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 work seeks to determine the criteria and methods used in the problem of selecting suppliers, thus contributing to the support of entities wishing to start a selection of suppliers more effectively. To achieve these objectives, an analysis was performed of articles that make the literature review of the methods and criteria from the year 1985 to the year 2012. With the data obtained from these reviews, it was possible to verify which are the three main methods used over the years, namely data envelopment analysis (DEA), Analytical hierarchy process (AHP) and Fuzzy set theory and the main criteria used in the selection of suppliers. In this work, we present an overview of the decision making and the methods used in multi-criteria decision making. It’s tackled the problem of supplier selection, the process of selection and the reviews of literary methods and criteria used in recent years. Finally is presented the contribution to the selection of suppliers of the study conducted during the development of this dissertation, being presented and explained the main methods of selection of suppliers as well as the criteria used.

Abstract
The problem of selecting suppliers/partners is a crucial and important part in the process of decision making for companies that intend to perform competitively in their area of activity. The selection of supplier/partner is a time and resource-consuming task that involves data collection and a careful analysis of the factors that can positively or negatively influence the choice. Nevertheless it is a critical process that affects significantly the operational performance of each company. In this work, trough the literature review, there were identified five broad suppliers selection criteria: Quality, Financial, Synergies, Cost, and Production System. Within these criteria, it was also included five sub-criteria. Thereafter, a survey was elaborated and companies were contacted in order to answer which factors have more relevance in their decisions to choose the suppliers. Interpreted the results and processed the data, it was adopted a model of linear weighting to reflect the importance of each factor. The model has a hierarchical structure and can be applied with the Analytic Hierarchy Process (AHP) method or Simple Multi-Attribute Rating Technique (SMART). The result of the research undertaken by the authors is a reference model that represents a decision making support for the suppliers/partners selection process.

Abstract
Companies that faces transport challenges and require logistics efficiency need to implement solutions based on automation, provided in Industry 4.0. Intelligent Logistics Management, satisfying the demanding requirements of the customer, using new technologies and advanced tools to control and to act along the supply chain are mandatory. To address this topic, this paper presents an application of an Automated Intelligent Vehicle.

Abstract
This project aimed to analyze the practical difficulties in storing and moving a high range of products making the process easier and more efficient. The objectives were to improve the use of available space in the raw materials warehouse and ensure the effectiveness, efficiency and reduction of the periods between the transportation from raw materials to production lines. The work developed focused on improving internal processes, storage practices and internal transportation. In fact, for the production flow to be continuous, the supply must be aligned with the production to ensure that there is no failure of materials.