Paulo Ávila

Abstract
This project focuses on the development and implementation of a Mini Learning Factory (Mini LF) 5.0, aligned with the principles of Industry 5.0, Cyber-Physical Systems (CPS), and Open Design. Industry 5.0 emphasizes human-centric innovation, fostering collaboration between humans and machines while promoting sustainability. CPS facilitates the integration of the physical and digital realms, enabling more agile and flexible production processes. Open Design plays a pivotal role by encouraging collaborative participation, transparency, and the democratization of knowledge, which leads to more personalized and sustainable solutions in product and service design. The research adopts the Design Science Research (DSR) methodology, involving problem identification, artifact development, evaluation, and iterative improvement. The goal is to create a replicable, low-cost training environment that equips students with practical skills in line with Industry 5.0's requirements. The Mini LF 5.0 also aims to explore new methods for human-machine interaction, collaborative communication, and sustainable production, while ensuring the technical and financial viability of the project for wider adoption. © 2025 The Authors.

Abstract
Effective project management is crucial to the success of any industry, particularly in metalworking, where deadlines, resources, and costs play critical roles. However, accurately predicting project execution times remains a significant challenge, directly impacting companies' competitiveness and profitability. In this context, the integration of Artificial Intelligence (AI) tools emerges as a promising solution to improve the accuracy of time predictions and optimise project management in the metal-working industry.AI, particularly through techniques such as Machine Learning (ML), has demonstrated significant potential in predicting timeframes for engineering projects. Predictive activity-based models can be trained with historical data to identify patterns and forecast future durations with high accuracy. In the metalworking sector, where projects are often complex and subject to variability, AI can provide notable advantages in terms of precision and efficiency.This study aims to formulate an activity-based model, represented in IDEF0 (part of the Integration Definition for Function Modelling), for predicting activity durations using AI to support project management in the metalworking industry. By applying the principles of the IDEF0 tool, the objective is to develop a robust and adaptable system capable of analysing historical data, environmental factors, project characteristics, and other relevant inputs to produce more accurate time forecasts.With this work, we aim to contribute to the advancement of Project Management (PM) in the metal-working industry, particularly by providing an activity-based model to support the creation of an innovative AI tool for predicting execution times with greater accuracy.

Abstract
This paper compares the performance of Simulated Annealing and Tabu Search meta-heuristics in addressing a parallel machine scheduling problem aimed at minimizing weighted earliness, tardiness, total flowtime, and machine deterioration costs-a multi-objective optimization problem. The problem is transformed into a single-objective problem using weighting and weighting relative distance methods. Four scenarios, varying in the number of jobs and machines, are created to evaluate these metaheuristics. Computational experiments indicate that Simulated Annealing consistently yields superior solutions compared to Tabu Search in scenarios with lower dimensions despite longer run times. Conversely, Tabu Search performs better in higher-dimensional scenarios. Furthermore, it is observed that solutions generated by different weighting methods exhibit similar performance. © 2025 The Author(s).

Abstract
In this article it is performed an in-depth literature review of simulation in Industry 4.0. This paper intends to evaluate the use of simulation tools, focusing on its developments in Industry 4.0. In the first part a literature review was executed on the significance of simulation tools, such as Digital Twins, Discrete Event Simulation and Agent-Based Modelling, throughout several fields of study, giving special attention to manufacturing and production. A bibliometric analysis is also performed to understand the growth in number of articles published on simulation in Industry 4.0. Then, two case studies are presented, conveying the effectiveness of Digital Twins and Discrete Event Simulation in assisting process planning and control, and manufacturing automation. A few more case studies are also briefly referred in order to reinforce this point. At the end, a discussion about the future of simulation, its applications and advantages in industry settings is held, finally presenting the final thoughts, predicting an exponential growth in simulation uses, establishing this tool as a pillar of industrial production in the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This article presents a comprehensive evaluation of the recent development in Enterprise Resource Planning (ERP) Systems. Through a detailed review of current literature and case study analysis, it explores the benefits, challenges, and trends related to the implementation and use of ERP systems in business environments, as well as the key factors influencing the success or failure of ERP projects in the context of Industry 4.0. Furthermore, it highlights the practical and strategic implications of using ERP systems to improve operational efficiency, decision-making, and competitiveness in the global market. This article contributes to a deeper understanding of the role of ERP systems in business management and promotes digital transformation within organizations. In the future, paths like Artificial Intelligence, sustainability, mobile ERP, and customization will enhance the efficiency and adaptability of ERPs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.