Today more than ever, companies rely on dynamic industry structure analyses that capture and address changes in the microenvironment (customers, partners and competitors) and the macroenvironment (socio-cultural, institutional and technological environment) in order to ensure sustainable business success. Accordingly, it is becoming increasingly important for companies to continuously reflect on their business models and to understand how value creation processes of customers, partners and competitors are evolving. Ultimately, addressing these changes in a precise and timely manner is crucial for business success.

Modern methods of market and trend research as well as methods of futurology already make it possible to assess future developments in market and technology environments. Still, most state-of-the-art procedures are primarily focused on descriptive and momentary analyses and often lack transparency regarding the applied methods. Furthermore, current procedures often use superficial data bases, as there is high effort involved in establishing empirically well-grounded foundations. However, the fact that digital value creation chains and new innovative applications are often strongly interlinked necessitates the connection of technological expertise (e.g. in the field of digital production) with an in-depth understanding of application logic (e.g. in the Smart Cities segment) and the logic of individual business models and business ecosystems that determine companies’ value creation.

This complexity can be resolved through the dynamic development and analysis of semantic web platforms (knowledge graphs). However, due to the high initial effort involved, knowledge graphs are rarely used, or their application is limited to descriptive or visualising processes for the compression of generally publicly available knowledge.

The Technical University of Nuremberg (with its Nuremberg Campus of Technology NCT) and the Fraunhofer Institute for Integrated Circuits IIS (with its Center for Applied Research on Supply Chain Services SCS) have founded the research group Future Engineering to mutually support and strengthen their activities in the field of data-based trend and scenario research. The objective is to combine tools of dynamic knowledge modelling with AI-guided methods of data analysis in a process that automatically builds an empirical basis for expertise, trend determination and scenario analysis from a variety of unstructured market and industry data.

For this purpose, TH Nuremberg and the Fraunhofer IIS are concentrating their joint research and development on bringing together the following complementary areas of expertise:

  • Technologies and methods of data science and text analysis to automatically extract, select and analyse available knowledge (e.g. from data bases and RSS-feeds) in order to provide a broad empirical data base and a high level of automation for trend and scenario analyses.
  • Dynamically growing semantic data structures (knowledge graphs) that connect explicit and implicit knowledge from selected technology domains (e.g. “Internet of Things IoT” or “Digital Production”), their areas of application and specific market environment structures of companies (“ecosystems”).
  • Business methods from the areas of strategic foresight, strategic business development, as well as technology and innovation management, with the aim of combining them with new possibilities of quantitative data science methods.

Through constant expansion and further development of the database and scientific methodology, the research group creates continuous and sustainable benefit, especially for the European Metropolitan Region of Nuremberg, which primarily consists of small- and medium-sized enterprises.