Since regulations on insulation building materials become more stringent, particular interest is growing in thermal low conductive composite building materials. By adding very expensive, low thermal conductive nanoparticles in widely used inorganic solid materials, a new high performance building material can be developed. To sufficiently balance the production cost and the use of the nanofillers, an automatic tool, PreCon, was developed to provide sufficient process information and to generate a general purpose numerical model which can be used for thermal insulation simulations. It is expected that a total random distribution of the particles in the inorganic matrix will not result in a significant increase in insulation properties and hence will not enhance the performance. PreCon is used to investigate the influence of a random distributed nanomaterial combined with a more normal distribution through thickness. Based on this information, one can gauge the efforts needed to localize the nanoparticles in particular regions. However, accurate and repetitive thermal conductivity measurements of real dispersions are very difficult and precarious to obtain. Nevertheless, combining PreCon with Siemens simulation software provides appropriate nanofiller distributions with respect to cost. The realized tool is also able to introduce fibrous material distributions as a composite reinforcement. POLYM. ENG. SCI., 58:568-585, 2018. (c) 2018 Society of Plastics Engineers