Carla Pereira

Abstract
Industry 4.0 promotes a paradigm shift in the orchestration, oversight, and optimization of value chains across product and service life cycles. For instance, leveraging large-scale data from sensors and devices, coupled with Machine Learning techniques can enhance decision-making and facilitate various improvements in industrial settings, including predictive maintenance. However, ensuring data quality remains a significant challenge. Malfunctions in sensors or external factors such as electromagnetic interference have the potential to compromise data accuracy, thereby undermining confidence in related systems. Neglecting data quality not only compromises system outputs but also contributes to the proliferation of bad data, such as data duplication, inconsistencies, or inaccuracies. To consider these problems is crucial to fully explore the potential of data in Industry 4.0. This paper introduces an extensible system designed to ingest, organize, and monitor data generated by various sources, focusing on industrial settings. This system can serve as a foundation for enhancing intelligent processes and optimizing operations in smart manufacturing environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Considering the great challenge of implementing digital tools to improve collaboration in the value chain and promote the adoption of circularity strategies, as is the case with digital traceability tools and digital product passports. This paper presents an innovative proposal for implementing an industrial data sharing ecosystem, namely an architecture and platform for digital traceability between entities based on Data Spaces. To validate our proposal, a use case scenario was implemented as part of the BioShoes4All project.

Abstract
Although the concept of Circular Economy (CE) has become popular in recent years, the transition towards a CE system requires a change in consumers' behaviour. However, there is still limited knowledge of consumers' efforts in CE initiatives. The present paper aims to analyse and compare consumers' behaviour towards circular approaches and compare the results on items like generation and demographics. 495 answers were collected through a questionnaire from 3 countries (Albania, Poland, and Portugal). Data collected was analysed mainly through a Crosstabs analysis to identify associations or different behaviours regarding nationality, gender, generation, education, and place of residence. From the paper's findings, we can emphasise that residents of EU countries seem to be more aware of the concept of circular economy. However, price is still a very important factor for EU residents when it comes to deciding on a greener purchase. Albanians (non-EU residents) tend to take a more linear approach when it comes to purchasing a new product regardless of its cost. Regarding the Digital Product Passport, a tool proposed by the European Commission through its Circular Economy Action Plan, non-EU residents have a better understanding of the concept. This tool seems to be more relevant for Millennials and Generation X. Generation Z, i.e., the tech generation, does not show an overwhelming propensity for technological options, such as online buying and digital technologies for a greener society.

Abstract
This paper discusses the Digital Product Passport (DPP) as a key tool for achieving a circular economy. An architecture of the DPP is presented built upon the principles of data spaces and W3C Decentralized Identifiers (DIDs). By leveraging data spaces, the DPP enables secure and controlled data exchange among stakeholders, fostering transparency, traceability, and collaboration throughout the product's lifecycle. The use of decentralized identifiers ensures the uniqueness and verifiability of product-related information, facilitating seamless access and sharing of data. The DPP architecture offers a promising framework for realizing the circular economy by promoting resource efficiency, sustainable practices, and informed decision-making. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
Nowadays the concept of digitalization and Industry 4.0 is more and more important, and organizations must improve and adapt their processes and systems in order to keep up to date with the latest paradigm. In this context, there are multiple developed Maturity Models (MMs) to help companies on the processes of evaluating their digital maturity and defining a roadmap to achieve their full potential. However, this is a subject in constant evolution and most of the available MMs don't fill all the needs that a company might have in its transformation process. Thus, European Digital Innovation Hubs (EDIH) arose to support companies on the process of responding to digital challenges and becoming more competitive. Supported by the European Commission and the Digital Transformation Accelerator, they use tools to measure the digital maturity progress of their customers. This paper analyzes several MMs publicly available and compares them to the guidelines provided to the EDIH.