A Comprehensive Framework for Circular Economy, Environmental, and Social Sustainability Indicators in Manufacturing

Manufacturing systems face growing pressure to address circularity, environmental and social sustainability, yet existing assessment approaches remain fragmented and unevenly developed across these dimensions. Moreover, the choice and relative importance of indicators are often based on ad-hoc judgement rather than systematic empirical input from experts. This study proposes a structured, expert-informed framework for the identification and prioritisation of circular economy, environmental, and social sustainability indicators for manufacturing applications. A structured literature review identified 1,082 candidate indicators, which were systematically screened and refined to 51 circularity, 32 environmental and 40 social indicators, subsequently organised into 9, 9, and 7 thematic categories, respectively. Indicator categories were prioritised within each sustainability pillar using the Bayesian Best–Worst Method, implemented in a Python-based analytical environment, based on input from 16 manufacturing experts from academia and industry. Results indicate that product design and adaptability, climate change and labour standards emerge as the highest-priority categories within the circularity, environmental and social pillars, respectively, while governance-related and environmental–economic trade-off categories receive consistently lower weights. These findings demonstrate how expert-informed prioritisation can clarify dominant concerns and underrepresented dimensions within each sustainability pillar. The Bayesian Best-Worst Method implementation further enables the explicit treatment of uncertainty in expert judgements, revealing both areas of strong consensus and dimensions where priorities remain ambiguous or weakly distinguished by experts. Overall, the study advances sustainability assessment in manufacturing by providing a comprehensive, transparent and actionable framework for pillar-specific prioritisation, supporting consistent indicator selection and evidence-based decision-making across future manufacturing applications.