Abstract
Organizations can transform their businesses and create more value by adopting Industry 4.0 initiatives. During evaluating these projects, the decision-maker must assess significant uncertainties (risks) resulting from socio-technical, economic, and financial factors. One of the main objectives of this study was to identify the necessary building blocks to develop a framework for project implementation in high-risk scenarios, as in the case of Industry 4.0. A multi-criteria framework divided into three stages was proposed, integrating knowledge from Front-End-Innovation (FEI), Innovation Decision Process (IDP), Traditional Project Evaluation Methods, and Real Options Valuation (ROV). The first step is to identify an investment opportunity. The second step is the definition of a business model. The third step is the simulation of different implementation strategies to give managerial flexibility to decision-makers to decide the best strategy to mitigate risks. A real case study was used to test the framework. According to the results, managers can use this framework to create different project implementation scenarios and determine the best strategy to mitigate risks. However, we must still understand whether uncertainties behave discretely, dynamically, or both, the interactions between elements, and how to calculate them to improve our model.

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 manufacturing industry faces a new revolution, grounded on the intense digitalization of assets and industrial processes and the increasing computation capabilities imposed by the new data-driven digital architectures. This reality has been promoting the Digital Twin concept and its importance in the industrial companies' business models. However, with these new opportunities, also new threads may rise, mainly related to industrial data protection and sovereignty. Therefore, this research paper will demonstrate the International Data Spaces reference model's application to overcome these limitations. Following a pilot study with a Portuguese machine producer/maintainer enterprise, this paper will demonstrate the development of a cutting and bending machine Digital Twin, leveraged on an International Data Spaces infrastructure for interoperability, for the plastic and metal industry and its importance to introduce this machine manufacturing company in a new business-to-business marketplace from the EU project Market 4.0.

Abstract
Recently, we have been observing a significant evolution in products, machines, and manufacturing processes, towards a more digital and interoperable reality. In this sense, the power transformers sector has also been evolving to develop smart transformers for the future, capable of providing the digital capabilities to leverage new services and features that follow its entire life cycle, from the design and manufacturing to the use and dismantling/recycling. In this sense, this paper aims to present and demonstrate how an innovative digital twin platform can be used in a secure and trustable way for the enhancement of the power transformers’ performance and potential lifespan, enabling, at the same time, the promotion of new business models. A real use case is also presented to demonstrate the applicability of Asset Administration Shells (AAS) for power transformer life cycle management, as well as the use of the International Data Spaces (IDS) for the secure and trustable horizontal interoperability along with the different actors of the value chain, from the manufacturers to the power network and maintenance services companies.

Abstract
This paper aims to thoroughly discuss the use of Digital Twin technology in complex operations environments, highlighting its potential applications and the research challenges that need to be addressed. This is necessitated by the fact that currently there is no comprehensive literature review and framework for implementing Digital Twin technology in complex operations environments. Furthermore, existing interpretations of DT implementation are inadequately detailed and not very informative in this area. This may be a consequence of the difficulties of collecting and extracting useful information from data in real-time. Another drawback worth mentioning is that Digital twins at the moment center on an individual or isolated part instead of integrating the whole system and no current work talks about this holistic approach. This paper will focus on Digital Twins in complex operations environments and their applications. A review of scientific literature on the use of Digital Twins in complex operations environments is performed and the articles are categorized by the problems and challenges that they address requiring DT as a solution. A selection of papers that focus on this topic and represent the current situation of research will be emphasized. In conclusion, this work will be utilized as a baseline study to propose a Digital Twin reference framework, which eventually leads to implementing and evaluating a comprehensive Digital Twin methodology in complex systems. © 2023 IEEE.