Similarity Assessment of Manufacturing Processes Within and Across Sectors

Abstract

The Foundation Industries, namely cement, metals, glass, ceramics, paper, and chemicals, are vital to industrial economies but are also highly resource- and emissions-intensive. Existing sustainability benchmarking approaches often focus on the facility or sector level, which limits their granularity and flexibility needed for targeted, cross-sectoral improvement. This thesis addresses these gaps by developing tools and methods for process-level sustainability benchmarking, enabling transferable and data-driven decision-making. The research is structured around five objectives, beginning with a systematic review that highlights fragmentation in existing decision-support methods and their limited applicability to sectoral comparisons at the process level. A framework for selecting environmental Key Performance Indicators (KPIs) is then developed and applied to industrial data, which assists in consistent benchmarking of environmental performance. Additionally, shared process examination using an adapted Dependency Structure Matrix (DSM) identified common operations across sectors, revealing opportunities for coordinated interventions. These insights led to the development of Similarity-based Sustainability Assessment (SIMSA), a novel framework that integrates ratio- based normalisation, Monte Carlo-based Multi-Criteria Decision Analysis (MCDA) weighting, and similarity analysis for benchmarking processes both within and across sectors. The thesis contributes novel methodologies and practical tools, encouraging a shift toward process-centric, cross-sectoral benchmarking. This offers valuable insights for industry and researchers seeking to accelerate sustainable transitions in process manufacturing.