Towards training network resilience to maintain disaster recovery expertise



Resilience, Education System, Evaluation, Key Performance Indicator, Decision Support, Steering


Effective disaster recovery depends on highly-skilled practitioners. Like any sector, however, the education and training systems that build these skills can experience crises that disrupt their ability to function effectively. These
can be one-off events (e.g. a major flood) or extend over long periods (e.g. a pandemic or a vocational crisis). Based on the observation that there is a shortage of trainers and teachers in the education sector, as shown by indicator D7 (OECD, 2023), in this article we study the ongoing ability of a French vocational training organization to transmit skills that are useful to society in the event of a natural disaster, and in particular those that will shorten the recovery time of buildings and infrastructures. We propose a model that simulates the flow of people through the educational system and the institution’s ability to generate enough trainers to maintain a chain of know-how transmission. In the simulation of this work in progress, we have parameterized the actual success rates, which we vary in order to
define a minimum success rate limit to ensure the renewal of human capital. Based on the proposed model, further testing and analysis will be used to characterize a range of resilient flow behaviors and to provide recommendations for maintaining the condition of the human know-how transmission chain.


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How to Cite

Savignac, P., Zobel, C., Petitdemange, E., Cellier, N., Weiss, R., & Lauras, M. (2024). Towards training network resilience to maintain disaster recovery expertise. ISCRAM Proceedings, 21.

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