Abstract
European industrial value chains and manufacturing companies have recently faced critical challenges imposed by disruptive events related to the pandemic and associated social/political problems. Many European manufacturing industries have already recognized the importance of digitalization to increase manufacturing systems’ autonomy and, consequently, become more resilient to adapt to new contexts and environments. Augmented reality (AR) is one of the emerging technologies associated with the European Industry 5.0 initiative, responsible for increasing human-machine interactions, promoting resilience through decision-making, and flexibility to deal with variability and unexpected events. However, the application and benefits of AR in increasing manufacturing resilience are still poorly perceived by academia and by European Manufacturing companies. Thus, the purpose of this paper is to contribute to the state of the art by relating the application of AR with current industrial processes towards manufacturing systems resilience. In order to cope with this objective, the industrial resilience and augmented human worker concepts are first presented. Then, through an exploratory study involving different manufacturing companies, a list of relevant disruptive events is compiled, as well as a proposal with specific ideas and functionalities on how AR can be applied to address them. In conclusion, this research work highlights the importance of AR in coping mainly with disruptive events related to Human Workforce Management and Market/Sales Management. The AR application ideas shared a common thread of availability and delivery of information to the worker at the right time, place, and format, acting on the standardization and flexibility of the work to support manufacturing resilience.

Abstract
In today's volatile, uncertain, and complex business environments, manufacturing companies must not only adapt to market demands but also minimize the time between problem occurrence and resolution. The implementation of lean manufacturing systems has been crucial in this regard. However, traditional approaches have shown notable inefficiencies that can be effectively addressed through digitalization. By embracing digital solutions, manufacturing companies can ensure efficient continuous improvement, driving performance to higher levels. This study aims to find a digital solution for a specific company that faces daily challenges associated with low visibility into production. An investigation revealed that the Lean tools used by the company were outdated, directly affecting the generated information and consequently, decision-making. The integration of a Manufacturing Execution System into the factory's dynamics was the solution found. In this context, a step-by-step methodology is proposed to guide the implementation. As a result, a prototype of the system was designed. The validation of the system by end-users demonstrates the success of the proposed methodology.

Abstract
In the Fourth Industrial Revolution era, commonly known as Industry 4.0, the manufacturing industry is undergoing a profound transformation driven by the convergence of technological advancements. Industry 4.0 technologies are revolutionising how products are manufactured, from design to production to delivery. These technologies, such as collaborative robotics, digital twins, IoT, and data analytics, enable manufacturers to improve efficiency, productivity, and quality. As Industry 4.0 continues to evolve, the demand for skilled engineers who can effectively design, implement, and manage these sophisticated systems is growing rapidly. Future mechanical engineers must be prepared to navigate this complex and data-driven manufacturing landscape. To address this need, the Faculty of Engineering at the University of Porto developed a new course titled Smart Factories, specifically designed to equip master's students with the knowledge and skills necessary to thrive in the factories of the future. This course utilises an innovative, active experimental learning methodology with industry collaborations and a comprehensive curriculum to foster the development of the multidisciplinary skills necessary to excel in this rapidly evolving field. Through this comprehensive and innovative approach, the Smart Factories course aims to prepare future mechanical engineers to become leaders in smart manufacturing, driving innovation and shaping future factories.

Abstract
The water supply and sewage sector (WSS) is essential for promoting health and providing the population with drinking water and the adequate disposal of effluents. Assessing the evolution of performance in WSS allows for highlighting the best and worst results achieved, identifying benchmarks, and pinpointing sources of improvement for water services. Brazil has a large population and immense freshwater reserves that are unevenly distributed throughout the territory. This situation emanates a challenge that requires the efficient management of water resources. This study develops a composite indicator framework based on the robust Benefit-of-the-Doubt (BoD) approach to estimate the performance of municipalities of the Brazilian State of Santa Catarina from 2009 to 2021, considering financial, operational, and quality dimensions associated with the provision of WSS services. Subsequently, the Global Malmquist Index (GMI) is applied to assess the performance evolution of the municipalities over time. The BoD results enable the quantification of the relative contribution of each sub-indicator to the performance score, allowing the assessment of the strengths and weaknesses of each municipality. The GMI results show an average performance loss of 3.3% in Santa Catarina state and considerable variability among municipalities, with scores ranging from losses of 54.2% to gains of 109.3% in the period analysed.

Abstract
This paper studies a real world dedicated parallel machine scheduling problem with sequence dependent setups, different machine release dates and additional resources (PMSR). To solve this problem, two previously proposed models have been adapted and a novel objective function, the minimisation of the sum of the machine completion times, is proposed to reflect the real conditions of the manufacturing environment that motivates this work. One model follows the strip-packing approach and the other is time-indexed. The solutions obtained show that the new objective function provides a compact production schedule that allows the simultaneous minimisation of machine idle times and setup times. In conclusion, this study provides valuable insights into the effectiveness of different models for solving PMSR problems in real-world contexts and gives directions for future research in this area using complementary approaches such as matheuristics. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
In cutting processes, one of the strategies to reduce raw material waste is to generate leftovers that are large enough to return to stock for future use. The length of these leftovers is important since waste is expected to be minimal when cutting these objects in the future. However, in several situations, future demand is unknown and evaluating the best length for the leftovers is challenging. Furthermore, it may not be economically feasible to manage a stock of leftovers with multiple lengths that may not result in minimal waste when cut. In this paper, we approached the cutting stock problem with the possibility of generating leftovers as a two-stage stochastic program with recourse. We approximated the demand levels for the different items by employing a finite set of scenarios. Also, we modeled different decisions made before and after uncertainties were revealed. We proposed a mathematical model to represent this problem and developed a column generation approach to solve it. We ran computational experi-ments with randomly generated instances, considering a representative set of scenarios with a varying probability distribution. The results validated the efficiency of the proposed approach and allowed us to derive insights on the value of modeling and tackling uncertainty in this problem. Overall, the results showed that the cutting stock problem with usable leftovers benefits from a modeling approach based on sequential decision-making points and from explicitly considering uncertainty in the model and the solution method. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )