Databased Planning of Production Processes

The new design and improvement of processes in chemical engineering nowadays is based most often on process simulations. Nearly always objectives for quality and costs have to be considered.

Finding not only good, but the best compromises between these competing objectives is difficult, but essential for the decision process. Currently, in many practical planning processes, good compromises are found empirically, without the guarantee of optimality, and without any relation to other good, or even best compromises.

We develop methods to efficiently compute a whole set of best compromises, based on up to date simulations. Furthermore, we provide tools for decision support, which visualize the set of best compromises interactively for the engineer. In this manner, single solutions are shown in the context of the whole decision space, such that they can be judged objectively and according to the specific needs of the user.

Example Projects


Capacity Planning for Complex Value Streams

We developed a tool for proactive capacity planning that addresses challenges through practical modeling. 


Optimizing Sales Promotions

The sales forecast for a trade promotion is one of many project examples in which smart data has proven itself superior to the pure, data driven, Big Data models.