy loss is. This research was implemented in a cyber–physical system typical of Industry 4.0 to demonstrate its applicability in real processes. Other productivity KPIs were implemented in order to compare efficiency and sustainability, highlighting the importance of paying attention to both terms at the same time, given that the improvement of one does not imply the improvement of the other, as our results show.

The purpose of this paper is to build up and implement a framework of a lean performance indicator with collaborative participation. A new indicator derived from OEE is presented, overall process effectiveness (OPE), which measures the effectiveness of an operation process. The action research (AR) methodology was used; collaborative work was done between researchers and management team participation. The framework was developed with the researchers’ and practitioners’ experiences, and the data was collected and analyzed; some improvements were applied and finally, a critical reflection of the process was done. This new metric contributes to measuring the unloading process, identifying losses, and generating continuous improvement plans tailored to organizational needs, increasing their market competitiveness and reducing the non-value-add activities. The OEE framework is implemented in a new domain, opening a new line of research applied to logistic process performance. This framework contributes to recording and measuring the data of one unloading area and could be extrapolated to other domains for lean performance. It was possible to generate and validate knowledge applied in the field. This study makes collaborative participation providing an effectiveness indicator that helps the managerial team to make better decisions through AR methodology.

El movimiento de la Industria 4.0, impulsado por tecnologías emergentes, abre nuevas oportunidades para la transferencia de conocimiento, fomentando la innovación y el emprendimiento. Los Fab Labs representan esta tendencia al facilitar la colaboración entre aprendices y expertos. En América Latina, los Fab Labs se adaptan a las necesidades locales, ofreciendo plataformas para desarrollar habilidades y resolver problemas en diversos sectores. Este artículo analiza el impacto de las tecnologías emergentes y los Fab Labs en la educación superior, centrado en América Central y del Sur. Se recopilaron datos de la Fab Foundation y de una muestra de Fab Labs regionales, examinando instalaciones, áreas, equipamiento y propósitos. Los resultados reflejan cómo estas iniciativas impulsan el desarrollo técnico y la innovación local. Además, se diseñó un modelo de impacto para explicar su contribución a la innovación, la educación y el emprendimiento en la región.