Abstract

Abstract
Nowadays, business models play a key role in competitiveness. Each industry has specific needs regarding the customization of their business models. Through a personalized business model, organizations can enjoy a customized mapping of all the business activities. In the machinery industry domain and specifically producers of integrated Products and Services, the need for a customized business model has been growing due to the specifications of the industry. The existing business models do not satisfy the capital goods companies’ needs, therefore a study was conducted to analyze and understand companies’ specifications, the existing supporting business frameworks to further proceed with the creation of a new methodology and framework that supports the businesses of this specific industry.

Abstract
This paper focuses on the drivers, curriculum and Project-Based Learning (PBL) learning strategies applied to the Business Process Modelling course, part of the Master in Services Engineering and Management (MESG), while presenting critical reflections on said course. The curriculum unit aims to develop skills that we consider essential in the analysis, design, management and improvement of processes that support the services provided by an organisation to its customers. Since the creation of the course, the main objective has been to motivate students to look into exploratory approaches to address specific challenges. In this sense, the PBL approaches explored have proved to be quite successful. Students are organised into larger teams and asked to come up with an innovative business idea. Then, they ought to carry out a project focused on the analysis and design of the business processes of the organisation/company, as well as specifying the respective supporting technological elements. The project, carried out as a team, is of medium/high complexity and long duration (throughout the semester). Each team is encouraged to use appropriate digital tools to support the collaborative work, namely, to facilitate information sharing, activity coordination, documentation management and communication. In this paper, we focus on the implementation and evaluation of the PBL practice, as well as on the analysis and consideration of the lecturers and students’ experience. We’ve adopted a cooperative and student-centred teaching and learning strategy since the beginning, in order to provide the right conditions to put into effect the skills of "doing" and "learning", without neglecting “knowledge”. Accordingly, we point out the main challenges, the lessons learned and the future views regarding the PBL practice.

Abstract
Complex systems, characterised by high interconnectedness and unpredictability, demand structured approaches to support decision-making, system integration, and operational efficiency. This study aims to develop a comprehensive understanding of these systems to facilitate the implementation of Digital Twins (DTs) in Complex Operations Environments (COEs). A Systematic Literature Review (SLR), following PRISMA methodology, is conducted and complemented by a grounded theory approach to identify, organise, and synthesise system characteristics. The result is a hierarchical conceptualisation comprising eight core categories that capture the essential dimensions of complexity, including: Process and System Interoperability, Human Relationships, Behaviour/Nature, Products and Services, Multi-processes, Performance, System Structure, and Management and Skill Requirements. This framework offers a structured foundation for designing and implementing DT-based decision support in COEs.

Abstract
This paper advocates for Digital Twin (DT) technology as a pivotal solution to address the complexities of Complex Operations Environments (COEs). Recognizing the need for a thorough understanding of COEs and their DTs, a methodology is introduced to bridge existing gaps. Given the lack of a universal definition, the approach leverages the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Latent Dirichlet Allocation (LDA) to extract insights, facilitating the development of a comprehensive definition for COE and DT. The methodology integrates Ontology and Systems Modelling Language (SysML) to provide a semantic and conceptual model of COE and DT. Ontology enriches the semantic understanding, exploring existence and entity relationships, while SysML ensures clear and concise communication through standardized graphical representation. This paper aims to present a methodology to achieve a precise understanding of COEs and their corresponding DTs, providing a robust foundation for addressing operational complexities in dynamic environments.

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.