Preliminary research about electric vehicle charging stations / Pesquisa preliminar sobre estações de carregamento de veículos elétricos

With growing concern about environmental issues, there is a constant search for engineering solutions on a global scale. In this context, attention is focused on the automotive sector, which is responsible for a large part of environmentally harmful emissions. Thus, electric vehicles appear as one of the solutions to this problem. However, it is not possible to popularize these cars without a recharging structure that can meet this demand. In this context, the objective of this paper is to present studies and research carried out in the area of technical and economic feasibility focused on recharging infrastructure, in order to provide knowledge and assist future research projects in this area

Performance Analysis on Health and Safety Issues of Companies from the Slaughterhouse Industry

The objective of this article is to analyses the performance of companies of the slaughterhouse industry in health and safety issues. The research method is the quantitative modelling. The main research technique uses a mixed method based on multi-attribute utility method (MAUT) and artificial neural networks (ANN). The research object are 34 slaughterhouse companies located in Southern Brazil. Then, we ranked the companies and modeled their decision trees using the MAUT method. From these results, neural networks were used to benchmark and compare the methods. This resulted in a linear equation that represents the closest solution to the ideal and percentage error in the decision trees resolution. Thus, neural networks are most efficient, because they indicate which KPIs (key performance indicators) most influence the organizations performance. We numerically present the gain of information and the margin of error, concluding that some KPIs do not influence competitiveness without requiring controls. The academic and social contribution is that through the union of MAUT and neural networks we can measure the performance and select the main KPIs that need to be controlled for any type of industry

Obtención de documentos de investigación sobre la competitividad de las pequeñas y medianas empresas: un enfoque basado en las redes de autores

This study aims to present a scientometric analysis, based on author’s network maps, to determine the most influential and relevant authors with papers published about the subject Small- and medium-sized enterprises, competitiveness and its measurement, including the use of key performance indicators. Academic research relies on the prospection to retrieve the most relevant research studies and establishing links to authors from key international research groups. To facilitate this study, we used the Scopus and Web of Science databases research results, due to the significant number of indexed scientific articles. The extracted data were compiled and analysed through author’s networks using the statistical software Sci2 Tool, which supports temporal, geospatial, topical, and networks analysis. This study also attempts to point out the research trends and gaps in this area. Results obtained are illustrated by maps of author’s networks that reveal the main authors and research subject groups, thereby enhancing access to information from a scientific approach.Este estudio tiene como objetivo presentar un análisis cientométrico, basado en mapas de redes de autores, para determinar los autores más influyentes y relevantes con trabajos publicados sobre el tema Pequeñas y medianas empresas, la competitividad y su medición, incluido el uso de indicadores clave de rendimiento. La investigación académica se basa en la prospección para recuperar los estudios de investigación más relevantes y establecer vínculos con autores de grupos de investigación internacionales clave. Para facilitar este estudio, utilizamos los resultados de la investigación de las bases de datos Scopus y Web of Science, debido a su número significativo de artículos científicos indexados. Los datos extraídos fueron compilados y analizados a través de redes de autores utilizando el software estadístico Sci2 Tool, que es compatible con el análisis temporal, geoespacial, tópico y de redes. Este estudio también intenta señalar las tendencias de investigación y las brechas en esta área. Los resultados obtenidos se ilustran mediante mapas de redes de autores, que revelan los principales autores y grupos de temas de investigación, mejorando así el acceso a la información de una manera científica

MOONA software for survey classification and evaluation of criteria to support decision-making for properties portfolio

The MOORA for Neural Networks Analysis (MONNA) software was created to classify variables and evaluate the degree of correlation between them, helping to choose a property portfolio and facilitating decision making involving multiple criteria. The MONNA software presents the classification of the alternatives calculated automatically by the MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) and provides a Global Average Rate (GAR). Artificial Neural Networks (ANNs) analysis provides the degree of correlation between variables and uses GAR as the output parameter. The degree of correlation between the variables allows us to assess whether these variables are dependent on each other and can capture customer preferences. For the application we used a survey that sought to know the preferences of customers, which will serve to make the decision of which properties should be part of the company’s portfolio. The contribution and originality of the MONNA software is that through the integration of the MOORA and ANN methods, the classification and criterion evaluation calculations are faster and standardized. The use of software by decision makers helps to have more accurately find and classify available options, preventing simulations from being done by iterative processes and providing validated numerical data for management evaluation

