Decision support

Decisions are usually based on multitude of information, which can rarely be perceived and interpreted simultaneously by decision makers. We develop interactive software systems that support decision makers by using AI-based methods to analyze, process and visualize this information in a task-oriented way. This enables decision makers to act efficiently while taking all relevant aspects into account.

Decision support in the application

For an efficient and sustainable energy supply, energy companies must take decisions regarding the use of resources in order to optimally balance energy production and consumption. AI-based energy management solutions (e.g. for energy forecasting or energy use optimization) make a decisive contribution to the decision-making process (EMS-EDM PROPHET® - Energy and Energy Data Management by Fraunhofer).

In the maritime domain, decision makers have to monitor shipping traffic, for example. Situation analysis methods help to identify all critical situations in the great flood of information (e.g. EU projects MARISA and OCEAN2020).

In the medical field, our AI-based methods provide healthcare experts with information that can support diagnosis, therapy, screening and follow-up decisions. One example is the OnkoLeit system, which is based on clinical practice guidelines as well as medical expert knowledge to support oncologists in making optimal clinical decisions.

In the Corona Crisis, digital learning is the focus of the population. In order to support teachers in compiling suitable teaching content and to provide learners with individually adapted learning opportunities, we are developing AI-supported learning analytics and recommender systems (ELAI and CLM).

The Bauhaus.MobilityLab Erfurt is creating a Smart City living lab in the heart of Germany under the leadership of Fraunhofer IOSB-AST. Here, we will develop, test and tangibly show how digitalized mobility, logistics and energy supply can interact in tomorrow's cities.