Division Supply Chain Services at Fraunhofer IIS

Supply Chain Risk Management

Optinale TRansportmittel-Wahl mit Fracht-Konsolidierung — © PaulShlykov / AdobeStock.com

In order to strengthen their own resilience and cope better with crises, companies need strategies 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 macroeconomic and industry-specific environment.

To this end, we assess supply chain risks in companies, use geodata and satellite images 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 relevant here.

Our expertise

Our competences	Your challenges
Analysis of freight flow interdependencies Change detection on satellite images Assessment of SC risks in companies Localisation of SC risks in geodata Development of ecosystem-wide data pools for SCM services that connect internal and external data sources	Supply chain optimisation and risk management Implementation of documentation obligations in the areas of sustainability, safety and due diligence Impact analysis of events in the supply chain and derivation of optimisation proposals

Referenzen

Organisation

»Risk and Location Analyses« Department

The department develops data-based solutions for greater civil security, more resilient supply chains and sustainable location decisions. With a strong understanding of the market and industry, research focuses on data-centred spatial and risk research. The experts rely on AI-supported remote sensing, geoinformation and empirical social research to analyse and locate complex economic relationships.

Abteilung Risiko- und Standortanalysen

Project

INSIGHT – Knowledge Graphs und Large Language Models in der Industrie

The Fraunhofer INSIGHT project uses knowledge graphs and large language models to efficiently analyse unstructured and structured data in industry. The aim is to economically establish semantic AI technologies for the automation of processes such as sustainability reporting and logistics location planning. One example is holistic logistics location planning, in which external factors are evaluated in order to make well-founded decisions.

About the project