Disaster management system as an element of risk management for natural disaster systems using the PESTLE framework

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Recently, we have witnessed so many natural catastrophes such as earthquakes in Japan, severe floods in the UK, US and many other parts of the world. Consequently businesses have been losing tens of billions of dollars as a result of various natural and man-made disasters. Disaster Management System (DMS) have proven to be important means for reducing risks associated with such damages to businesses. A DMS can minimize and in some cases, eliminates the risks through technical, management or operational solutions (risk management effort). However, it is virtually impossible to eliminate all risks. Information technology systems are vulnerable for a variety of disruptions (e.g. short-term power outage, disk drive failure) as a result of natural disasters to terrorist actions. In many cases, critical resources may reside outside the organizations control (such as telecommunications or electric power), and the organization may be unable to ensure their availability. This paper proposes a model for Disaster Management System as an Element of Risk Management using the PESTLE framework. Thus, an effective Disaster Management System in the form of contingency planning, execution and testing are essential to mitigate the risk of system and service availability. We have developed a global model for disaster recover planning and management based on the PESTLE framework which can be customised and applied to a variety of disasters prone systems such natural, emergency, IT/Network/Security, Data recovery, and incident-response systems. To summarise, this paper aims to maximise the benefits of PESTLE analysis it should be used on a regular basis within an organisation to enable the identification of trends.




Sarwar, D., Ramachandran, M., & Hosseinian-Far, A. (2016). Disaster management system as an element of risk management for natural disaster systems using the PESTLE framework. In Communications in Computer and Information Science (Vol. 630, pp. 191–204). Springer Verlag. https://doi.org/10.1007/978-3-319-51064-4_16

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