Aim: reduce the complexity of LCA for better decision-making Returnable bottles are a promising strategy to reduce the environmental impacts of food packaging. However, the complexity of life cycle assessment (LCA), often leads companies to rely on generic results, or a limited set of parameters for decision-making. This can hinder the environmental optimization of systems that can vary dependently from the context. Method: simplified parametrized LCA models We developed a user-friendly tool for small and mid-sized stakeholders to include LCA when developing specific distribution strategies, combining the results' simplicity of use and scientific accuracy. It is based on simplified parametrized LCA models, consisting of simplifying the impact equations by (1) identifying the key input parameters, applying Sobol’s method on Global Sensitivity Analysis, and (2) setting the non-key parameters. To ensure broad applicability, a typology of configurations for returnable bottle strategies was designed. Recognizing the decision-making challenges faced by stakeholders, we also worked on the usability of the results presented in the tool to be used for decisions. Results: good balance between simplicity and robustness The simplified models developed demonstrate an excellent ability to balance simplicity with robustness. For instance, simplified models tested by stakeholders during FAIRCHAIN project (https://www.fairchain-h2020.eu/) reduce the required input data from 46 to 13, while explaining 90% of the total variability of the results for all environmental impacts. In addition, the tool quantifies the impacts of setting uncertain input parameters on the final results (e.g., in the case, it is difficult to collect). We experimented with the search for trade-offs with project partners. Various simplified models were generated and we provided scripts and protocols for users to generate more. Conclusion In environmental assessment, a key challenge lies in finding the right balance between users’ requirements for simplicity (“we need it to be simple”) and scientific rigor (“we want it to be robust”). Our proposed solution for developing returnable bottle strategies addresses this challenge: a user-friendly tool built upon robust statistical simplification methods. The tool was co-developed and tested with future users, incorporating practical constraints and ideas for future improvements to envision a widespread adoption beyond the project.