Abstract
This paper presents an innovative approach in supporting the operation of complex equipment. The concept was developed in the context of the KoBaS project (Knowledge Based Customized Services for Traditional Manufacturing Sectors Provided by a Network of High Tech SMEs) whose main objectives are the development of new knowledge based tools for an intelligent use and management of more sophisticated manufacturing machines; and the creation of an innovative extended network of high-tech SMEs for use, customize, support and make business out of the new development tools. As a concrete example the paper presents a KoBaS solution based on the machine Maintenance. This solution allows one to know the machine condition, to detect and diagnose machine failures, to manage ordinary and extraordinary maintenance plans and maintenance work orders and to provide training support for maintenance interventions.

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
Make-to-order (MTO) supply chains (SC), common in aerospace industries, are very complex compared to mass production chains. Moreover, one of the significant long-term challenges is sustainability performance across its entire supply chain. However, this is not an easy task given the complexity involved, so decision support tools are fundamental instruments in understanding the entire supply chain from a sustainability performance perspective. Using simulation as a research method, this article proposes a hybrid and hierarchical performance assessment model, considering key sustainability indicators. The model is hybrid due to the use of different simulation methods- System Dynamics (SD), Discrete Event Simulation (DES), and Agent-based simulation (ABS). The model is applied to an aerospace manufacturer's real case to assess the sustainability performance of alternative SC. Unlike existing contributions, this work addresses the make-to-order supply chain from a sustainability performance perspective and does not compromise the representation of the diverse manufacturing functions and resources.

Abstract
This study aimed to identify how service operations managers perceive the effects of task duration variability and activity pooling on key performance indicators such as flow time, queue length, perceived service quality, and customer satisfaction. A scenario-based experiment was conducted with 229 professionals working in service operations in Brazil and Portugal. Participants evaluated fictional processes with varying levels of variability (low vs. high) and task allocation formats (specialized vs. pooled). All scenarios were validated through computer simulations prior to the experiment. The results reveal a gap between analytical models in the literature and managerial perceptions. While queuing theory associates increased variability with performance deterioration, respondents frequently attributed positive effects to higher variability and activity pooling, especially in relation to perceived quality. The study contributes by uncovering managerial interpretations that diverge from established operations management principles, suggesting the need for greater integration between analytical approaches and service-oriented perspectives. From a practical standpoint, the findings underscore the importance of strengthening managerial training in process analysis and promoting the use of computational tools as support for decision-making in complex service operations.