The first objective of this project was to achieve material characterisation with already existing standardised methods. Furthermore, using the angle of repose test, the bulk material was calibrated by DEM.

Simulation Output Analytics

In general, the calibration is carried out by the matching of real and simulated experiments in the geometrical scale of 1:1, where particles have to be upscaled. Once the calibration is done, the simulation parameters are properly identified. The values that are closest to the experiment are chosen for usage in the main simulation. With these identified parameters, the main simulation, including the material-mixing machine, can be executed.
The secondary, more complex objective was to carry out deep dive into the data output of the simulation.

In the beginning, Big Data output must be read and structured into the clear data model, so that future data discovery could bring its values. The goal was to apply a scientific data analysis approach to analyse the mixing efficiency of mixing machine. Hereby, data analysis allowed us to post-process particles on the quantitative level by creating performance indicators for mixing efficiency using visual analytics.


One of our most exciting and memorable projects made for one of the biggest companies in automobile industry was to channel customer data into meaningful and interpretable information in a form of dashboards.

Customer data kpis

Gigabytes of data were collected on daily basis, which had to be monitored in real life sequence. Using our skills and knowledge, we were able to build a unique application based on Qlik technologies. Thus, real time monitoring of your business is essential for growth of customer-centric company nowadays.

Our solutions in this field will provide you with more insights to increase significantly your customer satisfaction.