Discrete Event Simulation (DES) is one of the most important simulation techniques in decision-making in several areas. Some authors state that small and large companies can benefit significantly through the utilisation of DES. Literature shows that most DES studies carried out are at large companies. Studies carried out at small and medium-sized enterprises (SMEs) have been mostly explored on a one-off, caseby-case basis, given the characteristics and limitations of smaller companies. In order to expand DES studies in a wide range of companies, this article seeks to consider the SME characteristics and limitations prevent them from broadly adopting traditional ("hard") DES. Then an alternative application of DES in "soft" mode was proposed. A review of facilitated DES frameworks was carried out. The frameworks were critically analysed in this review in relation to their adequacy to specific SME requirements and similar contexts. Finally, some issues and characteristics of these frameworks are presented that make directly applying in these contexts difficult. Furthermore, some suggestions are proposed for developing future facilitated DES frameworks.
Paper aims: This paper presents a comparative evaluation of different forecasting methods using two artificial neural networks (Multilayer Perceptron network and Radial Basis Functions Neural Network) and the Gaussian process regression.Originality: Due to the current world scenario, solving economic problems has become extremely important. Artificial neural networks are one of the most promising tools to forecast economic trends and are being widely studied in economic analyses. Therefore, due to the concerns about the performance of different forecasting methods to solve economic problems, this study contributes with an example of the forecasting performance of artificial neural network models compared with Gaussian process regression using Nelson-Plosser and U.S. macroeconomic real-life data sets.Research method: Two real-life data sets were used to evaluate the forecasting methods proposed in this paper. These data sets were normalised to values between zero and one. After that, the data training was performed and, once it was built, a model was used to generate forecasts. Thus, observations were made to verify how accurately the fitted model forecast the values. Main findings:The results obtained from the study show that, for all forecasting horizons, multi-layer perceptron networks and Gaussian process regression models had the most satisfactory results. On the other hand, the radial basis functions neural network model was unsuitable for econometric data. Implications for theory and practice:This study contributes to a discussion about artificial neural networks and Gaussian process regression models for econometric forecasting. Although artificial neural networks are mainly used in economic analyses, the results showed that not all models, such as radial basis functions neural networks, present good results. In addition, the regression of the Gaussian process showed promising results to forecast econometric data.
Paper aims:To propose a framework to support online simulation studies considering facilitated modeling and concepts of modern industry context, such as agility and flexibility.Originality: Since the frameworks in the literature deal with simulation projects focused on healthcare and face-to-face meetings, the present work innovates by offering an agile and flexible guide for simulation projects in production systems, which also supports online interventions.Research method: Action Research method was used to develop the framework. After its development, the FaMoSim (Facilitated Modeling Simulation) framework was applied in a real case to evaluate its applicability. Main findings:In the application of FaMoSim, we achieved the framework's objectives: carrying out a faster (up to 3 months) and more flexible online modeling process; creating a simple computer model that does not require a complex data collection structure nor a specialist team; generating a better understanding of the process and assisting the stakeholders in identifying improvements.Implications for theory and practice: Considering some challenges that prevent the expansion of DES studies, the framework assists in expanding DES studies in environments where it is not widely used. The framework supports online interventions, making it an interesting tool that can be used mainly in times of social distancing.
This study presents a Systematic Literature Review on an agile project management tool. The study offers a brief comparison between traditional and agile project management methodologies. Their respective concepts and characteristics are laid out to highlight and explain their main differences. The agile methods include quantitative and qualitative data, showing Scrum framework characteristics. This study highlights the importance of project management in function of its emergence as a response to problems encountered during improperly conducted projects. Furthermore, this study provides relevant information for professionals in the Industrial Engineering area and computer science. The results allowed us to conclude that Scrum is an agile framework for empirical-based project development; it was developed in the 1990s by Jeff Sutherland. It is a flexible and adaptable methodology. Scrum research peaked in 2020, and continues to be studied, mainly in the field of computer science. Finally, Brazil is well-positioned in third place for works published.
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