As a result of the worldwide depletion of natural resources, increased energy use, and environmental, economic, and social imbalance, organizations are working to identify the proper strategies supporting the continuous reduction of their impacts. While this trend is fundamentally agreed upon in the literature, several manufacturing industries still fail to identify which elements most influence their contributions to the impact of sustainability and how to easily manage the calculation of these effects within a manufacturing system. The purpose of this article is to incorporate sustainability practices into manufacturing by developing a set of key performance indicators (KPIs) for assessing and improving environmental and economic management practices at the corporate and production level. The definition of the framework began with in-depth research of the leading indicators and framework types in the literature, integrating the most exploited industrial standards to make them easily acceptable in the industrial domain. Then, to provide a broad view of company behavior, the framework has been designed to take either an inventory and impact point of view, thus providing indicators for the online monitoring of the company operations, or assessing their impacts in an LCA-LCC perspective. In selecting the indicators and the definition of the framework structure, five industrial cases covering different business sectors were involved in identifying the most critical indicators in terms of calculability and defining a structure that would allow for their application in various business situations. Therefore, the defined framework has been validated at a conceptual level, thus laying the basis for future quantitative validation. Twenty key performance indicators (KPIs) for assessing the sustainability of manufacturing firms have been created based on the 163 indicators studied.

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.

This study presents a cradle-to-gate Life Cycle Assessment (LCA) of silk manufacturing across the Eurasian value chain, covering yarn-dyed, open-width, and printed fabrics. Based on foreground data collected from Chinese companies and thirteen Italian manufacturers in the Como silk district, the analysis was performed in OpenLCA using CML 2001, ReCiPe Endpoint and Midpoint, and USEtox, with background data from Ecoinvent v3.8. The study compares dry and fresh cocoon use in silk reeling and examines the environmental profiles of the three fabric routes. Results show that cocoon reeling is the main environmental hotspot, while yarn-dyeing, fabric dyeing, and printing also contribute significantly, especially through water and chemical consumption. The comparison highlights both common patterns and route-specific differences. The findings provide a baseline for environmental improvement in silk manufacturing and support future harmonization efforts in environmental labelling, certification, and PCR-aligned assessment.

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.