Systematic review of engineering simulation models for life cycle assessment

Manufacturing companies increasingly integrate the strive towards sustainability into product engineering to reduce resource consumption and environmental impacts. A substantial share of a product’s environmental performance is determined during engineering. Early assessments rely on generic data, which is gradually replaced by more concrete simulation and primary data as product maturity increases. The effective use of engineering simulation models for Life Cycle Assessment (LCA) therefore remains a central challenge under heterogeneous, distributed and rapidly evolving data. This article presents a systematic review of product engineering approaches for integrating simulation models into LCA. A seven-step research approach is applied to identify, categorize and evaluate existing approaches across CAx tools. Relevant simulation parameters for sustainability assessment, including material properties, process descriptors, consumables and waste streams, are identified and analyzed. Based on identified data characteristics, the potential of Data Science and Artificial Intelligence methods for LCA data preparation is assessed. A conceptual knowledge-graph-based decision support approach is derived to enable structured integration, traceability and reuse of simulation data for sustainability assessment. The approach is evaluated using criteria from literature. Structured integration of simulation data improves early-stage data quality and supports decision-making in sustainable product engineering.