Exploring the Conceptual Foundation of Continuity Management in the Context of Societal Safety.

Public and private actors with critical roles for ensuring societal safety need to work proactively to reduce risks and vulnerabilities. Traditionally, risk management activities have often been performed in order to ensure continuous functioning of key societal services. Recently, however, business continuity management (BCM), and its analytical subcomponent business impact assessment (BIA), has been introduced and used more extensively by both the private and public sector in order to increase the robustness and resilience of critical infrastructures and societal functions and services. BCM was originally developed in the business sector but has received a broader use during the last decade. Yet, BCM/BIA has gained limited attention in the scientific literature-especially when it comes to clarifying and developing its conceptual basis. First, this article examines and discusses the conceptual foundation of BCM concepts, including practical challenges of applying the concepts. Based on recent conceptual developments from the field of risk management, a developed conceptualization is suggested. Second, the article discusses challenges that arise when applying BCM in the societal safety area and provides some recommendations aiming to improve the clarity and quality of applications. Third, the article provides suggestions of how to integrate the overlapping approaches of BIA and risk assessment in order to improve efficiency and effectiveness of proactive, analytic processes. We hope that the article can stimulate a critical discussion about the key concepts of BCM, their wider use in societal safety, and their connection to other concepts and activities such as risk assessment.

Integration of Critical Infrastructure and Societal Consequence Models: Impact on Swedish Power System Mitigation Decisions.

Critical infrastructures provide society with services essential to its functioning, and extensive disruptions give rise to large societal consequences. Risk and vulnerability analyses of critical infrastructures generally focus narrowly on the infrastructure of interest and describe the consequences as nonsupplied commodities or the cost of unsupplied commodities; they rarely holistically consider the larger impact with respect to higher-order consequences for the society. From a societal perspective, this narrow focus may lead to severe underestimation of the negative effects of infrastructure disruptions. To explore this theory, an integrated modeling approach, combining models of critical infrastructures and economic input-output models, is proposed and applied in a case study. In the case study, a representative model of the Swedish power transmission system and a regionalized economic input-output model are utilized. This enables exploration of how a narrow infrastructure or a more holistic societal consequence perspective affects vulnerability-related mitigation decisions regarding critical infrastructures. Two decision contexts related to prioritization of different vulnerability-reducing measures are considered-identifying critical components and adding system components to increase robustness. It is concluded that higher-order societal consequences due to power supply disruptions can be up to twice as large as first-order consequences, which in turn has a significant effect on the identification of which critical components are to be protected or strengthened and a smaller effect on the ranking of improvement measures in terms of adding system components to increase system redundancy.

Topological performance measures as surrogates for physical flow models for risk and vulnerability analysis for electric power systems.

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically-oriented models to advanced physical-flow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.