Risk and location analyses

The »Risk and Location Analyses« department develops data-based solutions for companies and authorities for greater civil security, more resilient supply chains and more economical and sustainable location decisions. The department's experts are characterised by years of market and industry expertise in supply chain management, logistics location analysis and security research.

The department focuses in particular on new methods and processes for application-oriented AI-supported remote sensing: for example, it uses and links geoinformation for better spatial analyses. It also incorporates economic framework data and methods of empirical social research into its analyses in order to be able to map and locate complex economic relationships in a data-centric manner.

Analysing and shaping civil security

The aim of civil security research is to make fire and disaster protection more efficient. For example, using artificial intelligence (AI) to analyse images during disaster relief operations, to assess river crossings or bridges and to check the navigability of roads. The department also analyses data from objects for fire protection requirements planning and creates automated accessibility maps (travel time isochrones) for these objects.

Research also focuses on the application of empirical social research methods in order to understand the behaviour of management groups (FüGK) or company crisis teams in the event of a disaster. Processes and training methods are analysed in order to improve the ability to react in emergency situations. The department's portfolio in the field of civil security research also includes the design of information services based on the Internet of Things (IoT), which serve to better manage disaster situations.

Supply Chain Risk Management

Companies need strategies to strengthen their own resilience and cope better with crises. These strategies should serve to manage potential risks along their supply chain (SC). The basis for this is a comprehensive assessment of everyday and extraordinary risks as well as continuous monitoring of the environment.

To this end, we assess supply chain risks in companies, use geodata and remote sensing to map global supply chains and develop data pools for ecosystem-wide SCM services. The department's in-depth expertise in linking internal and external data sources is particularly helpful here.

Market transparency through location analyses

We analyse location characteristics, structures and interrelationships within the manufacturing industry. Our aim is to increase market transparency by providing stakeholders at federal, state, regional or property level with up-to-date, neutral and multi-layered information. The analyses are based on geodata and statistics enriched with machine learning (ML), for example in the context of employment, VAT or transport.

For this task, we analyse spatial objects such as logistics properties, brownfield sites and railway sidings. This is done using an image recognition process that utilises automated pattern recognition in satellite images. The recognised objects are classified and located on a map.