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Lookup NU author(s): Professor Graham Coates, Dr Mohammed AlharbiORCiD, Professor Nick Wright
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The resilience of Small and Medium-sized Enterprises (SMEs) to disruptive events is significant as this highly prevalent category of business forms the economic backbone in developed countries. This article provides an overview of the application of a computational modelling and simulation approach to evaluate SMEs’ operational resilience to flooding based on combinations of structural and procedural mitigation measures that may be implemented to improve their premises’ resistance to flooding and safeguard their business continuity. The approach integrates flood modelling and simulation with agent-based modelling and simulation (ABMS) within a modelled geographic environment. SMEs are modelled as agents based on findings of semi-structured interviews with SMEs that have experienced flooding or are at risk of flooding. In this paper, the ABMS has been applied to a new case study of the major flood event of 2007 in Tewkesbury. Further, to enable an evaluation of the operational resilience of manufacturing SMEs in terms of the relative effectiveness of flood mitigation measures, a new coefficient based on production loss is introduced. Results indicate structural mitigation measures are more effective than procedural measures. While this result is intuitive, the approach provides a means of evaluating the relative effectiveness of combinations of mitigation measures that SMEs may implement to enhance their operational resilience to flooding.
Author(s): Coates G, Alharbi M, Li C, Ahilan S, Wright N
Publication type: Article
Publication status: Published
Journal: Philosophical Transactions A
Year: 2020
Volume: 378
Issue: 2168
Print publication date: 03/04/2020
Online publication date: 17/02/2020
Acceptance date: 09/12/2019
Date deposited: 10/12/2019
ISSN (print): 1364-503X
ISSN (electronic): 1471-2962
Publisher: The Royal Society Publishing
URL: https://doi.org/10.1098/rsta.2019.0210
DOI: 10.1098/rsta.2019.0210
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