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One of the problems of a LCA is the complexity of the considered systems. Results depending strongly on the boundary conditions. More appropriate is to parameterise the LCA and enable it for variations. With that, the Life Cycle Modeling and Simulation leads to a deeper understanding of the examined system. Design parameters, like the geometry or the material of the part can be varied as well as the mass and energy flow in the process chain or methodological parameters. This is especially necessary in the early stage of the design process as a tool for sensitivity analysis and optimisation of products. A dominance analysis ensures that the complexity of the model is suitable for goal and scope of the study.

LCA studies aim at an integrating system assessment as a comprehensive and holistic approach to prevent tradeoffs and guide users and decision makers for better informed decisions. The total life cycle approach aims at informing and supporting decision making and management support. LCA, like other management techniques as well, has inherent limitations, making choices, assumptions etc. inevitable. Before using the findings of life cycle studies, a consideration of those uncertainties, the effects of value choices and assumptions, as well as the inherent data inaccuracies must be examined in more detail. Traditional error and uncertainty analysis failed in practical use due to the specific system modeling, the data availability and the respective data collection procedures in life cycle studies. New approaches to identify and understand the system specific uncertainties are necessary for this purpose.