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.

This paper is meant to presents a concrete methodology to integrate the vendor rating into a Request for Quotation process carried out in a sharing economy context where MaaS digital platforms are exploited. After an analysis of the current state of the art of sustainable decision making into vendors’ selection and simplified LCA techniques, the different steps of the methodology are in depth illustrated: the Resource instantiation, the Sustainability Suppliers Ranking and the Quotation-specific impacts calculation. The methodology deployment in the manufacturing context is then described, focusing on the resource instantiation phase that allows a rapid, but rigorous environmental characterization of suppliers offer. The described path, developed and tested in the MANUSQUARE EU project, could be easily adapted to other digital platforms and further extended to different industrial sectors.

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.

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.