Motivation

To secure their business success, companies today are more than ever in need of continuous transparency about changes in the micro-environment (the market in the narrower sense) and the macro-environment (socio-cultural, institutional and technological environment) and make possible these changes to be addressed. The dynamic nature of this environment, the rising complexity, and growing insecurity make it increasingly necessary for companies to regularly reflect on their business models and to recognise how the processes of customers, partners, and competitors are changing. Under these conditions, it is becoming increasingly important for companies and research institutions to recognise issues of strategic importance, that have potential for success, and above all, that are future-oriented at an early stage and to derive decisions, e.g., for setting priorities or making investment decisions. In the age of the internet, access to basic information is easier than ever before, but the volume of information also increases the effort required from companies and market researchers to recognise market changes and trends at an early stage.

Objective

The aim of our research is to combine dynamic knowledge modelling tools with AI-led data analysis methods in a holistic process that automatically creates an empirical basis for expert opinions and the determination and analysis of trends and scenarios from a variety of unstructured market and industry data. Under the umbrella of “Future Engineering”, we are accomplishing this objective through research and development and the results are brought into application through collaborative approaches with partners from industry and research.

Our Approach

To develop data-based processes for strategic prediction (“data empowered strategic foresight”) in industrial and consumer-driven markets, the Ohm and Fraunhofer IIS are focusing their joint research and development on merging the following, complementary areas of competence:

• automated, AI-based analysis and processing of unstructured text data,
• development of domain specific ontologies and dynamic modelling of knowledge in knowledge graphs, and
• transfer into established methods and tools used in innovation management and market research.

Contact the FE Group

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