Silvia Menato

Life Cycle Assessment (LCA) is a powerful methodology for evaluating the environmental impacts of products and processes across their entire life cycle. However, the quality of the data used in LCA is critical to ensuring accurate, reliable, and meaningful results. This paper provides an overview of how data quality is assessed in LCA studies, focusing on three key frameworks: the Product Environmental Footprint (PEF), the International Reference Life Cycle Data System (ILCD), and the Pedigree Matrix. These approaches evaluate data quality through different lenses, considering several key factors. While these frameworks offer valuable guidance, they are still plagued by some limitations, particularly in their ability to fully quantify uncertainty and provide actionable insights for data improvement. This paper highlights these shortcomings and discusses the limitations of the existing methods, particularly their reliance on qualitative assessments and the lack of integration with emerging digital technologies. In addressing these gaps, the paper proposes future interventions, including the development of a hybrid quality metric, and the adoption of digital technologies to enable robust data sharing. These advancements aim to enhance data transparency, reliability, and relevance, ultimately improving the credibility of LCA-based studies as a decision-making tool for sustainable development.

The transition to a circular economy is fundamental for promoting sustainable industrial practices, particularly in manufacturing. This paper introduces a structured methodology for assessing circularity in manufacturing companies through a multi-level indicator framework. Initially, a broad set of circularity indicators was analysed and refined through a systematic filtering process, applying criteria aligned with the RACER methodology. This selection led to the identification of three key macro indicators: Material Circularity Indicator (MCI), which evaluates the balance between linear and restorative material flows; Circularity Index (CI), which quantifies the effectiveness of material retention across the product lifecycle in terms of both quantity and quality conservation; and Value Capture, which measures the economic benefits retained through circular strategies such as remanufacturing, recycling, and refurbishment. The proposed framework provides a structured approach to evaluating circular loops within a product lifecycle, offering quantitative insights that support decision-making for manufacturers.

In recent decades, environmental challenges have become an increasingly global concern. The rapid advancement of technology and the growing demand for products and resources have fueled industrial growth but also exacerbated environmental issues. To identify key impact areas and enhance environmental performance, accurately quantifying the environmental footprint of various processes is essential. Life Cycle Assessment (LCA) is a widely recognized methodology for measuring and estimating these impacts. However, its application faces several challenges, including difficulties in collecting reliable supply chain data, inefficiencies in data distribution, privacy concerns that discourage companies from sharing information, and the absence of incentives for data sharing. This paper explores how a digital ecosystem, built around a multi-sided market, can facilitate the structured exchange of environmental life cycle data within a value chain. Specifically, it examines how blockchain technology can enhance transparency and efficiency in data tracking and sharing. Smart contracts can automate data validation and transaction execution, reducing the risk of fraud or manipulation while streamlining data transactions. Specifically, the study focuses on leveraging this technology to improve environmental impact data sharing through a purposebuilt marketplace designed to accelerate LCA calculations. The proposed framework is based on a decentralized platform that ensures secure data sharing and verification by utilizing a public infrastructure. The platform's architecture follows a modern three-tier design pattern, and the proposed infrastructure serves as a proof of concept.

Within a market in constant evolution, manufacturing companies are facing several challenges that are putting their businesses at risk. The convergence of technological advancements and a growing emphasis on sustainable manufacturing processes are significantly turning manufacturers' production standards upside down. In the context of the management of the machinery life cycle, transitioning to a circular approach becomes imperative to maximize the use of resources while reducing environmental impacts. A good compromise to encompass both a digital and a circular transition in the machinery sector can lie in the adoption of product life extension strategies. Due to the complexity of the machinery ecosystem value chain and the need for knowledge and tools enabling circular economy at each level of the supply chain, a tailor-made methodology is proposed, aimed at supporting companies looking to extend the machinery life cycle, emphasizing the importance of integrating digital technologies for sustainable and profitable change. The research methodology followed considered first an analysis of the traditional management model of machinery manufacturing ecosystem. This allowed to identify associated challenges linked to the modification of the machinery value chain when digitalization is integrated to reach sustainable and circular goals. An transition methodology (named in the article as Circular Transition Management - CTM - methodology) is introduced, that integrates digital technologies to support manufacturing companies in this circular transition. The methodology enables the matchmaking of digitalization requirements to the different stakeholders of the value chain, thus allowing a company to adopt a circular strategy for its business. Three business strategies are explored in order to extend the machinery life cycle, from integrating digital solutions within new equipment or providing add-on services, towards servitizing new machines. The proposed CTM methodology has been applied in an industrial case discussed in the article.

Digitalization technologies have been identified as enablers for the adoption of circular economy practices. The machinery-value chain addressed in this study is affected by the introduction of digital technologies that enable real-time monitoring of data on product condition and control optimization, the deployment of predictive analytics techniques, as well as offering circular-based services. Machinery-lifetime extension can be digitally enabled on both old and new machines. The research objectives were to investigate how digital technologies enable the adoption of circular economy-based business models by manufacturing companies and provide answers regarding (i) which Life Cycle Extension Strategy is suitable for digital circular-business model adoption and (ii) how digitalization of machines enables manufacturing companies to innovate their business models. The correlation matrix is the tool developed from the proposed approach and it aims to support manufacturers in their first contact with circular business models. In the European RECLAIM project context, two manufacturers have applied the approach. The next steps are expected to introduce quantitative indicators to define thresholds for the steps toward circularity without replacing the qualitative approach, as this guarantees its applicability in a context that has never considered circularity yet.

Digitalization technologies have been identified as enablers for the adoption of Circular Economy practices. The machinery value chain, addressed in this study, is affected by the introduction of digital technologies that enable real-time monitoring data on product condition and control optimization deploying predictive analytics techniques, as well as the offerings of circular-based services. Machinery lifetime extension can be digitally enabled on both old and new machines. The research objectives were to investigate how digital technologies enable the adoption of circular economy-based business models in manufacturing companies and provide an answer about i) what Life Cycle Extension Strategy is suitable for digital circular business model adoption and ii) how digitalization of machines enables manufacturing companies to innovate their business model. The correlation matrix is the tool developed from the proposed approach; it aims at supporting manufacturers in the very first contact with circular business models. In the context of the European RECLAIM project, two manufacturers apply the approach, proving its validity and wide applicability. The next steps are expected to introduce quantitative indicators to define thresholds for the steps toward circularity, without replacing the qualitative approach, as it guarantees the wide applicability of the approach in context that never considered circularity yet.

The adoption of Cyber Physical System (CPS) technologies at European level is constrained by a still emerging value chain and by the challenging transformation of manufacturing processes and business ecosystems that their deployment requires. This issue becomes even more challenging when the concept of CPS is exploited to propose cyber-physical machines and manufacturing systems, where the complexity of the controlling intelligence and of the digital counterpart explodes. As a matter of fact, the market behind CPS has a potential that is still scarcely supported by methodologies and tools able to foster the rise of a solid ecosystem required for a relevant market uptake. Multi-sided platforms (MSPs) have demonstrated to play the pivotal role of providing the environments and the technological infrastructures able to match make the needs of manifold user insisting on them. The manufacturing sector did not remain untouched by this trend and moves its first step towards the integration of platform logics across value networks: the CPS business ecosystem is one of those.