A multi-criteria similarity-based framework for sustainability assessment of manufacturing processes in resource-intensive industries

Process industries such as metals, cement, and paper pose significant sustainability challenges due to their high resource demands and emissions. While benchmarking tools such as Best Available Techniques (BAT) help assess performance, existing approaches are often sector-specific, focused on isolated performance ranking, and influenced by subjective weighting assumptions. This work therefore shifts sustainability benchmarking from ranking-based evaluation to relational similarity-based analysis at the manufacturing-process level. This study introduces the Similarity-based Sustainability Assessment (SIMSA) framework to support transparent cross-sectoral benchmarking by comparing how unit processes relate to one another in sustainability terms. SIMSA integrates ratio-based normalisation relative to BAT values with structured similarity analysis using Relative Percentage Difference (RPD). Sustainability indicators are selected through a literature review and classified into the environmental, economic, and social pillars. A stochastic, simulation-based weighting and aggregation procedure, Optimisation-based Stochastic Weighting with TOPSIS (OSW-TOPSIS), is used to reduce reliance on directly elicited subjective preference weights. The framework is demonstrated through a case study comparing unit processes in the steel and cement industries. Results illustrate SIMSA’s ability to identify candidate transferable practices and distinguish between structural commonality and sustainability-performance similarity . This research advances transparent, relational benchmarking and supports the early-stage identification of candidate opportunities for cross-sector learning, industrial symbiosis screening , and sustainability optimisation.