Competitiveness of Food Industry in the Era of Digital Transformation towards Agriculture 4.0

Industry 4.0 and its technologies can potentially increase business competitiveness in the age of digital transformation through the implementation of its technologies. These digital technologies are increasingly present in the food industry, characterizing the concept of Agriculture 4.0. This digital transformation is a reality; however, it is unclear which digital technologies are most useful for each industry sector. Therefore, this paper seeks to explore the current state of implementation of digital technologies in different industrial sectors and which digital technologies should be leveraged to increase the performance of the agribusiness system. To do so, we used secondary data from a large-scale survey of 28 industrial sectors, representing 2225 companies in the Brazilian industry. Analyzing the different industrial sectors allowed us to present a framework of digital transformation to boost food industry competitiveness towards Agriculture 4.0. The results show that the food industry usually uses only one digital technology, showing the need for simultaneous and joint investments in the other technologies presented in this research. Public policies must be directed to encourage the expansion of digital technologies in the food industry

How E-learning Is Correlated with Competitiveness and Innovation and Critical Success Factors

E-learning has gained a prominent role in the education scenario, either because of its capacity for extraterritorial coverage, or because of the scale it offers for free and academic courses. How e-learning is being managed and identifying opportunities for improvement in this process is a challenge for managers. A systematic review of the literature on e-learning was carried out from the perspective of process management. The Scimat and VOSviewer software were used together to make it possible to understand the volume of publications, terms, density, and perspectives of studies on the subject on the indexing platforms, as well as pointing out challenges and trends in the area. The term e-learning does not appear as a trend driver in published articles. When related to the terms critical success factors (CSFs) and competitiveness and innovation, the greatest concentration of articles is directed to the e-learning infrastructure or technologies applied to it. As a result, it is possible to observe that the co-occurrence of e-learning with critical success factors and competitiveness and innovation is in its early stages, with scarce research in this area indicating room for future growth. E-learning entails unique business metrics that require specific tools and knowledge of technology, concepts, and the organizational environment. However, there is a dearth of publications addressing these aspects and proposing relevant methodologies and processes

O ciclo da produção de inteligência como apoio à estratégia de tomada de decisão organizacional

Many authors claim that to cope with rapid changes in social and productive environments, and especially for responding quickly to customer demands and / or users in an organization, information is needed and its management. Decision-making processes also take into account only the past experiences, and this model no longer meets the precepts of today's corporate world, considering the speed with which the market and competition are in search of improvement. Centered on the concepts of Competitive Intelligence and Artificial Intelligence, this article aims to show the importance of information processing, focusing on the processes of decision making and provide a model for their organization and storage. The findings point to the use of intelligent systems, contributing to improved decision-making process and seeking to obtain answers with high quality standards relating to market demand.Muitos autores afirmam que para enfrentar mudanças aceleradas no ambiente produtivo e social e, principalmente, para responder com agilidade às demandas dos clientes e/ou usuários de uma organização, são necessárias informações e seu gerenciamento. Processos de tomada de decisão ainda levam em conta apenas experiências passadas, sendo que este modelo não atende mais aos preceitos do mundo corporativo atual, visto a velocidade com que o mercado e a concorrência estão na busca de aperfeiçoamento. Centrado nos conceitos de Inteligência Competitiva e Inteligência Artificial, este artigo tem como objetivo mostrar a importância do tratamento das informações, focando os processos de tomadas de decisões, e apresentar um modelo para sua organização e armazenamento. As conclusões apontam para a utilização de sistemas inteligentes, contribuindo na melhoria do processo de tomada de decisão e objetivando obter respostas com alto padrão de qualidade referente às demandas do mercado