Abstract
Industry 5.0 complements the Industry 4.0 approach by enabling the transition of industry digitization to a sustainable, human-centered and resilient paradigm. This paper delves into the exploration of enabling technologies that facilitate both Industry 4.0 and Industry 5.0 in the context of supporting supply chain (SC) logistics. The paper defines the principles of Logistics 5.0, which focuses on smart logistics systems for customized distribution, transportation, inventory management and warehousing by emphasizing interconnectivity, digitization, and optimization across SC operations. The traditional logistics framework requires innovative solutions grounded in emerging Industry 5.0 technologies capable of capturing and processing extensive datasets to empower decision-making based on information and knowledge. A comprehensive research has enabled to critically analyze enabling Industry 5.0 technologies by assessing their application status through real-case scenarios within SC Logistics 5.0. Furthermore, the paper identifies research gaps in the reviewed technologies by outlining promising areas for each Industry 4.0 technology. This guidance aims to direct future studies toward the practical application of technologies in supporting Logistics 5.0.

Abstract
Some of the fundamental activities of the software development process relate to the discipline of Requirements Engineering. Their objectives are to discover, analyze, document, verify and manage the requirements that will be part of the software. The requirements are the system's characteristics, which are identified based on information provided by users or experts in the field. The effective management of this information is essential to ensure that the system meets the needs of those who will use it. Considering that the requirements are information, we carried out research to propose a framework contemplating a set of tools used by the information management to assist the requirements management process's activities. This article aims to present the FIRMa - Framework based on Information Management to Requirements Management, and the results of an evaluation carried out by a group of eighteen specialists in Requirements Engineering, who gave their opinion about the tools contemplated by the framework, as well as defined steps and the guidelines elaborated for its use. The evaluation results showed that FIRMa could contribute to the requirements management process's activities and increase the project's chances of success.

Abstract
Manufacturing industry is experiencing another revolution towards the digitalization of industrial processes. Different value chain actors must share specific and sensitive data according to business and data requirements. Digital architectures must ensure seamless and comprehensive communications between actors according to agreed-upon vocabularies. The digital representation of machines and other types of equipment, including crucial information about their static and dynamic operational data, is made possible by the ontological modelling of Asset Administration Shells (AAS), which is proposed in this paper as modular and semantically interoperable resources. These Cognitive Digital Twins are herein defined with de facto domain ontologies that model the semantics of the current operation, status and configurations of assets. This paper reports a proof-of-concept technical implementation that demonstrates an innovative digital architecture that connects and communicates active and modular Digital Twin of a machine in a bi-directional, connecting this asset to a digital manufacturing service provider.

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
Water is at the core of sustainable development, and its use for human activities, including vehicle washing, should be done in a sustainable way. There are several technical solutions for washing buses offering different performances, making it difficult to choose the one that best meets the requirements of each specific case. The literature on the topic hardly analyzes the choice of the best technical solution for washing buses and does not apply and compare the results of different multicriteria decision-making (MCDM) methods for the problem. The unique information available is from the different suppliers in the market. Whereby, this work intends to give a technical-scientific contribution to fulfill this gaps. Therefore, the main objectives of this work are (1) to select the best sustainable technical solutions for washing buses depending on the specific conditions for a case study and (2) to analyze how different multicriteria decision-making methods behave in the selection process. To achieve these objectives, the problem was approached as a case study in a public transport company in Portugal and the methodology followed the next steps: started with the identification of the different types of commercial technical solutions for washing buses; the company's experts selected four main criteria: water consumption, operating costs, quality of washing, and time spent; the criteria weights were determined using the fuzzy-AHP method; then four representative MCDM methods were selected, namely, AHP, ELECTRE, TOPSIS, and SMART; the ranks obtained for the four methods were compared; and a sensitivity analysis was performed. Considering the input data for the criteria and their weights, the results for all the methods showed that the best and the worst solution was the same, mobile portico with a brush and porticoes with three brushes, respectively. Furthermore, the results of the sensitivity analysis performed with disturbances for the weights of each criterion presented that the results are slightly affected and the similarity in rankings for the four MCDM methods was validated by Spearman's rank correlation coefficient (rs) and Kendall's coefficient of concordance (W). Considering these results, the SMART method, the less complex one, showed no difference from the others. For that reason, simple methods, such as SMART, in line with other works in the literature perform well in most cases. As a final remark of this work, it can be said that the methodology employed in this project can also be deemed applicable to other similar companies seeking technical solutions for bus or truck washing. Furthermore, the application of the SMART method, the less complex one and the most understandable for people, showed no difference from the others, being able to be applied in similar situations.

Abstract
The footwear industry is energy-intensive and, consequently, a source of large amounts of greenhouse gas emissions every year. Issues related to climate change and growing conflicts on a global scale that impact the prices of raw materials and energy prices have led companies in the footwear industry to take actions to mitigate these impacts. Among these actions is the growing focus on producing its energy from energy systems based on renewable sources and battery energy storage units. This paper addresses the energy-efficient manufacturing scheduling in footwear industries with onsite energy production from a photovoltaic system with batteries. The problem is formulated as a mixed integer linear programming problem. Different objectives are presented, depending on the priorities of the entity that owns the footwear factory, namely, minimizing operation costs, minimizing CO2 emissions, or both. The case study is footwear factory located in Portugal that uses a manufacturing process based on injection molding. The results show the effectiveness of the proposed approach, with active demand side management playing a fundamental role in shifting periods of higher energy consumption to periods of lower prices or lower CO2 emissions. Also, Pareto fronts are depicted to make the trade-off between CO2 emissions and operation costs. As expected, the reduction of CO2 emissions promotes an increase on operation costs. Furthermore, a sensitivity analysis is carried out on the increase in photovoltaic capacity and battery capacity. The results show that increasing photovoltaic and battery capacity promotes reductions in costs up to 30% and in the emissions up to 37%.