Sustainable supply chain design can be performed using optimisation strategies for minimising environmental impacts while maximising profit. It is not clear how such strategies influence the resilience of a supply chain - its ability to handle disturbances without compromising its function. This research used the ecosystem network analysis (ENA) to evaluate the resilience during the design of a sugar beet supply chain. The ε-constraint method was used to solve a multi-objective, mixed integer linear programming (MOMILP) model. Results showed that ENA results are compromised when the strategy of minimising environmental impacts is used, due to the increased fragility of the configuration, compared with the configuration from the profit maximisation strategy. Sensitivity analysis also revealed that, when the number of facilities is increased, ENA results improve while profit is decreased. ENA showed an interesting potential to support the pursuit of balance between resilience and performance during early design stages.

Purpose: to propose two enhancements for the European Union’s Circular Material Use rate (CMU): inclusion of Preparation for Reuse (PfR) flows and enhanced reproducibility across lower levels of analysis. Methods: PfR flows are added to the material flow Sankey Diagram. The Local Circularity Rate (LCR) is based in the CMU and is broke down in three waste-related ratios: recovered-to-treated (RCV-to-TRT), treated-to-end-of-life and end-of-life-to-overall-material-use (EoL-to-OMU). LCR, CMU and CMU’, an alternate version of CMU, are computed and compared in the macro-level for EU27 member states and in the meso-level for Germany’s sixteen states. LCR is computed and broke down for regions in Belgium, The Netherlands and Greece. In the micro-level, LCR is computed for a network modelled around a Textile Sorting Centre (TSC) in Amsterdam. Results: LCR showed closer average results to CMU in comparison to CMU’. Considering RCV-to-TRT and EoL-to-OMU, The Netherlands and Luxembourg are the best performing countries in the EU27. Eight countries performed worse than 0.4 in both ratios. In total, twelve German regions showed negative results, either for CMU or CMU’. Saxony-Anhalt is the most circular region in Germany, while Berlin is the less circular. The Amsterdam textiles’ network features an LCR of 12%, with the TSC contributing to 63% of all textiles recovered. Conclusion: The revised circular Sankey Diagram comprehensively illustrates the circularity gap. LCR’s three ratios enhances in-depth analysis, allowing better prioritisation of public policies. Limitations remain in data availability and harmonisation across regional and national databases. Graphic