Abstract
Circular Economy business models rely on complex data exchange between organizations, which require a supporting digital infrastructure facilitating the circularity-related processes. In a digital platform context, value is generated not by the underlying technologies but by its allied ecosystem: community, users, developers, and integrated applications. These ecosystems come with an intrinsically complex interorganisational structure often overlooked during the development phase, leading to low platform adoption and obsolete platforms in the mid to long-term. Developed through a combined action-research and design science research approach, we propose a framework to support the design and deployment of circular economy ecosystems from a sociotechnical perspective, including practices from the requirements engineering, circular innovation ecosystems and digital platforms literature. © 2023, IFIP International Federation for Information Processing.

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. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
Digital Twin (DT) is recognized as a key enabling technology of Industry 4.0 and 5.0 and can be used in collaborative networks formed to fulfillment of complex tasks of the manufacturing industry. In the last years, the variety and complexity of DTs have been significantly increasing with new technologies and smarter solutions. The current definition of DT, such as cognitive, hybrid, and others, embraces a wide range of solutions with different aspects. In this sense, this article discusses DT definitions and presents a five-dimensional analytical framework to classify the different proposals. Finally, to better understand the proposal, we analyzed 12 articles using the analytical framework. We argue this research may help researchers and practitioners to better understand digital twins and compare different solutions. © 2023, IFIP International Federation for Information Processing.