Would you like to analyse a parameter for which there is little data?
Are you planning to produce your products sustainably?
Do you want to make an informed decision despite uncertainty?
Nature and our economy have one thing in common: they are complex, dynamic systems in which undesirable and unforeseen side effects can sometimes occur after a decision has been made. With the help of our system modelling, we show you different variants with all their consequences in a model. We visualise interrelationships and show how a single factor can change the entire system.
Better results through fuzzy logic
The imprecise is difficult to understand. For example, there may be a lot of expert knowledge about a topic, but no quantitative data is available. The fuzzy logic method helps to describe qualitative knowledge with mathematical precision and to offset it against measurement data. In this way, even imprecise data can be precisely recorded.
Experience from various projects on air quality or biodiversity has shown this: Fuzzy logic helps to achieve better results with less effort.
The material flow analysis
Material flow analysis analyses specific material and energy flows. This enables us to record and visualise technical, economic or ecological systems. This is an important competence for determining the climate-relevant emissions of a region and utilising resources efficiently, for example. The material flow analysis shows which substances enter a cycle and where, and how long they remain in it. This enables us to provide a basis for planning and show you how a system can be improved.
System and uncertainty analyses
The Monte Carlo method makes it possible to use assumptions and estimates in system modelling to calculate how variable the results of a decision can be. This allows the risk of a project or system to be quantified. The Monte Carlo method is also suitable for sensitivity analyses and the calculation of confidence limits. It is also used, for example, in life cycle assessments to determine the significance of the results.
If required, we work with our client to model the system under consideration with all relevant assumptions and the associated uncertainties. We use this to calculate the variability of the result, or we limit ourselves to calculating the variability of the results of a model created by the client.