Natural disasters can result in considerable economic costs, which can have important consequences for the affected regions and their recovery. More important than the direct damages (e.g., destruction of buildings and physical infrastructures) are often the indirect damages that occur due to business and supply chain interruptions. As the interdependencies within and across modern supply networks are, however, complex and information about them is typically limited, an assessment of indirect disaster impact is difficult.

The project aims at developing a method that enables the near-real time assessment of economic impact (both direct and indirect) for all phases of a disaster. The developed method must therefore help decision makers to come up with a good decision that is founded on a valid basis quickly although the available information may be incomplete, uncertain, lacking or conflicting. Additionally, the method should be designed to guide support the search for further information by focusing on the most relevant or vulnerable aspects. At the same time, the model hast to be easily adaptable. In this manner, newly available information can be integrated rapidly. The analysis can then be enriched successively providing near real time (as the situation unfolds) more and more detailed levels of analysis and more precise results. Finally, it must be possible to quickly adapt the model to different information systems and infrastructures to ensure that first results can be determined shortly after any potential disaster, wherever it might happen.

Given these requirements, we chose an indicator-based method to assess the sector specific vulnerability against business interruptions. In order to rapidly adapt the model depending on data availability, we use key indicators. These key indicators are calculated on the basis of national input-output tables, which are available in any country, so that a first rough estimation of indirect costs is available near real time. Whenever further information or data is available, the model can be adapted to integrate further indicators to enable more detailed or precise assessment by considering further aspects such as the production strategies or the dependency on critical. At the same time, methods will be developed to communicate the uncertainties present and to explain the possible consequences of the lack of information in a transparent manner.

Linkages to other projects

Case-Based-Reasoning will be used to identify the most relevant indicators, to determine cause -effect chains and interdependencies between indicators as well as to calibrate the model.