Paulo Ávila

Abstract
The concept of Industry 4.0 promises to transversally revolutionise industries. Simulation, as one of the main pillars of Industry 4.0, allows improvements in the organisational and production processes of companies. This research work develops a decision support tool based on system dynamics, that address the problem of car dealership sales forecast and evolution depending on the commercial strategies adopted. This decision support tool considers main variables that are expected to influence car sales in Portugal. To develop this tool several interviews were conducted with the people responsible for the commercial sector of different dealerships while considering existing literature on the subject. This allowed us to parameterize a system dynamics model with the most influential sales factors. The developed tool is expected to contribute to car dealerships to evaluate their commercial policies and define adjustments to these to improve profitability. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Demand Driven Material Requirements Planning (DDMRP) assumes that a production order is generated for replenishment when the inventory position, given by the net flow equation, is below a given level. Literature on this production planning and control system suggests prioritizing open orders on the shop floor based on the inventory buffer status. However, the performance of buffer-oriented priority dispatching largely remains unknown. Using discrete event simulation, this study suggests that buffer-oriented dispatching based on the net flow equation outperforms due date-oriented dispatching rules and first-come-first-served. The performance impact depends, however, on the reorder quantity associated with the production orders. These results have important implications for industrial practice.

Abstract
Total evacuation time constitutes an important factor in the safety of any building. It is thus essential to devise an emergency evacuation plan, which will enable the safe evacuation of all the occupants in the shortest possible time. The main objective of this article was to examine and improve the evacuation process of a 4-star hotel located in the city of Porto, Portugal. To this end, one looked into 6 scenarios, by means of PathFinder simulation software, so as to determine the shortest total evacuation time and identify possible bottlenecks and congestion. The simulation model developed was tested to analyze the evacuation of 429 people from the hotel, based on the availability of the 3 accessible exit doors (central exit, side exit, spa exit) and elevators. Strategy 4 presented the shortest total evacuation time, with 536.0 s. Two other strategies which showed very similar times were 5 and 6, with 537.0 s and 537.5 s, respectively.

Abstract
The digitalization and evolution of information technologies within the industry 4.0 have allowed the creation of the virtual model of the production system, called Digital Twin, with the capacity to simulate different scenarios, providing support for better decision-making. This tool not only represents a virtual copy of the physical world that obtains information about the state of the value chain but also illustrates a system capable of changing the development of productive activity towards personalized production, extending product versatility. Decentralized production seeks to respond to these needs because it allows the agglomeration of several services with different geographic locations, promoting the sharing of resources. This paper proposes an architecture for the development of a digital platform of personalization and decentralization of production based on sharing of sustainable resources. With a single tool, it is possible to define the entire production line for a product.

Abstract
Industry 4.0 brought modernization to the productive system through the network integration of the constituent entities that, combined with the evolution of information technologies, allowed an increase in productivity, product quality, production cost optimization, and product customization to customer needs. In this paper a model was created using the open-source tool Knime that, based on a set of data provided by Bosch, parameterized the model with several pre-processing techniques, resource selection, and minimization of over-fitting, allowing the development of a final improved model for internal product failure prediction at Bosch production line. The study shows that model efficiency improved with the application of resource selection and reduction techniques, with Logistic Regression and PCA resource selection techniques standing out, obtaining a Recall of 100% and precision and accuracy, both with 99.43%.